PAM Survey Final for Web

PAM:
Measuring Patient Activation in
South Eastern Sydney
Health
South Eastern Sydney
Local Health District
APRIL 2015
SOUTH EASTERN SYDNEY
www.sesml.org.au

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ACKNOWLEDGEMENTS
This report was prepared by Maria Pifarre, Community Engagement and Health Promotion
Coordinator (PAM Project Lead) and Karen Frost, Planning & Evaluation Coordinator (PAM Project
Evaluator).
The South Eastern Sydney Medicare Local wishes to acknowledge and thank the support of its key
stakeholders without whom this project would not have been possible. Particular thanks are given to
the following people for their contribution to the report: Lauren Dalton, Vanessa Banda, Amanda
Hese and Erin Lilley.
The Agency for Clinical Innovation provided the financial support and the South Eastern Local Health
District is the Patient Activation Measure™ licence holder and the key partner in this project.
The PAM Project Steering Committee and Working Parties also included: Kurranulla Aboriginal
Corporation; the Local Government Agencies of Hurstville, Kogarah, Rockdale and the Sutherland
Shire; the Eastern Sydney Medicare Local and the Consumer Representative. (See Appendix 1.)
South Eastern Sydney Medicare Local Limited (ABN 68157719296)
Level 3, 15 Kensington Street PO Box 57 t 02 9330 9900
Kogarah Kogarah d 02 9330 9967
NSW 2217 NSW 1485 f 02 9330 9988

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Contents
ACKNOWLEDGEMENTS...........................................................................................................................1
EXECUTIVE SUMMARY............................................................................................................................5
PROJECT SUMMARY................................................................................................................................7
1. INTRODUCTION...............................................................................................................................8
1.1. BACKGROUND.........................................................................................................................8
1.2. RATIONALE..............................................................................................................................9
1.3. STUDY OBJECTIVES..................................................................................................................9
1.3.1. TIME LINE........................................................................................................................9
1.3.2. ETHICS ...........................................................................................................................10
2. METHODOLOGY ............................................................................................................................10
2.1. STUDY OUTLINE.....................................................................................................................10
2.1.1. STUDY FLOW CHART .....................................................................................................10
2.2. DESIGN..................................................................................................................................10
2.3. STATISTICAL ANALYSIS PLAN.................................................................................................11
2.4. PARTICIPANT SELECTION ......................................................................................................11
2.4.1. SAMPLE SIZE OR POWER CALCULATION.......................................................................11
3. RESULTS.........................................................................................................................................13
3.1. OBJECTIVE 1 QUESTIONS: .....................................................................................................13
3.1.1. EASE OF IMPLEMENTATION..........................................................................................13
3.1.2. ASSESSMENT OF DATA QUALITY...................................................................................15
3.2.1. WHOLE OF POPULATION BASELINE FOR HEALTH ACTIVATION LEVELS........................18
3.2.2. AREAS FOR INTERVENTION...........................................................................................18
3.2.3. KEY POINTS OF DEMOGRAPHIC DATA ..........................................................................19
3.3.1. REPORT ON FINDINGS...................................................................................................34
3.3.2. RECOMMENDATIONS ON THE USE OF PAM13TM
.........................................................35
4. KEY FINDINGS................................................................................................................................36
5. LIMITATIONS .................................................................................................................................38
5.1. Establishment........................................................................................................................38
5.2. Implementation ....................................................................................................................38
5.3. Delivery .................................................................................................................................38
6. DISCUSSION...................................................................................................................................40
7. RECOMMENDATIONS....................................................................................................................43
8. CONCLUSION.................................................................................................................................44

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REFERENCES..........................................................................................................................................45
APPENDICES..........................................................................................................................................47
1. PROJECT TEAM AND STEERING COMMITTEE............................................................................47
2. SURVEY......................................................................................................................................48

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Tables
Table 1: Activation Levels.....................................................................................................................11
Table 2: Participant selection...............................................................................................................12
Table 3: Mean and median per PAM question and the Activation Level. ...........................................16
Table 4: Breakdown of missing and actual responses per question....................................................16
Table 5: Summary of response rates for demographic questions.......................................................17
Table 6: Activation Levels.....................................................................................................................18
Table 7: Chi-Square Test result for speaking Language Other Than English at home .........................23
Table 8: Chi-Square Test result for those with a regular GP/Family Doctor........................................24
Table 9: Chi-Square Test result for Private Health Insurance..............................................................25
Table 10: Chi-Square Test result for used a health service in the past twelve months.......................26
Table 11: Chi-Square Test result for how often use the internet to access health information .........28
Table 12: Chi-Square Test result for self-rating of health....................................................................30
Table 13: Correlation assessments on continuous variables...............................................................34
Table 14: Chi-Square test results on categorical variables ..................................................................34
Table 15: Project Team and Steering Committee................................................................................47
Graphs
Graph 1: Number of Hard Copy Surveys by Collection Areas..............................................................14
Graph 2: Eligible vs Ineligible Responses .............................................................................................14
Graph 3: Activation Levels by Year of Birth (1922-1997).....................................................................19
Graph 4: Activation Level by Gender ...................................................................................................19
Graph 5: Postcode where respondents live by Activation Level .........................................................20
Graph 6: Postcode where respondents work by Activation Level.......................................................20
Graph 7: Aboriginality by Activation Level...........................................................................................21
Graph 8: English as First language by Activation Level........................................................................22
Graph 9: Languages other than English Spoken at home....................................................................22
Graph 10: GP/Family Doctor by Activation Level.................................................................................23
Graph 11: Private Health Insurance by Activation Level......................................................................24
Graph 12: Where respondents get most of their care by Activation Levels........................................25
Graph 13: Frequency of healthcare use in past 12 months.................................................................26
Graph 14: Use of interpreter for health services in past 12 months by Activation Level....................27
Graph 15: Use of internet to access health information by Activation Level......................................28
Graph 16: Health Condition by Activation Level..................................................................................29
Graph 17: Self rating of health by activation level...............................................................................29
Graph 18: Use of Mental Health Services by Activation Level.............................................................30
Graph 19: Education by Activation Level.............................................................................................31
Graph 20: Employment status by activation Level ..............................................................................32
Graph 21 How respondents found out about PAM.............................................................................33
Figures
Figure 1: Project process......................................................................................................................10
Figure 2: Data Safety...........................................................................................................................12

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EXECUTIVE SUMMARY
South Eastern Sydney Medicare Local (SESML) was established on 1 July2012, as a primary health
care organisation to drive improvements in primary health care delivery, ensure that services are
tailored to meet the needs of the local community and address service gaps. The area covers four
Local Government Areas (LGAs) and a total population of 442,864 (24)
with a high level of cultural and
linguistic diversity across the region.
The SESML strategic intent is to drive better health outcomes in our community by planning,
coordinating and helping to integrate services, bringing all parts of the primary health system
together so that patients receive the best quality outcomes and improved experience through
efficient services. As part of this journey SESML undertook a collaborative project with relevant
stakeholders to obtain a baseline measure of patient engagement across the population who live
and work in the SESML region. This baseline of consumer health activation levels will help to inform
the implementation of relevant and targeted approaches to local health needs.
People with good health literacy and activation (engagement) in their health care have better health
outcomes. Research shows these people are significantly more likely to exercise regularly, eat a
healthy diet and not smoke. Additionally, they report significantly better health, significantly lower
rates of GP visits, Emergency Department (ED) presentations and hospital admissions. Thus, health
literacy and engagement has a direct relationship with wellbeing and health care costs. As health
care costs are projected to increase significantly and unsustainably in the coming decades, improving
health literacy, engagement and satisfaction of health care amongst Australian individuals and
communities is one of a suite of current national initiatives to reduce morbidity and mortality,
increase productivity and reduce or offset health care expenditure.
The Patient Activation MeasureTM
(PAMTM
) measures the knowledge, skills and confidence required
to manage one’s own health and healthcare. The PAMTM
scores consumers into one of four
activation levels each of which indicates a range of self-care behaviours that drive health activation.
The level of activation can also predict healthcare outcomes including medication adherence, use of
hospital and health services. (23)
This project aimed to evaluate the efficiency and effectiveness of the Patient Activation Measure
(PAM13™) tool in an Australian setting, and determine the level of patient engagement in their
health care, using the UK based House of Care model as the framework.
The project achieved a representative survey sample of 1522 completed surveys of which 1490 met
the criteria for analysis.
The outcomes identified in this report inform and strengthen health engagement in the South
Eastern Sydney community. The following is a summary of key observations regarding the
population health engagement status in the SESML region:
1. The PAM13TM
tool was an easy to implement survey, with 97.9% (n=1490) of returned
surveys eligible for analysis. Of the respondents who answered additional feedback
questions, 97.6% (n=459) found the survey simple and easy to understand and completed
within 10 minutes
2. The population baseline was established with an average population Activation Level of 3 for
the South Eastern Sydney area. This suggested a high level of activation indicating our
population believed it had an important role to play in managing their own health care.
Areas for further intervention included:

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a. Further in-depth analysis of the data to maximise our understanding of our
population.
b. Validation of the tool in a variety of local priority languages.
c. Development and implementation a Health Literacy Strategy for SESML including
identification of a health literacy measure to complement this engagement tool.
3. This report concluded that the PAM13™ was a useful tool for measuring patient engagement
in an Australian health care setting.
Overall the report concluded that the South Eastern Sydney population believed they had a role to
play in managing their own health care, both alone and in collaboration with health professionals.
Supporting our population to increase their engagement and health literacy in the Australian health
care setting remains key to improving efficiencies and reducing health care costs and improving
outcomes.

