Chapter 16Culture Change in Long-Term CareLearning O

Chapter 16
Culture Change in
Long-Term Care
Learning Objectives
1. Understand the nature of culture change
2. Identify the benefits of culture change
3. Understand the role of culture change in
long-term care
4. Identify the components of culture change and
how it is implemented
5. Understand the difference between resident-centered culture
change and organizational culture change
Culture Change
Two ways in which “culture change” is used are as follows:
As it applies to long-term care consumers (particularly nursing
home residents)
As it relates to changing organizational (corporate) culture in
long-term care
What Is Culture Change?
The common name given to the national movement for the
transformation of older adult services, based on person-directed
values and practices where the voices of elders and those
working with them are considered and respected.
Benefits of Culture Change

Resident benefits:
Reduces loneliness, helplessness, and boredom
Improves physical and mental health
(e.g. reduces depression and behavioral problems)
Reduces unanticipated weight loss
Reduces mortality
Benefits of Culture Change continued
Staffing benefits:
Reduces employee turnover
Eliminates temporary agency staffing
and mandatory overtime
Reduces workers’ compensation
claims/costs
Benefits of Culture Change continued..
Additional benefits:
Significantly improves employee, resident, and family
satisfaction
Increases involvement with the outside community including
children, students, clubs, and religious organizations
Culture Change Programs
The Eden Alternative
The Wellspring Model
The Green House Project
The Pioneer Network
Components of Culture Change
Decision making
Leadership

Staff roles
The physical environment
Organizational design
Other Aspects of Culture Change
Creating a sense of community
Amenities
Transportation
Social media
Organizational Culture
The collection of self-sustaining patterns of behaving, feeling,
thinking, and believing; the patterns that determine how things
are done
The workplace environment formulated from the interaction of
the employees in the workplace
Characteristics of Successful Organizational Culture
1. Respect for all individuals, including employees, residents,
and visitors
2. Responsiveness to questions
3. Freedom from blame
4. Honesty
5. Respect for scientific evidence
Changing the Culture
To implement organizational cultural change:
Understand that change takes time
The organization usually needs to
provide resources

Recognize change opportunities
Role of the Leader in
Cultural Change
A leader is necessary:
To motivate team members
To be a visible role model
To explain what is acceptable
and desired
Summary
There are two ways in which culture change is used in long-
term care:
As it applies to long-term care consumers
As it relates to changing organizational (corporate) culture
Both have been recognized as critical to success for a long-term
care provider.
Contents lists available at ScienceDirect
Research in Autism Spectrum Disorders
journal homepage: www.elsevier.com/locate/rasd
A systematic review of factors related to parents’ treatment
decisions for their children with autism spectrum disorders
Meghan Wilson⁎ , David Hamilton, Thomas Whelan, Pamela
Pilkington
School of Psychology, Faculty of Health Sciences, Australian

Catholic University, 115 Victoria Parade, Fitzroy VIC 3065,
Australia
A R T I C L E I N F O
Number of reviews completed is 2
Keywords:
Autism spectrum disorder
ASD
Treatment decisions
Parents
Systematic review
A B S T R A C T
Background: There are many treatment options for children with
Autism Spectrum Disorder
(ASD). Misinformation and easy access to ineffective
treatments complicates the decision-making
process for parents. Research on implicit factors (e.g., parent or
child characteristics) and de-
clared factors (e.g., parent-reported reasons) contributes to an
understanding of what influences
these decisions.
Method: The aim of this systematic review was to examine the
significance of factors associated
with treatment selection. The review was conducted in
accordance with the PRISMA protocol.
Results: The search revealed 51 studies which contained data on
implicit and/or declared factors
associated with treatment selection. The data were tabulated by
factor and synthesised. The
severity of a child’s behavioural problems, parental stress, and
parent beliefs about ASD were
consistently identified as implicit factors associated with the

use of particular treatments. A wide
range of reasons for treatment choices were declared by parent
respondents, including; the in-
dividual needs of their child, recommendations from others,
practical reasons (e.g., cost), child
age, hope for recovery, hope for improvement, and concerns
about side-effects.
Conclusion: A better understanding of these factors will inform
targeted educational approaches
which encourage evidence-based practice and a more informed
view of treatments not yet sup-
ported by research.
1. Introduction
Following a diagnosis of Autism Spectrum Disorder (ASD),
parents are encouraged to access an intervention for their child.
This
can be challenging given that there are many options. Green et
al. (2006) identified 111 different treatments for ASD. The list
included a wide range of options such as dietary interventions
(e.g., restricted diets or vitamin supplements), other alternative
therapies (e.g., detoxification treatments), educational or
clinical approaches (e.g., Applied Behaviour Analysis programs
or speech
therapy), and combined programs (e.g., Floor Time). The
commitment of resources (e.g., time or cost) and ease of
implementation
can vary greatly between approaches (Green, 2007). The
selection of interventions is further complicated in that it is
common for
professionals to recommend treatments that are not evidence-
based (Miller, Schreck, Mulick, & Butter, 2012) and the internet
provides a forum for misinformation (Matson, Adams, Williams,
& Rieske, 2013). Not surprisingly, choosing treatments can be
overwhelming for parents. Exploring the reasons treatments are

chosen is a worthwhile step in understanding the scope of this
problem and developing meaningful strategies to assist with
choice making. Therefore, the present review aimed to identify
and
understand the significance of factors associated with the
selection of ASD treatments.
https://doi.org/10.1016/j.rasd.2018.01.004
Received 28 July 2017; Received in revised form 18 December
2017; Accepted 9 January 2018
⁎ Corresponding author.
E-mail address: [email protected] (M. Wilson).
Research in Autism Spectrum Disorders 48 (2018) 17–35
Available online 03 February 2018
1750-9467/ © 2018 Elsevier Ltd. All rights reserved.
T
http://www.sciencedirect.com/science/journal/17509467
https://www.elsevier.com/locate/rasd
mailto:[email protected]
http://crossmark.crossref.org/dialog/?doi=10.1016/j.rasd.2018.0
1.004&domain=pdf
Intervention research has largely focussed on programs based on
behavioural principles (e.g., ABA programs) or educational
approaches (e.g., Treatment and Education of Autistic and
Related Communication Handicapped Children) (Myers &
Johnson, 2007).

Such programs are implemented to teach new skills and address
maladaptive behaviours. Behavioural interventions are
supported by
the best available evidence (Anagnostou et al., 2014; Myers &
Johnson, 2007). Along with traditional intensive behavioural
inter-
ventions, there is emerging evidence for variations to these
approaches, for example, developmental, play-based, or social
skills
interventions (Weitlauf et al., 2014). Yet, evidence-based
treatments do not result in equal gains for every child, progress
can be slow,
and there is no expectation of a cure (Myers & Johnson, 2007).
The high prevalence of comorbidity in children with ASD (e.g.,
ADHD or intellectual disability) adds to the difficulty of
choosing
an appropriate intervention (Matson & Williams, 2015). Some
common approaches used for children with ASD (e.g., restricted
diets
or drug treatments), may be warranted for comorbid problems,
but are not currently recommended to treat the core features of
ASD
(National Institute for Health and Care Excellence, 2013).
Treatments outside of the realm of conventional practice
(known as complementary and alternative medicine, CAM)
continue to
be used (Matson et al., 2013; Whitehouse, 2013). In addition,
parents often access multiple treatments simultaneously. For
example,
Smith and Antolovich (2000) found that, of 121 children
engaged in ABA therapy, parents reported accessing an average
of seven
additional treatments. Commonly used CAM treatments in the
paediatric ASD population are the use of vitamins (e.g., vitamin

B6/
Magnesium) and restrictive diets (e.g., a gluten-free/casein-free
diet) (Levy & Hyman, 2008; Whitehouse, 2013). Other
examples are
detoxification treatments, mind-body practices, hyperbaric
oxygen therapy and sensory integration therapies (Levy &
Hyman, 2008;
Whitehouse, 2013). CAM practices may be ineffective or pose
unnecessary risks (e.g., nutritional imbalances) (Levy &
Hyman, 2008;
Whitehouse, 2013). Other concerns about using CAM include
high financial costs and missing out on treatments supported by
research (Matson et al., 2013).
It appears that the research evidence guiding professional
practice is often not the driving force behind parent decisions
(Matson
& Williams, 2015). Indeed, many factors have been
hypothesised to influence parents’ decisions about treatments.
Implicit factors are
those characteristics associated with the use of treatments, but
not necessarily cited by parents as a reason for choosing a
treatment.
Parent demographics (e.g., education or age), child
characteristics (e.g., age, gender or ASD severity), and family
demographics (e.g.,
income or ethnicity) are examples of implicit factors that have
been explored (Matson & Williams, 2015). Declared factors are
reasons
or influences that parents cite regarding their intervention
choices. A systematic review of 16 studies (Carlon, Carter, &
Stephenson,
2013) examined factors parents declared to have influenced
treatment choices for their child with ASD. Recommendations
(by health
professionals or others) was the most cited reason for choosing

a treatment. Other frequently declared factors included practical
reasons (e.g., availability, accessibility, cost, time constraints,
funding, and availability of other interventions), perception of
pro-
gress, use and perceived effectiveness of other interventions,
needs of the child, research evidence, child’s resistance, side
effects, and
compatibility with other interventions (Carlon et al., 2013).
In a recent discussion paper, Matson and Williams (2015)
identified concerns about the process of ASD treatment
selection and
highlighted the importance of researching parent decision-
making. Both implicit and declared factors contribute to a
complete
understanding of why treatments are selected (Carlon et al.,
2013). To date, there has been no systematic review
incorporating both
implicit and declared findings.
Knowledge of the relationship between implicit factors and
treatment use may be useful in understanding the context in
which
parents choose treatments. If groups with specific
characteristics are likely to choose particular treatments, this
information could
inform the development of targeted educational strategies. In
some instances, factors that influence decision-making (e.g.,
beliefs
about ASD) may be modifiable. Equally, the explanations
provided by parents are key to understanding what is important
or not
important to their decision-making. The present systematic
review of the literature was not limited to specific study
designs. It aimed
to synthesise (a) the implicit factors (e.g., child or family