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PROJECT SUMMARY
Study title Measuring Patient Activation in South Eastern Sydney
Study Duration 1 July 2014 to 30 April 2015
Objectives Primary objective:
This project aimed to evaluate the efficiency and effectiveness of the
PAM13™ tool in an Australian setting, and determine the level of patient
engagement in their health care, using the UK based House of Care model
as the framework.
The objectives were to:
a) Undertake a pilot study with health consumers in South Eastern
Sydney to measure the ease of effectiveness and efficiency of
the PAM13TM
and the House of Care framework.
b) Establish a baseline for health literacy levels in the South
Eastern Sydney region and identify areas for intervention; and
c) Make recommendations on the evaluation tool for measuring
patient engagement in an Australian health care setting.
Project Partners The key collaborative partners included: the South Eastern Sydney Local
Health District, the Kurranulla Aboriginal Corporation, the LGA's of
Hurstville, Kogarah, Rockdale and the Sutherland Shire, the Eastern Sydney
Medicare Local and a health consumer representative.
Study design The project undertook an anonymous, convenience sample survey using the
PAM13TM
tool with additional demographic questions distributed to the
residential and working population of the SESML region.
Sample size: 1064 required but target set at 1500 surveys.
1522 completed surveys received, 1490 met eligibility criteria for analysis.
Selection criteria: Convenience sampling of people living and working in SESML region, self-
selection and participation.
Study procedure: Distributed via networks, newspapers, email to SESML region for residents,
consumers and workforce population participation. Reply paid code for
return via mail. SurveyMonkey for electronic submission.
Key Results  A statistically representative sample was achieved.
 The mean population Activation Level was 3
 Significant differences in activation levels were identified for those who:
o speak a Language Other Than English (LOTE) at home
o have a regular GP/Family Doctor
o private health insurance
o frequently use health care services
o access information from the internet
o have a high self-rating on health status
o have used a mental health professional in the last 12 months
 97.6 % of respondents who provided additional feedback reported the
PAM13TM
tool was simple and easy to understand and complete.
 Email and Survey Box recruiting methodologies proved the most
effective in reaching the population.

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1. INTRODUCTION
1.1. BACKGROUND
The SESML was established on 1 July2012, as a primary health care organisation established to drive
improvements in primary health care delivery, ensure that services are tailored to meet the needs of
the local community and address service gaps. SESML started from a strong base with the work of
the St George Division of General Practice (SGDGP) and Sutherland Division of General Practice
(SDGP) which, over a period of 19 years built a broad range of strong and successful primary health
care initiatives with General Practitioners (GPs), Allied Health Professionals (AHPs), hospitals,
community health services, aged care facilities, community based organisations and local
governments. The area composed of four Local Government Areas (LGAs) and a total population of
442,864 (23)
with a high level of cultural and language diversity across the region.
The SESML strategic intent is to drive better health outcomes in our community by planning,
coordinating and helping to integrate services, bringing all parts of the primary health system
together so that patients received best quality and efficient services. This will create better
connected primary health care services that respond to the local needs of the community. As part of
this journey SESML undertook a project to measure patient activation across the SESML population
to develop a picture of consumer health activation (engagement) levels in order to implement a
relevant and targeted approach to local health needs. The key collaborative partners in this project
included: the South Eastern Sydney Local Health District, the Kurranulla Aboriginal Corporation, the
LGA's of Hurstville, Kogarah, Rockdale and the Sutherland Shire, the Eastern Sydney Medicare Local
and a health consumer representative.
As the Australian Healthcare system continues to transition towards more patient-centred and
integrated models of health care with increased focus on primary health care, there is an emerging
need to have a range of metrics that can be used to measure patients’ engagement in their health
care.
The House of Care concept developed in the United Kingdom (UK) describes a coordinated service
delivery model, and is used to illustrate a whole of system approach to care. A key component is that
it assumes an active role for patients, with collaborative personalised care planning at its heart. It is
therefore important to understand patients’ willingness to have an active role in their health care,
and barriers that might be addressed. (4)
SESML undertook a project to evaluate the PAM13TM
for measuring consumers’ ability and
willingness to take on the role of managing their health and health care in an Australian setting,
drawing on the House of Care model as a framework.
People with good health literacy and activation (engagement) in their health care have better health
outcomes. Research shows these people are significantly more likely to exercise regularly, eat a
healthy diet and not smoke. Additionally, they report significantly better health, significantly lower
rates of GP visits, ED presentations and hospital admissions. Thus, health literacy and engagement
has a direct relationship with wellbeing and health care costs. As health care costs are projected to
increase significantly and unsustainably in the coming decades, improving health literacy,
engagement and satisfaction of health care amongst Australian individuals and communities is one
of a suite of current national initiatives to reduce morbidity and mortality, increase productivity and
reduce or offset health care expenditure. Measuring how literate and engaged patients are in their
health care is a key first step in determining where and how changes need to be made to increase
levels of health literacy and engagement. (1, 2, 5)

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Overall, it is vital that patients feel engaged and empowered to manage their health, are proactive
about their health, and are more aware about primary health care services. Importantly, services
need to be developed to cater for the cultural requirements and preferences of different groups.
1.2. RATIONALE
PAM™ is a tool for measuring the level of patient engagement in their healthcare. It was designed to
assess an individual’s knowledge, skill and confidence for self-management (6)
. Hibbard and
colleagues developed PAM13TM
in the United States in 2004 (13)
as a 22 item scale. Hibbard and
colleagues then developed it into a short form 13 item scale in 2005 (14)
, known as PAM13TM
.
Previous validation studies have shown PAM22TM
and PAM13TM
to be valid and reliable measures of
activation (4)
. Patient activation is one component of a patient’s overall health literacy level.
PAM13TM
has been applied in different settings across a number of different countries, including
Germany, UK, Denmark and Australia (3, 7, 8, 20).
The tool has been translated into 15 languages and
validated in 6. The difficulty structure was maintained across language and culture (14)
The project aimed to determine if the PAM13TM
tool would be as easy and efficient to implement in
an Australian context, and if it could be meaningfully used in a broader context to assess a whole of
population level of health care engagement. The tool has only been used in clinical setting with
individuals.
1.3. STUDY OBJECTIVES
The objectives of the project were to:
1) Undertake a pilot study with health consumers in South Eastern Sydney to measure the
effectiveness and efficiency of the PAM13TM
and the House of Care framework.
a) Determine how easy it is to implement by:
i) Quantifying the level of uptake across SESML.
ii) Establishing how many surveys have been completed.
b) Data quality to be assessed by recording the mean, median and percentage of missing data
and the percentage of ‘non applicable’ answers.
2) Establish a baseline for health literacy levels in the South Eastern Sydney region and identify
areas for intervention;
a) Establish a whole of population baseline for health engagement levels in the South Eastern
Sydney region
b) Identify areas for intervention at a SESML region population level.
3) Make recommendations on the evaluation tool for measuring patient engagement in an
Australian health care setting.
a) Prepare a report on findings including any correlations between PAM13TM
scores and
demographic, health and engagement characteristics.
b) Make recommendations on the use of PAM13TM
as a tool for measuring patient engagement
before and after an intervention, among the SESML population.
1.3.1. TIME LINE
The project time line was 1 July 2014 to 31 March 2015. The surveys were distributed from 21
November 2014 to 27 February 2015. Due to the 3 month delay in obtaining ethics approval the
project time line was extended for 1 month to 30 April 2015 to enable time to conduct analysis of
the results.

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1.3.2. ETHICS
Ethics applications were submitted to the Human Research Ethics Committee (HREC) and the
Aboriginal Health and Medical Research Council (AH&MRC). Approvals were received from both
committees on 21 November 2014 and 9 April 2015 respectively.
2. METHODOLOGY
2.1. STUDY OUTLINE
2.1.1. STUDY FLOW CHART
Figure 1: Project process
2.2. DESIGN
The PAM13TM
tool is a unidimensional, interval level, Guttmann-style survey developed through
Rasch analysis and classical test theory psychometric methods. It measures an overarching construct
- being in charge of one's own health. The PAM13TM
tool is not designed to assess behaviours in
isolation, but instead recognises that people who feel 'in charge' of their health engage in a range of
behaviours. Extensive research in many diverse populations confirms the PAM13TM
tool's strong
measurement properties. (4)
The PAM13TM
contains a series of 13 statements designed to assess the level of a patient’s
activation. These statements are about beliefs, confidence in the management of health related
tasks and self-assessed knowledge. As outlined in the Kings Fund (4)
introduction to Patient
Activation, patients are asked to rate the degree to which they agree or disagree with each
statement. This then provides a score between 0 and 100, equating to four (4) levels of activation
that represent the person’s concept of themselves as an active manager of their health and health
care (see table 1).
The Project also included demographic questions that were divided into must have responses at the
beginning of the survey and good to have responses at the end of the survey, with the PAM tool in

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the middle. The survey was designed to have as much of the must have information at the front such
that if participants found the survey too long, a maximum amount of necessary data would still be
obtained. The survey was also planned to have less than 40 questions in total. As research indicates
that surveys with more than 40 questions are less likely to be completed by respondents.
The first selection of demographic data followed the census questions and were aimed at identifying
age, gender, language and finally living and working suburb to address eligibility. The final
demographic data identified potential factors that might be impacted by a person’s level of health
activation such as health service use, working status, education level etc.
Table 1: Activation Levels
Activation
Level
Likely Characteristics
Level 1 Does not feel in charge of their own health and care. Managing health is overwhelming for them
with all of life’s other challenges. Lacks confidence in their ability to manage health. Has few
problem solving skills and poor coping skills. They may not be very aware of own behaviours.
Level 2 May lack basic knowledge about their condition, treatment options, and/or self-care. Have little
experience or success with behaviour change. Look to their doctor to be the one in charge. Low
confidence in their ability to manage health.
Level 3 Have the basic facts of their condition and treatments. Some experience and success in making
behavioural changes. Some confidence in handling limited aspects of their health.
Level 4 Have made most of the necessary behaviour changes, but may have difficulty maintaining
behaviours over time or during times of stress.
2.3. STATISTICAL ANALYSIS PLAN
Insignia Health provides a guide to measurement for the analysis of the PAM13TM
tool. (26)
The demographic data was analysed to determine whether responses were representative of the
SESML population and any identifiable sub-groups (ethnicity, age, sex, culture, language, income,
education). The sub-groups were analysed to determine any statistically significant relationships to
engagements levels in relation to the PAM13TM
responses.
2.4. PARTICIPANT SELECTION
2.4.1. SAMPLE SIZE OR POWER CALCULATION
The sample size was determined by using a confidence interval of 3 and confidence level of 95%. (25)
SESML population over 18 years of age is 345,924. Thus the minimum survey sample required was
1064.
The SESML region has a population of 442,858 and the population over 18 year of age is 345,924. (24).
To obtain a statistically representative sample, a minimum of 1064 surveys was required. This is
using a confidence level of 95% and a confidence interval of 3.
As the survey intended to capture a population base line including all health consumers, residents
and workforce personnel in the SESML region aged 18 years and over, the inclusion and exclusion
criteria covered the parameters in the table below.

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Table 2: Participant selection
Included population groups Excluded population groups
 People aged 18 years and over
 People who reside or work in the SESML
region
 People of Aboriginal and Torres Strait
Islander origin
 People from CALD communities
 People experiencing no significant health
issues
 People with chronic disease, disabilities or
mental health issues
 Carers
 People who do not reside or work in the
SESML region
 People under the age of 18
 People living in residential care facilities
 People requiring care for significant mental
health, disabilities or other issues that
precludes them from being able to give
informed consent to participate in the
study.
Participation in the study was sought through a convenience sample of the SESML population
through:
 Advertisements (e.g. newspaper, posters in public places and health services)
 Information letter via email to SESML stakeholder networks and forums.
 Hard copy distribution and SESML stakeholder sites with collection box.
 Face to face via stalls in shopping centres, hospital sites, and at events.
The investigators did not screen potential participants. Participation in the study was voluntary and
contributors were advised of this on the information sheet and FAQ prior to completing the survey.
Distribution was through third party avenues such as word of mouth, local advertising and emails via
networks.
Figure 2: Data Safety

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3. RESULTS
Evaluation questions were posed to guide the analysis of the PAMTM
project data to answer the
identified objectives and aims as outlined above. The questions posed were as follows:
Objective 1 questions:
i. Determine how easy it is to implement by:
a. Quantifying the level of uptake across SESML.
b. Establishing how many surveys have been completed.
ii. Data quality to be assessed by recording the mean, median and percentage of missing
data and the percentage of ‘non applicable’ answers.
i. Establish a whole of population baseline for health engagement levels in the South
Eastern Sydney region.
ii. Identify areas for intervention at a SESML region population level.
i. Prepare a report on findings including any correlations between PAM13TM
scores and
demographic, health and literacy characteristics.
ii. Make recommendations on the use of PAM13TM
as a tool for measuring patient
engagement before and after an intervention, among the SESML population.
This section reports the results of evaluation questions for Objectives 1 and 2 as outlined above. The
evaluation questions for Objective 3 are covered as follows:
i.
This report as a whole serves as the report which identifies any correlations between
PAM13TM
scores and demographic, health and literacy characteristics. (Tables 13&14).
ii. Recommendations on the use of PAM13TM
are outlined in the RECOMMENDATIONS
chapter.
3.1. OBJECTIVE 1 QUESTIONS:
3.1.1. EASE OF IMPLEMENTATION
The following data assists in determining the ease with which the PAM tool can be implemented
across SESML.

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a. Graph 1 shows the level of uptake of the PAM hard copy survey across the
SESML region.
Graph 1: Number of Hard Copy Surveys by Collection Areas
 551 hard copy surveys were completed and received through survey boxes, mail, from
stalls.
 63 hard copy surveys were obtained from GP and Allied Health Practices across the LGA’s.
 908 surveys were submitted electronically directly into SurveyMonkey by respondents.
b. Number of surveys completed
 1522 surveys (online and hardcopy) were returned; of which 5 (0.3%) were ineligible as
respondents did not fit eligibility criteria of living and/or working within the SESML
catchment,
 1490 (97.9%) fit eligibility criteria and responded to a minimum of 7 questions to the
PAM13TM tool. Graph 2 shows the breakdown of eligible and ineligible surveys.
Graph 2: Eligible vs Ineligible Responses
0
20
40
60
80
100
120
140
Number of Hard Copy Surveys by Collection Areas
97.9%
0.3%
1.8%
Eligible Vs Ineligible Surveys
Eligible
Returned
ineligible
Returned
PAM
incomplete

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3.1.2. ASSESSMENT OF DATA QUALITY
Additional questions designed by the project team to obtain feedback on the ease and efficiency of
completing the survey. Some respondents (n=459) elected to provide this additional feedback. This
process was non-compulsory and the results are as follows:
1. How long did the survey take to complete?
 97.6% (n= 448) of respondents answered 5-10 minutes;
 2.4% (n=11) of respondents answered 10-20 minutes;
 1031 respondents (69.2%) did not answer this additional question
2. Did you have any difficulty answering the questions?
Thirty three (33) respondents (7.2%) indicated difficulty in answering some of the questions.
The main themes from respondents were:
 Some options on Likert scales were not appropriate or didn’t offer enough options to
choose from;
 Respondents with more than one condition requiring ongoing treatment were not able to
indicate the duration for each, as the survey only allowed one option for “time since
diagnosis”;
 Some questions were quite time specific, while others were broad in time, sometimes
making it difficult to know how to respond;
 Lack of clarity around what is defined as a health service; suggestion was put forward by
numerous respondents that a definition be included to provide context for respondents;
3. Do you have any other comments or suggestions for improving the survey?
Seventy five (75) respondents (16.3%) provided further comments or suggestions for
improving the survey. The main themes from respondents were:
 Allowing more ‘Comment’ options so respondents could explain their response;
 Include more options for responding to the health provider they get regular care from to
include a range of Allied Health Professionals;
 Allow multiple options for “time since diagnosis” to accommodate for people with
multiple conditions requiring more than six months treatment;
 Ask what respondents profession is, as respondents who work in the health area could
skew results;
 Better define questions and responses including clearer as to why questions are being
asked
 Ask about what services are missing and what people look for when choosing a health
service e.g. cost, location etc.
i. In line with other studies conducted around the PAM13TM
tool, surveys returned with
fewer than 7 questions answered on the PAM13TM
tool were eliminated from analysis.

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Table 3: Mean and median per PAM question and the Activation Level.
PAM
Q1
Score
PAM
Q2
Score
PAM
Q3
Score
PAM
Q4
Score
PAM
Q5
Score
PAM
Q6
Score
PAM
Q7
Score
PAM
Q8
Score
PAM
Q9
Score
PAM
Q10
Score
PAM
Q11
Score
PAM
Q12
Score
PAM
Q13
Score
Activation
Level
(coded)
Activation
Score
Adjusted
raw
score
N Valid 1490 1490 1490 1490 1490 1490 1490 1490 1490 1490 1490 1490 1490 1490 1490 1490
Mean 3.713 3.631 3.436 2.726 3.428 3.452 3.500 3.181 3.040 3.136 3.250 3.083 2.944 3.364 70.818 43.96
Median 4.000 4.000 4.000 3.000 3.000 4.000 4.000 3.000 3.000 3.000 3.000 3.000 3.000 4.000 70.800 44.00
On average the sampled population has an Activation Level of 3, and a mean activation score of 70.8 (table 3). Respondents in this Activation Level are
characterised as having "the basic facts of their conditions and treatments. Some experience and success in making behavioural changes. Some confidence
in handling limited aspects of their health." (26)
Table 4: Breakdown of missing and actual responses per question
PAM Question # Missing responses Disagree Strongly Disagree Agree Agree Strongly Total Responses
1 (8) 7 5 9 367 1102 1490
2 (9) 7 3 22 469 989 1490
3 (10) 13 4 59 658 756 1490
4 (11) 325 1 59 477 628 1490
5 (12) 6 5 52 709 718 1490
6 (13) 4 5 75 635 771 1490
7 (14) 22 2 26 599 841 1490
8 (15) 82 4 88 704 612 1490
9 (16) 96 4 136 762 492 1490
10 (17) 11 22 231 716 510 1490
11 (18) 15 5 113 816 541 1490
12 (19) 20 5 225 821 419 1490
13 (20) 9 34 360 715 372 1490
Totals 617 99 1455 8448 8751

17 | P a g e
The results in Table 5 show the level of response for each question in the PAM13TM
tool; of particular
note is the large number of missing responses for PAM13TM
question 4; this question also had the
lowest mean score (2.7) as shown in Table 4. This question asks “I know what each of my prescribed
medications do.”
It should be noted that the PAM13TM
tool questions 1-13 are referred to as questions (8-20) in the
PAM project survey; the question number as referred to in Table 5 responds to the number in the
PAM13TM
tool, the number in brackets refers to the number within the overall survey.
Table 5: Summary of response rates for demographic questions
Measure Result Comment
Number of surveys returned 1522 Total number of surveys returned
Number of eligible surveys 1517 5 respondents lived and/or worked outside of the eligible
catchment area
Count of PAM13TM tool completed 1490 27 respondents were excluded as they completed less than 7
questions in the PAM13TM tool
Q1 Count of year of birth completed 1467 26 respondents did not complete their year of birth
Q2 Count of gender completed 1474 16 respondents did not complete gender
Q3 Count of postcode where live
completed
1472 19 respondents did not complete postcode where they live
Q4 Count of postcode where work
completed
1094 397 respondents listed no postcode of work or N/A
Q5 Count of Aboriginal/Torres Strait
Islander completed
1421 69 did not indicate whether they are or are not Aboriginal/Torres
Strait Islander
Q6 Count of English as first language
completed
1470 20 respondents did not indicate whether or not English is their
first language
Q7 Count of other languages spoken
completed
1365 125 respondents did not indicate whether they speak a LOTE at
home
Q21 Count of regular GP or family
doctor completed
1466 24 respondents did not indicate whether they have a regular GP
or Family Doctor
Q22 Count of private health insurance
completed
1463 27 respondents did not indicate whether they have Private Health
Insurance or not
Q23 Count of where get most care
completed
1829 This question allowed respondents to select multiple options. 46
respondents selected more than one option
Q 24 Count of number of times used
health service in last 12 months
completed
1461 29 respondents did not indicate how many times they have used a
health service in the past 12 months
Q25 Count of interpreter used in last 12
months completed
1456 34 respondents did not indicate whether or not they had used an
interpreter with health care professional in past 12 months
Q26 Count of access health information
on internet completed
1457 33 respondents did not indicate how often they access health
information from the internet
Q27 Count of current health condition
completed
1459 31 respondents did not indicate if they do or do not have a
current health condition lasting longer than 6 months which
requires medical treatment
Q28 Count of diagnosis completed 712 778 respondents did not indicate how long ago they were
diagnosed
Q29 Count of health rating completed 1449 41 respondents did not rate their current health status
Q30 Count of used mental health
professional completed
1441 49 respondents did not indicate whether they had seen a mental
health professional in the past 12 months
Q31 Count of education completed 1452 38 respondents did not indicate their highest level of education
completed
Q32 Count of employment status
completed
1456 34 respondents did not indicate their 'employment' type; 51
indicate they fit into more than one type
Count of how you found out about
survey completed
1589 This question allowed respondents to select multiple options.

18 | P a g e
3.2.1. WHOLE OF POPULATION BASELINE FOR HEALTH ACTIVATION LEVELS
The tables below shows the baseline measures per activation level.
Table 6: Activation Levels
Activation
Level
Likely Characteristics Number of
respondents
% of
respondents
Level 1
(PAM score
of 47.0 or
lower)
Does not feel in charge of their own health and care. Managing
health is overwhelming for them with all of life’s other
challenges. Lacks confidence in their ability to manage health.
Has few problem solving skills and poor coping skills. They may
not be very aware of own behaviours.
91 6.1%
Level 2
(PAM score
of 47.1 to
55.1)
May lack basic knowledge about their condition, treatment
options, and/or self-care. Have little experience or success with
behaviour change. Look to their doctor to be the one in charge.
Low confidence in their ability to manage health.
129 8.7%
Level 3
(PAM score
of 55.2 to
67.0)
Have the basic facts of their condition and treatments. Some
experience and success in making behavioural changes. Some
confidence in handling limited aspects of their health.
416 27.9%
Level 4
(PAM score
of 67.1 or
above)
Have made most of the necessary behaviour changes, but may
have difficulty maintaining behaviours over time or during times
of stress.
854 57.3%
 As shown in table 6, of the 1490 completed surveys, more than 50% of respondents (n=854) fit
the criteria for Level 4 Activation.
 Only 6.1% and 8.7% of respondents (n=91, n=129) fit into Level 1 and 2 Activation respectively.
 Over a quarter of respondents (27.9%) fit criteria for Level 3 Activation (n=416).
3.2.2. AREAS FOR INTERVENTION
Statistical significance has been obtained regarding each variable and Activation Level where able. Note
that in some instances Chi Square tests could not be performed for categorical variables, as there was a
high percentage of responses that had an expected count below 5 or there were too many variables to
calculate (Tables 13 & 14).

19 | P a g e
3.2.3. KEY POINTS OF DEMOGRAPHIC DATA
Age
Respondents were asked to complete their year of birth. The results are shown in Graph 3.
Graph 3: Activation Levels by Year of Birth (1922-1997)
 Year of Birth was collected on surveys as opposed to age;
 Q-Q plots show that age is normally distributed across respondents, skewness and kurtosis
coefficients are within acceptable ranges for each Activation Level
 The highest number of responses was from respondents born in 1959 and 1964 (n=47); the
lowest number of responses was from those born in 1997 (n=1)
Gender
Respondents were asked to identify their gender. The options were: Male or Female. The results are
shown in Graph 4
0
5
10
15
20
25
30
35
40
45
50
1922
1927
1930
1933
1936
1939
1942
1945
1948
1951
1954
1957
1960
1963
1966
1969
1972
1975
1978
1981
1984
1987
1990
1993
1996
Activation Level by Year of Birth (1922-1997)
Level 4
Level 3
Level 2
Level 1
71.4% 72.7% 73.4%
80.1%
28.6% 27.3% 26.6%
19.9%
0.0%
20.0%
40.0%
60.0%
80.0%
100.0%
Level 1 Level 2 Level 3 Level 4
Activation Level by Gender
Female Male
Graph 4: Activation Level by Gender

20 | P a g e
 16 respondents did not include gender
 77% of respondents were female (n=1136);
 Females represented 71.4%, 72.7%, 73.4% and 80.1% of Levels 1 – 4 respectively, while
males accounted for 28.6%, 27.3%, 26.6% and 19.9% of respondents in each Level 1 – 4
respectively
Analysis of gender showed no significance between gender and Activation Level.
Postcodes
Respondents were asked to identify the postcode in which they lived and the postcode in which they
worked. Responses were open text; the results are shown in Graphs 5 & 6.
Living Postcode
Graph 5: Postcode where respondents live by Activation Level
Working Postcodes
Graph 6: Postcode where respondents work by Activation Level
 Responses were received from residents in 24 of the 25 postcodes within our catchment; no
respondents identified living in the area with postcode 2172 (Sandy Point).
 96 postcodes for ‘Living Address’ were outside of the SESML catchment
0.0%
20.0%
40.0%
60.0%
Postcode where respondents live by Activation Level
Sutherland St George Other
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
Postcode where respondents work by Activation Level
Sutherland St George Other

21 | P a g e
 Responses were received from people working in 22 of the 25 postcodes within our
catchment; no respondents identified working in the area with postcode 2225 (Oyster Bay),
2231 (Kurnell) or 2172 (Sandy Point).
 71 postcodes for ‘Working Address’ were outside of the SESML catchment; an additional two
were invalid postcodes
 Analysis of St George vs Sutherland regions showed that 798 (54.2%) of respondents who
provided a home postcode (n=1472) live within the Sutherland Shire and 462 (31.4%) of
respondents live within the St George region (which incorporates the Hurstville, Kogarah and
Rockdale LGAs)
 212 (14.4%) of respondents who provided a home postcode (n=1472) live outside of the
SESML catchment area
 390 (35.6%) of respondents who provided a work postcode (n=1094) work within the
Sutherland Shire and 526 (48.1%) of respondents work within the St George region (which
incorporates the Hurstville, Kogarah and Rockdale LGAs)
 178 (16.3%) of respondents who provided a work postcode (n=1094) work outside of the
SESML catchment area
Aboriginal/Torres Strait Islander
Respondents were asked to identify if they were Aboriginal and/or Torres Strait Islander. The options
were: No; Yes, Aboriginal; Yes, Torres Strait Islander; Yes, Aboriginal and Torres Strait Islander. The
results are shown in Graph 7.
Graph 7: Aboriginality by Activation Level
 2% of respondents (n=1421) identified as Aboriginal; 0.1% of respondents identified as being
Aboriginal and Torres Strait Islander. No respondents identified as being Torres Strait
Islander.
 97.9% of respondents (n=1421) did not identify as being Aboriginal and/or Torres Strait
Islander
 Six (6) of those who identified as Aboriginal were at Level 2 Activation (20.7%), ten (10) were
at Level 3 Activation (34.5%) and thirteen (13) were at Level 4 Activation 44.8%.
English as First Language
Respondents were asked if English is their first language. The options were: No, or Yes. The results
are shown in Graph 8.
0
5
10
15
Aboriginality by Activation Level
Aboriginal Torres Strait Islander Aboriginal and Torres Strait Islander

22 | P a g e
Graph 8: English as First language by Activation Level
 211 respondents (14.4%) stated that English is not their first language
 27.5% of those who fit Level 1 Activation, responded that English is not their first language;
 2.8% of those who fit Level 4 Activation, responded that English is not their first language
 16.5% of those who fit Level 2 Activation and 14.1% of those who fit Level 3 Activation
stated that English is not their first language
The chi-square statistic is 15.0272. The P-Value is 0.001794. The result is significant at p < 0.05.
When assessing the proportions of those with English as their first language in each Activation Level,
a moderate association (0.451) is seen for respondents with English as their first language and Level
1 Activation, with a moderate-high association (0.669, 0.719, 0.745) for those with English as a first
language and Levels 2, 3 & 4 Activation respectively.
Speak Language Other Than English a Home
Respondents were asked if they speak a Language Other Than English (LOTE) at home. The options
were: No, or Yes. If yes, they were then asked to identify their first language. Fifteen languages were
listed with an additional option of ‘Other’. If ‘Other’ was selected respondents were asked to specify.
The results are shown in Graph 9.
0.0%
20.0%
40.0%
60.0%
80.0%
100.0%
English as First Language vs Activation Level
No
Yes
Arabic
7% Bengali
1%
Cantonese
16%
Croatian
3%
Filipino/Tagalog
3%
German
3%
Greek
13%
Indonesian
1%
Italian
8%
Macedonian
5%
Mandarin
10%
Nepali
1%
Russian
1%
Spanish
8%
Vietnamese
0%
35 Other Languages
21%
Percentage of Languages other than English Spoken at home
Graph 9: Languages other than English Spoken at home

23 | P a g e
 50 languages, other than English, were identified by respondents as being spoken at home
 A minimum of one person spoke each of the 15 languages identified on the survey; 35
additional languages were identified by respondents
 318 (23%) respondents (n=1365) speak a language other than English at home
 Sufficient data was available to analyse significance of speaking a language other than
English on Activation Level
Table 7: Chi-Square Test result for speaking Language Other Than English at home
Chi-Square Tests
Value df Asymp. Sig. (2-sided)
Pearson Chi-Square 23.975a
6 .001
Likelihood Ratio 21.663 6 .001
Linear-by-Linear Association 7.094 1 .008
N of Valid Cases 1490
a. 0 cells (0.0%) have expected count less than 5. The minimum expected count is 7.63.
Pearson Chi-Square statistic has a value of 23.975 with a significance of .001. The significance value
is below the alpha level of .05, therefore this result is statistically significant.
When assessing the proportions of those who speak a LOTE at home in each Activation Level, a
moderate negative association (-0.44, -0.52) is seen for respondents who speak a LOTE at home and
Levels 1 & 2 Activation respectively, with a strong negative association (-0.69, -0.73) for those who
speak a LOTE at home and Levels 3 & 4 Activation respectively.
Those with a GP/Family Doctor
Respondents were asked if they have a regular GP or Family Doctor. The options were: No or Yes.
The results are shown in Graph 10.
Graph 10: GP/Family Doctor by Activation Level
 89.6% of respondents (n=1466) have a GP or Family Doctor.
 91.5% (n=771) of respondents who fit Level 4 Activation have a GP or Family Doctor; only
8.5% (n=72) of those without a GP or Family Doctor fit Level 4 Activation.
0.0%
20.0%
40.0%
60.0%
80.0%
100.0%
GP/Family Doctor by Activation Level
Wthout GP/Family Doctor With GP/Family Doctor

24 | P a g e
Table 8: Chi-Square Test result for those with a regular GP/Family Doctor
Chi-Square Tests
Pearson Chi-Square
17.410a
6 .008
Linear-by-Linear Association
12.337 1 .000
Pearson Chi-Square has a value of 17.410 with a significance of .008. The significance value is below
the alpha level of .05, therefore this result is statistically significant.
When assessing the proportions of those with a regular GP/Family Doctor in each Activation Level, a
moderate association (0.40, 0.48) is seen for respondents with a regular GP/Family Doctor and
Levels 1 & 4 Activation respectively, with a low-moderate association (0.23, 0.38) for those with a
regular GP/Family Doctor and Levels 2 & 3 Activation respectively.
Private Health Insurance
Respondents were asked if they have Private Health Insurance. The options were: No or Yes. The
Graph 11: Private Health Insurance by Activation Level
 1178 respondents (80.5%) stated that they have Private Health Insurance.
 83.8% (n=705) of those in Level 4 Activation have Private Health Insurance.
 One third (33%) of those respondents in Level 1 Activation do not have Private Health
Insurance (n=29).
0.0%
20.0%
40.0%
60.0%
80.0%
100.0%
Private Health Insurance by Activation Level
No Yes

25 | P a g e
Table 9: Chi-Square Test result for Private Health Insurance
Chi-Square Tests
6 .002
Assessing the proportions of those with Private Health Insurance in each Activation Level to those
without, shows that a strong association (0.68) is seen for those respondents with Private Health
Insurance and Level 4 Activation; a low association (0.34) is seen for those with Private Health
Insurance and Level 1 Activation and a moderate association (0.53, 0.56) is seen for those with
Private Health Insurance and Level 2 & 3 Activation respectively.
Setting where you get most of your care
Respondents were asked where they get most of their care in relation to ongoing health conditions
they have. The options were: GP or Family Doctor; Emergency Department; Hospital Doctor;
Specialist; No-one; or Other. Responders could select more than one option. The results are shown
in Graph 12.
Graph 12: Where respondents get most of their care by Activation Levels
0.0%
20.0%
40.0%
60.0%
80.0%
GP or Family
Doctor
Emergency
Department
Hospital Doctor Specialist Noone Other
Where Respondents Get Most Of Their Care
By Activation Level

26 | P a g e
 46 (2.5%) respondents selected more than one option regarding where they get most of
their care in relation to ongoing health conditions.
 1351 (73.9%) respondents said they get most of their care from a GP or Family Doctor.
 283 (15.5%) respondents said they get most of their care from a Specialist.
 24 (1.3%) respondents said they received most of their care from no-one.
 100 (5.5%) respondents selected ‘Other’, covering 23 groups/types of other provider;
Chiropractors were identified by 22 people as the provider they get most of their care in
relation to ongoing health conditions.
 5 people noted that they do not have ongoing health conditions.
Used health services in past 12 months
Respondents were asked how many times they have used a health service in the past 12 months on
a 4 point Likert scale. The options were: None; 1-5 times; 6-12 times; or More than 12 times. The
 94.1% of respondents who fit Level 4 Activation have used a health service at least once in
the past twelve months.
 82 respondents (5.6%) have not used any health services in the past twelve months.
 1379 respondents have used a health service at least once in the past twelve months.
 The highest proportion of respondents used a health service between 1 -5 times in the past
twelve months (61.2%), followed by 6-12 times (20.9%), more than 12 times (12.3%) and
none (5.6%).
Table 10: Chi-Square Test result for used a health service in the past twelve months
Chi-Square Tests
12 .013
Linear-by-Linear Association
2.679 1 .102
None
6%
1-5 times
61%
6-12 times
21%
more than 12 times
12%
Frequency of healthcare use in past 12 months
Graph 13: Frequency of healthcare use in past 12 months

27 | P a g e
Use of interpreter
Respondents were asked if they had used an interpreter service to communicate with a health care
professional in the last 12 months. The options were: No or Yes. The results are shown in Graph 14.
Graph 14: Use of interpreter for health services in past 12 months by Activation Level
 1.4% of respondents (n=20) have used an interpreter for health care in the past twelve
months.
 Those who have used an interpreter are 3 times more likely to fit into Activation Level 1
(3.4%) as opposed to Activation Level 4 (1.1%).
 45% of those who have used an interpreter fit Level 4 Activation.
The chi-square statistic is 12.5691. The P-Value is 0.050413. The result is not
significant at p < 0.05.
Access Health information from the internet
Respondents were asked how often they access health information from the internet on a 3 point
Likert scale. The options were: Never, Sometimes and Often. The results are shown in Graph 15.
0.0%
20.0%
40.0%
60.0%
80.0%
100.0%
Use of interpreter for health services in past 12 months by
Activation Level
No Yes

28 | P a g e
Graph 15: Use of internet to access health information by Activation Level
 55.7% of respondents admitted to using the internet to access health information
sometimes; 26.9% often and 17.4% never.
 57.6% of those in Level 4 Activation have used the internet sometimes to access health
information.
Table 11: Chi-Square Test result for how often use the internet to access health information
Chi-Square Tests
9 .000
Have current health condition lasting more than 6 months
Respondents were asked if they have any current health conditions lasting longer than 6 months
requiring medical treatment. The options were: No or Yes. If respondents answered yes, they were
asked to specify. The results are shown in Graph 16.
0.0%
20.0%
40.0%
60.0%
80.0%
Use of internet to access health information by Activation
Level
Never Sometimes Often

29 | P a g e
Graph 16: Health Condition by Activation Level
 719 (49.3%) respondents indicated that they have a current health condition lasting longer
than 6 months which requires medical treatment; 740 (50.7%) indicated that they do not
have a current health condition lasting longer than 6 months which requires medical
treatment; 31 respondents left this question blank.
 60.7% of respondents who fit Activation Level 1, and responded to this question, have a
current health condition lasting longer than 6 months which requires medical treatment.
 47.0% of respondents who fit Activation Level 4, and responded to this question, have a
current health condition lasting longer than 6 months which requires medical treatment.
Rate current health status
Respondents were asked to rate their own health on a 5 point Likert scale. The options were: Poor,
Fair, Good, Very Good, and Excellent. The results are shown in Graph 17.
Graph 17: Self rating of health by activation level
60.7%
52.4%
50.5%
47.0%
39.3%
47.6%
49.5%
53.0%
LE VE L 1 LE VE L 2 LE VE L 3 LE VE L 4
Health Condition by Activation Level
Yes No
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
Self Rating of Health by Activation Level
Poor Fair Good Very Good Excellent

30 | P a g e
 84% of respondents rate their current health status as good, very good or excellent
(n=1449).
 92.4% of respondents who fit Level 4 Activation rated their health as good, very good or
excellent; 43.8% or respondents who rated their health as good, very good or excellent fit
Level 1 Activation.
 93.3% of respondents who fit Level 1 Activation rated their health as poor (9.0%), fair
(47.2%) or good (37.1%).
 10.4% of respondents rated their health as Excellent, these respondents fit into Level 3 and
Level 4 Activation Levels (11.3% and 88.7% respectively).
Table 12: Chi-Square Test result for self-rating of health
Chi-Square Tests
15 .000
The chi-square statistic is 312.6071. The P-Value is < 0.00001. The result is significant
at p < 0.05.
Used Mental Health Professional in the last 12 months
Respondents were asked if they had used a mental health professional in the last 12 months. The
options were: No or Yes. The results are shown in Graph 18.
Graph 18: Use of Mental Health Services by Activation Level
 81.5% of respondents (n=1441) stated that they had not used a mental health professional
in the last 12 months.
 49% of those who had used a mental health professional in the last 12 months fit Level 4
Activation.
 84.2% of respondents who fit Level 4 Activation had not used a mental health professional in
the last 12 months.
0.0%
20.0%
40.0%
60.0%
80.0%
100.0%
Use of Mental Health Services by Activation Level
No Yes

31 | P a g e
 Approximately one –third of respondents who fit each Level 1 and Level 2 Activation had
used a mental health professional in the last 12 months.
The chi-square statistic is 22.4473. The P-Value is 0.0000053. The result is
significant at p < 0.05.
Assessing the proportions of those who have used a mental health professional in the past 12
months in each Activation Level compared to those who have not revealed a strong negative
association (-0.64, -0.68) for Levels 3 & 4 respectively for those who have used a mental health
professional in the past 12 months; there is a moderate negative association (-0.41, -0.40) for Levels
1 & 2 respectively.
Education
Respondents were asked what their highest level of completed education was. The options were:
Primary: 5-12 years; Secondary: 13-16 years; Secondary: 17-18 years; Tertiary: Trade Certificate or
Diploma; Tertiary: Bachelor Degree; or Post Graduate qualifications. The results are shown in Graph
19.
Graph 19: Education by Activation Level
 78.9% of respondents (n=1452) had completed either Tertiary level or Post graduate level
education.
 50% of those whose highest completed education was Primary: 5-12 years fit Level 4
Activation; 37.5% fit Level 1 Activation and 12.5% fit Level 2 Activation.
 81.7% of respondents who fit Level 4 Activation had completed either Tertiary level or Post
graduate level education.
 64% of respondents who fit Level 1 Activation had completed either Tertiary level or Post
graduate level education; 36% of respondents who fit Level 1 Activation had completed
Primary or Secondary education.
 77% of respondents who fit Level 2 and 3 Activation had completed either Tertiary level or
0.0%
5.0%
10.0%
15.0%
20.0%
25.0%
30.0%
35.0%
40.0%
Education Level by Activation group
Primary: 5-12 years Secondary: 13-16 years
Secondary: 17-18 years Tertiary: trade certificate or diploma
Tertiary: Bachelor degree Post graduate qualifications

32 | P a g e
Post graduate level education; 23% of respondents who fit Level 2 and 3 Activation had
completed either Primary or Secondary education.
Chi-Square test was not conducted on this variable as there were more than 5 variables to
compare.
Work/employment status
Respondents were asked their current work/employment situation by asking “Are you currently”
with the options of: Student; Employed for wages; Self-employed; Homemaker; Pensioner; Out of
work and looking for work; Self-funded retiree; Out of work but not currently looking for work; or
‘Other’. Those who responded with ‘Other’ were asked to specify. The results are shown in Graph
20.
Graph 20: Employment status by activation Level
 64.4% of respondents were employed for wages; 13% of respondents were pensioners
 1.9% of respondents stated that they were currently out of work (1.3% of whom are looking
for work; 0.6% are not currently looking for work);
 Homemakers and Students each accounted for 2.3% of respondents
 55.1% of respondents who fit Level 1 Activation are employed for wages; 18% are
pensioners
 67.4% of respondents who fit Level 4 Activation are employed for wages; 9.7% are
pensioners
 8.4% of respondents are self-funded retirees and they account for 7.9%, 5.6%, 8.7% and
8.8% of respondents in Level 1, 2, 3 and 4 Activation respectively.
Chi-Square test was not conducted as there were more than 5 variables to compare
Employed for wages
64%
Home Maker
2%
Out of work and
looking for work
1%
Out of work but not
currently looking for
work
1%
Pensioner
13%
Self-employed
8%
Self-funded Retiree
9%
Student
2%
Employment status by activation Level

33 | P a g e
How did you find out about us, key points?
Respondents were asked to identify how they found out about the PAM13TM
survey. Nine options
for response were included, as well as an ‘Other’ option. Where ‘Other’ was selected, respondents
did not have to specify. The results are shown in Graph 21.
Graph 21 How respondents found out about PAM
 1589 responses were received by 1490 respondents.
 106 responses selected ‘Other’. 'Other’ responses were categorised into 14 groups, the
highest responses fit into 'Work' (46.5%), 'Council' facilities (14.9%) and Westfield/shopping
centre stalls (11.9%).
 31 respondents selected more than one option for 'How did you hear about PAM?'
 40.6% of responses identified ‘Email’ as the method of hearing about PAM; 33.4% pf
responses identified ‘Survey Box’ as the method of hearing about PAM.
 45.1% of those who fit Level 4 Activation identified ‘Email’ as how they heard about PAM,
followed by 29.7% via ‘Survey Box’ and 6.8% via ‘Other’ methods.
 44.2% of those who fit Level 1 Activation heard about PAM through ‘Survey Box’, followed
by 27.4% via ‘Email’ and 9.5% via ‘Word of Mouth’.
Leader
3%
Twitter
0%Facebook
1% Web page
2%
Email
41%
Poster
2%
Word of
mouth
7%
Survey box
33%
e-newsletter
4%
Other
7%
How respondents found out about PAM

34 | P a g e
3.3.1. REPORT ON FINDINGS
The finding of the project including the correlations between PAM13TM
scores and demographic and
health characteristics are addressed in Tables 13 and 14 below. However, it should be noted that the
PAM13TM
tool is a health engagement tool, which is a component of overall health literacy but does
not itself measure health literacy.
Correlations assessments were conducted on the following quantifiable demographic data:
Table 13: Correlation assessments on continuous variables
Variable Correlation
Year of birth Pearson’s correlation has a value of 0.040, the significance is 0.127 therefore
there is no significance between Activation Levels based on year of birth
Chi-square tests were used to test for relatedness or independence for categorical variables as
appropriate, on the following variables with activation levels:
Table 14: Chi-Square test results on categorical variables
Variable Relationship
Gender Unable to determine as more than 20% of cells had expected
value of less than 5
The chi-square statistic is 12.2351. The P-Value is 0.056924.
The result is not significant at p < 0.05. Meaning there is not a
significant difference in Activation Level between genders
Aboriginal/Torres Strait Islander
status
Unable to determine as more than 20% of cells had expected
value of less than 5
English as first language The chi-square statistic is 15.0272. The P-Value is 0.001794.
The result is significant at p < 0.05. Meaning there is a
significant difference in Activation Level between those who
have English as a first language and those who do not
Language other than English
spoken at home
Pearson Chi-Square has a value of 23.975 with a significance of
.001. The significance value is below the alpha level of .05,
therefore there is a statistically significant difference in
Activation Level between those who speak a language other
than English at home and those who do not
Whether respondents have
regular GP or Family Doctor
have a GP or Family Doctor and those who do not
private health insurance
have Private Health Insurance and those who do not

35 | P a g e
Variable Relationship
Where respondents get most of
their care
Chi-Square test was not conducted as there were more than 5
variables to compare.
Number of times respondents
have used health care service in
past 12 months
The chi-square statistic is 25.295. The P-Value is 0.013485. The
result is significant at p < 0.05. Meaning there is a significant
difference in Activation Level based on the number of times
respondents have used health care services in the past 12
months.
Whether respondents used an
interpreter in the past 12
months
have used an interpreter in the past 12 months and those who
have not.
Whether respondents access
health information from the
internet
significant difference in Activation Level based on whether
respondents access health information from the internet.
Whether respondents have a
current health condition lasting
longer than 6 months
The chi-square statistic is 8.0667. The P-Value is 0.233258. The
result is not significant at p < 0.05. Meaning there is not a
significant difference in Activation Level between those with a
health condition lasting longer than 6 months and those
without.
How long since diagnosis The chi-square statistic is 10.8773. The P-Value is 0.284215.
significant difference in Activation Level based on time since
diagnosis.
Respondents self-rating on their
current health
The chi-square statistic is 312.6071. The P-Value is < 0.00001.
significant difference in Activation Level based on respondents
self-rating on their current health.
used a mental health
professional in the past 12
months
The chi-square statistic is 22.4473. The P-Value is 5.3E-05. The
result is significant at p < 0.05. Meaning there is a significant
difference in Activation Level based on whether respondents
have used a mental health professional in the past 12 months.
Highest level of education
obtained by respondents
Current ‘work/employment’
situation
3.3.2. RECOMMENDATIONS ON THE USE OF PAM13TM
Recommendations on the use of PAM13TM
as a tool for measuring patient engagement before and
after an intervention, are proposed in line with the Insignia PAM13TM
license materials (26)
. Each
activation level has proposed strategic goals and action plans to be used in a patient centred
approach to improving health outcomes.

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4. KEY FINDINGS
Key Finding 1:
 A representative sample was obtained, based on power calculation as outlined (page 13).
Based on our sample, the SESML population has an average Patient Activation Level of 3.
This finding means that, on average, the population of SESML has “the basic facts of their
health condition and treatment and some confidence in handling limited aspects of their
health.”
Key Finding 2:
 A statistically significant relationship is seen between Patient Activation Level and having
English as first language. A moderate positive association was seen for Level 1 Activation; a
moderate-high association was seen for Levels 2, 3 and 4 Activation. This finding suggests
that those with English as a first language are more likely to have a higher activation level
than those for whom English is a second language.
Key Finding 3:
 A statistically significant relationship is seen between Patient Activation Level and Individuals
who speak a LOTE at home. A moderate negative association was seen for Levels 1 and 2
Activation; a strong negative association was seen for Levels 3 and 4 Activation. This finding
suggests that those who speak a LOTE at home have a lower health activation than those
who do not speak a LOTE at home.
Key Finding 4:
 A statistically significant relationship is seen between Patient Activation Level and individuals
who have a regular GP or family doctor. A moderate positive association was seen for Levels
1 and 4 Activation; a low-moderate positive association was seen for Levels 2 and 3
Activation. This finding suggests that those with a regular GP/Family Doctor are more likely
to sit at the extremes of the PAM levels (i.e. levels 1 and 4).
Key Finding 5:
 A statistically significant relationship is seen between Patient Activation Level and individuals
who have Private Health Insurance. A graduated association was seen between Private
Health Insurance and Activation with association increasing from Level 1 to Level 4. A strong
positive association was seen for those respondents with Private Health Insurance and Level
4 Activation; a moderate association was seen for those with Private Health Insurance and
Level 2 and 3 Activation and a low association was seen for those with Private Health
Insurance and Level 1 Activation. This finding suggests that those with Private Health
Insurance are more likely to have higher activation in their health.
Key Finding 6:
 A statistically significant relationship is seen between Patient Activation Level and the
number of times individuals have used health care services in the past 12 months. This
finding suggests that there is a relationship between accessing health services and activation

37 | P a g e
level. Further analysis needs to occur in order to determine an association between
accessing health services and health activation level.
Key Finding 7:
 A statistically significant relationship is seen between Patient Activation Level and the
frequency with which individuals access health information from the internet. This finding
suggests that there is a relationship between accessing health information from the internet
and activation level. Further analysis needs to occur in order to determine an association
between frequency of accessing health information on the internet and health activation
level.
Key Finding 8:
 A statistically significant relationship is seen between Patient Activation Level and self-rating
on current health status. This finding suggests that there is a relationship between an
individual’s self-rating of their health and activation level. Further analysis needs to occur in
order to determine an association between self-rating of health and activation level.
Key Finding 9:
 A statistically significant relationship is seen between Patient Activation Level and whether
respondents have used a mental health professional in the past 12 months. A strong
negative association was seen for Levels 3 and 4 Activation; a moderate negative association
was seen for Levels 1 and 2 Activation. This finding suggests that those who have seen a
mental health professional in the past 12 months are less likely to have high health
activation.

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5. LIMITATIONS
Throughout the establishment, implementation and delivery of this project, a number of limitations
have been identified. These are outlined below in relation to respective time periods.
5.1. Establishment
Two issues where identified during the project that had implications from the outset of the project:
1. The funding application process had a tight timeframe, which did not allow as much time to
develop a clear and concise plan. Whilst the application included a number of identified
objectives, further consultation once the project was approved highlighted the need to
modify or alter objectives to be more meaningful and achievable. Learning for future project
funding applications is to ensure appropriate parties are included in planning and design of
projects from the outset.
2. The ethics process was not considered from the outset and built into the original application
timeframes.
5.2. Implementation
Complications arose during the implementation period due to additional project requirements:
1. Survey collection period had to be postponed due to Ethics application processes; as such
our primary collection period occurred over the December-February period. This impacted
on smooth delivery of the project as project team members were on leave and recruiting
during this period was therefore limited.
5.3. Delivery
Our administration staff identified a number of limitations in the structure of the survey or survey
questions when entering hard copy responses into SurveyMonkey for collation with online
responses:
1. Some (hard copy) responses from hard copy surveys were ambiguous (i.e. Q8-20 (PAM13TM
Tool): people were ticking on the line between responses because unsure of where they fit
on Likert Scale);
2. Q32 – respondents of hard copy surveys ticked more than one option available on
SurveyMonkey, making response unusable;
3. Q26 & 29 there was not the option for those with more than one chronic disease to identify
time since diagnosis for each, rather SurveyMonkey only allowed one response;
4. Q29 – respondents wanted to report more than one rating for current health;
5. Q32 and question “how you found out about PAM” SurveyMonkey did not allow for ‘other’
to be specified in detail;
6. Question asking if respondents received help to complete survey was not included in
SurveyMonkey and therefore had to be collected separately; 11 respondents indicated that
they required assistance to complete survey. 5 of these surveys were incomplete and 6 were
complete;
7. Some questions (5) had not been entered into SurveyMonkey exactly the same as the
printed copies i.e. some wording was missing/altered.
8. Due to the delayed roll out of the survey as noted above, we had a reduced timeframe for
evaluation in order to meet project deadlines; this would have impacted on our ability to
effectively assess all aspects of the data. Fortunately we were able to secure a one month

39 | P a g e
extension on report submission, however due to such a high response rate analysis was still
limited in some respects.
Other limitations were identified from the project team during the delivery of this project:
1. As this study took a convenience sample of the population through somewhat targeted
distribution of the survey, it could be that those who responded to the survey are generally
more engaged and activated in their health care than those who chose not to participate.
2. Some target groups were more difficult to reach than others which could affect the
representativeness of the population demographics and therefore the transferability of the
results seen.
The project team supports further studies being conducted to test reliability, validity and
transferability of these results.

40 | P a g e
6. DISCUSSION
This study set out to achieve three key objectives using the Patient Activation Measure (PAM13)™
and the House of Care Framework. PAM13™ has been used to measure patient engagement levels of
the South Eastern Sydney population and provide a baseline for our population in regards to health
activation and identify any relationships and associations with a range of demographics collected. A
statistically representative sample of our population was achieved through this study. The mean
population Activation Level was 3. At this Activation Level patients “Have the basic facts of their
condition and treatments. Some experience and success in making behavioural changes. Some
confidence in handling limited aspects of their health” (26)
. No significant difference was seen in
Activation Levels based on year of birth, gender, whether respondents had used an interpreter in the
past 12 months for health care, whether respondents have a current condition lasting more than 6
months which requires treatment or time since diagnosis of condition. Significant differences were
seen in Activation Levels for those who speak a language other than English at home, those who
have a regular GP/Family Doctor, those who have Private Health Insurance, how many times health
services have been used in the past 12 months, whether health information is obtained from the
internet, self-rating on health or those who have used a mental health professional in the past 12
months.
Hibbard et al (13)
use the PAM13™ tool to identify four stages of patient activation, as outlined on
page 12 of this report. Matthews et al (17)
defines activation as “people need to believe that they
have a role to play in self-management, in collaborating with their provider and in taking preventive
action. They also need to have some skill and confidence” to manage their health. As the prevalence
of chronic disease increases, there is an increased need for patients to take part in managing their
own health (5).
This requires skills, knowledge and confidence by patients to effectively manage
ongoing health conditions. Our results suggest a high level of activation for the SESML population,
indicating that our population believes they have a role to play in managing their own health, both
alone and in collaboration with health professionals. Our results show that two (2) questions on the
PAM13
tool had a mean score of less than three (3); Questions 4 and 13. Question 4 asks respondents
to rate the degree to which they agree with the statement “I know what each of my prescribed
medications do”; low scores in this field suggest that either respondents are not aware of the
purpose of each of their prescribed medications, alternatively, as there was a large number of
missing or not ‘applicable responses’, it could indicate that respondents are not taking any
prescribed medication and therefore the question is not applicable to them. Question 13 of the tool
asks respondents to rate the degree to which they agree with the statement “I am confident that I
can maintain lifestyle changes, like eating right and exercising, even during times of stress”. Low
scores in this question suggest that behaviour change is difficult for respondents and therefore low
scores on this question could prompt health professionals to utilise motivational interviewing
techniques to support positive behaviour change.
The next steps to consider are patients’ health literacy levels to ensure they understand the
information being provided to them by health professionals involved in their health care. High
engagement does not automatically equate to understanding. To ensure effective patient centred
care, we need to ensure we have a health literate population. Most respondents in the study
reported that the tool was simple to understand and complete, with the survey and added
demographic data taking up to 10 minutes to complete for 97.6% of respondents who completed
our additional qualitative feedback questions. Statistical significance was seen in Activation Levels

41 | P a g e
for those respondents who used the Internet to access health information. This result supports the
need to ensure accurate information regarding health conditions and management or prevention of
it is easily accessible and understandable by the population. Health literacy is the means by which
we can measure an individual’s ability to understand the health information provided to them and
act accordingly. This study did not measure health literacy and therefore we cannot draw any
conclusions regarding the populations’ health literacy levels compared to activation level, however,
acknowledge that it is an important aspect of health engagement. This is an area that requires
further exploration.
Previous studies have been used to assess the effectiveness of the PAM13™ tool in specific
population groups (6,7,11),
this study set out to establish a baseline of activation levels in our
population group to lead onto recommendations for use of the PAM13™ tool for measuring patient
engagement before and after an intervention, among the SESML population. A moderate – high
association has been demonstrated between having English as a first language and Activation Level.
Conversely a negative association was seen in Activation Level for those who speak a language other
than English at home. This result is of particular interest for the SESML population, as 30% of our
population speak a LOTE at home; yet only 14% of respondents speak a LOTE. The PAM13TM
tool has
been validated in six (6) languages, further investigations should be undertaken with the translated
tool in our population to determine results when using a translated tool in populations who speak a
LOTE.
Green et al (11)
found that the PAM13TM
tool was a reliable and valid measure of patient activation
among individuals with mental health problems. 2007-08 Census data (ABS, 2011) showed that
11.2% of the SESML population had high or very high psychological distress levels; slightly lower than
the National average of 11.5%. Our results show that there is a negative association between those
who have used mental health professionals in the past 12 months and Activation; meaning that
those who have used a mental health professional in the past 12 months are more likely to have
lower Activation Levels. The SESML population who see mental health professionals could benefit
from the use of the PAM13TM
tool in regards to their ongoing management of mental health
conditions. The PAM13TM
tool, could be used to tailor the interventions to the level of their
activation and thus positively impact on the active management of their condition.
Further associations were seen for those with a regular GP/Family Doctor and those with Private
Health Insurance. Although, only moderate or low associations were seen for those with a regular
GP/Family Doctor, the association was positive; indicating that those in the population with a regular
GP/Family Doctor are generally more engaged in managing their health. These results support the
use of the PAM13TM
tool in a health care setting, as health professionals can use the PAM13TM
tool
within consultations to support patients to take an active role in their own health care whilst
increasing their activation level over time. A graduated association was seen between Private Health
Insurance and Activation with association increasing from Level 1 to Level 4, indicating that those
with Private Health Insurance are more engaged in the management of their health. 57.2% of the
SESML population has Private Health Insurance (PHIDU, 2011); this supports the result of Level 3
Activation across our population as Activation increases with Private Health Insurance. It should be
noted that 80.5% of our study sample has Private Health Insurance, which is an over representation
in comparison to the SESML population statistics; further investigation should be considered.
The majority of respondents had used health services at least once in the past twelve months. Our
results show that most respondents had used health services 1-5 times in the past twelve months,
with the rate of use decreasing as the number of visits increased. This supports the work by Hibbard

42 | P a g e
et al as cited by Donald et al (7)
who suggest that individuals who are less engaged in their health
care will access more services. From an implementation point of view, introducing the PAM13TM
tool
in health consultations could assist in guiding self-management support for individuals and reduce
the number of health services accessed by patients per year which could result in lower expense to
both the health system and the individual. Further analysis should be completed in this area to
assess the level of health expenditure for this population group to determine the potential level of
cost saving to the system and the individual.
Respondents self-rating of their health status by activation level was seen to be statistically
significant. The table on page 32 shows trends in the level of self-rating by Activation Level. Of
particular note, there is a trend of those with lower Activation Levels (i.e. 1 and 2) having a high
proportion of respondents indicating poor or fair health. Conversely, those with Levels 3 & 4 were
more likely to rate their health as very good or excellent. This could indicate that those who are
more active in managing their health rate their health more positively than those who are less
active. As such this would support the implementation of the PAM13TM
tool to support people in
increasing their role in self-management.
A range of dissemination methods was used in this study to maximise reach and achieve the
population sample. The targeted approaches of email and survey box distribution were the most
successful methods in reaching the population. These methods saw the highest number of responses
across the levels of activation. A range or methods continues to be required as not everyone has
access to IT or is IT literate. This survey was only distributed in English and thus did not access
respondents who spoke no English. Use of interpreters would have invalidated the study. Proper
validation in priority languages would be required to appropriately target the non-English speaking
population.
The House of Care Framework is a whole of system approach to patient care and provides a visual
representation of the interactions required for the system to work effectively. The Framework
provides a model of person centred, coordinated health care at three levels: personal, local and
national (18).
The results from this study align with the personal and local level of the framework to
ensure professionals providing frontline services have a framework to guide delivery for best patient
outcomes (Personal level) and local health services provide a whole system approach to health care
(Local level). In 2013, The Kings Fund (4)
outlined steps required to effectively implement the House
of Care Framework. Of particular interest is the requirement for health professionals to reassess
how they ‘treat’ patients and being willing to give away some of the decision making role to
empower patients to take more of a collaborative role in management (18).
Anecdotally, further work
is needed within the Australian health system to enable the House of Care Framework or similar to
be effectively implemented to provide efficient patient centred care. The PAM13™ tool can be used
within the House of Care Framework to tailor health care services for patients based on their current
level of engagement.

43 | P a g e
7. RECOMMENDATIONS
There are a number of key priorities recommended by the PAM Project Team.
1. Further analysis of the data collected is indicated, including deep dives into special needs
populations and LGA specific issues that may have significance.
2. Resources should be put towards up-skilling health professionals in the use of the PAM13TM
tool
for individual patient care. Allowing health professionals to implement specific strategies, as
outlined in the Insignia license materials (26)
, to best meet patients’ needs and current
engagement levels and continue to build their engagement as their condition changes.
3. Examination of the cost of services to both the system and the individual to determine whether
the implementation of a health activation measure, such as PAM13TM
, could provide costs
savings by reducing the demands on the health system through increased self-management of
conditions.
4. Validation of PAM13TM
in local priority languages to target the non-English speaking community.
5. Development and implementation of an overarching Health Literacy Strategy to address
identified issues with results to be assessed against the baseline data.
6. Assess the populations’ health literacy levels to ensure that they understand the information
provided to them by health professionals in the management of their condition.
7. Practical application at the primary health care level to assist primary health care professionals
with proactive follow-up of certain patient groups.

44 | P a g e
8. CONCLUSION
In conclusion this project has proved a success in addressing the objectives. It has achieved the
following conclusions:
1. The PAM13TM
tool was an easy to implement survey, with 97.9% of returned surveys eligible
for analysis. Of the respondents who answered our additional feedback questions, 97.6%
found the survey simple and easy to understand and completed within 5-10 minutes.
2. The population baseline has been established with an average population Activation Level of
3 for the South Eastern Sydney area. This suggests a high level of activation indicating our
population believes it has an important role to play in managing their own health care. Areas
for further intervention include:
a. Further in-depth analysis of the data to identify priority groups
b. Validation of the tool in a variety of local priority languages
c. Develop and implement a Health Literacy Strategy for SESML including identification
of a health literacy measure to complement this engagement tool.
3. This report concludes that the Patient Activation Measure (PAM13) ™ is a useful tool for
measuring patient engagement in an Australian Health Care setting.

45 | P a g e
REFERENCES
REFERENCES TO NATIONAL AND INTERNATIONAL GUIDELINES ON THE CONDUCT OF RESEARCH IN
HUMANS*
1. Australian Commission on Safety and Quality in Health Care. Health Literacy Stocktake,
Consultation Report, Sept 2012
2. Australian Commission on Safety and Quality in Health Care. Consumers, the health
system and health literacy: Taking action to improve safety and quality. Consultation
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3. Brenk-Franz K, Hibbard JH, Herrmann W, Freund T, Szecsenyi J, Djalali S, Steurer-Stey C,
Sonnichsen A, Tiesler F, Storch M, Schneider N, Gensichen J. Validation of the German
Version of the Patient Activation Measure 13 (PAM13-D) in an International Multicentre
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Conditions: Building the House of Care. The Kings Fund, 2013
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Manage their chronic conditions? The Patient 2009, Dec 2009; 2(4):257-268
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Patient Activation Measure for Mental Health, Adm Policy Ment Health, August 2009
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17. Melanie Mathews, Steps in Measuring Patient Activation, Heathcare Intelligence
Network, Thursday, January, 2010 at 1:05pm.
18. NHS, Kidney Care, Summary of the Evidence on Performance of the Patient Activation
Measure, (PAM) May 2012
19. Patel DN, Lambert EV, da Silva R, Greyling M, Nossel C, Noach A, Derman W, Gaziano T,
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21. South Carolina Hospital Association, Best Practice Report: The Patient Activation
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23. http://www.insigniahealth.com/solutions/patient-activation-measure)
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25. (http://www.surveysystem.com/sscalc.htm)
26. Patient Activation Measure (pam) 13™: Guide to Measurements, License Materials,
© Insignia Health, LLC 2013 –

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