characteristics) significantly associated with the use of any
treatment
reported by parents for their children with ASD and (b) the
reasons reported by parents of children with ASD to influence
or explain
their decision to use any treatment.
2. Method
A systematic search of the literature was conducted in
accordance with the PRISMA guidelines (Moher, Liberati,
Tetzlaff, &
Altman, 2009). The review protocol was registered on the
PROSPERO International prospective register of systematic
reviews (Regis-
tration number: CRD42016033955).
2.1. Inclusion and exclusion criteria
Included studies reported on factors associated with the use of
treatments or declared reasons for selecting treatments for
children
with ASD. Included studies met the following criteria.
(a) Studies were published after 1993. This timeframe was
selected to target studies where children were more likely to
have been
diagnosed under recent criteria and a similar range of treatments
would have been available.
(b) Respondents were mothers, fathers, or the child's primary
caregivers.
(c) Children reported on in the studies had a primary diagnosis
of ASD (as indicated by the mother, father, or primary
caregivers or

independently confirmed). A study was excluded if it was
specified that criteria prior to the Diagnostic and Statistical
Manual of
Mental Disorders, Fourth Edition (DSM-IV) were used (i.e.,
DSM-III) or if it was not clear that a sample or sub-sample of
the children
M. Wilson et al. Research in Autism Spectrum Disorders 48
(2018) 17–35
18
had a diagnosis of ASD. Comorbid conditions (e.g., intellectual
disability or ADHD) often occur with ASD, thus it was expected
that some children would have secondary diagnoses.
(d) Studies on services that are not intervention or treatment
types (e.g., respite or recreational activities) or not chosen by
parents
(e.g., exclusively school-based interventions) were excluded.
(e) Review or discussion papers, meta-analyses, conference
papers, case studies, and dissertations were excluded.
(f) Studies on declared factors included in a review by Carlon et
al. (2013) were excluded. Given that the review had similar
inclusion criteria to the current review, these studies were
excluded to avoid replication.
2.2. Search strategy
A systematic search of the databases Medline, CINAHL,
PsychINFO, ERIC, Scopus and Web of Science was first
conducted in May

of 2016 and repeated in December 2016. The search terms used
were; (autis* or ASD or asperger*) AND (mother* or father* or
parent* or family or families) AND (treat* or intervention* or
therap*) AND (decision* or selection or choice or choose). The
same
strategy was used in each database. Relevant subject headings
(MESH terms) were used in Medline, CINAHL and PsycINFO
databases.
Additional studies were identified through hand-searching the
references and a forward citation search. The search strategy for
Medline is included as Appendix A.
2.3. Quality assessment
All included studies were assessed for quality using the
Standard Quality Assessment Criteria for Evaluating Primary
Research Papers
(Kmet, Lee, & Cook, 2004). A quality checklist for each
included study was completed by the first author (MW).
Checklists for 35%
(18 studies; 14 quantitative and 4 qualitative) of the studies
were completed by the last author (PP) to ensure accuracy. The
studies
for double rating were randomly selected using the random
number generator function in Microsoft Excel. The initial inter -
rater
agreement was calculated by dividing agreed item scores by the
total scores and multipl ying by 100. Agreement was 85% for
quantitative studies and 75% for qualitative studies. Any
discrepancies in ratings were resolved through discussion and
re-checking
the papers in question.
2.4. Data extraction and synthesis
Data were extracted on study characteristics: publication year,

design, data source, methodology, treatment type investigated,
sample size, age of the children, and key findings. Data
extraction was completed by the first author (MW), and 50%
(26 papers) of
studies were coded by the fourth author (PP) to ensure accuracy.
The initial inter-rater agreement was calculated by dividing the
number of agreed studies by the total number of studies checked
and multiplying by 100. The agreement was 80.8%.
Discrepancies in
data extraction for five papers were resolved through discussion
and re-checking the papers.
2.4.1. Implicit factors
Studies were examined to identify all factors (e.g., child, parent
or family characteristics) that were investigated for associations
with treatment use. Factors were tabulated according to
frequency (i.e., number of papers in which they appeared). Any
factors which
appeared in fewer than three studies were listed, but the results
were not extracted. Across studies, 20 implicit factors were
identified.
Statistics with p < .05 were considered significant. For studies
which presented more than one statistical analysis, the main
analysis relevant to the factor was selected for the synthesis. If
parents of children with ASD were a subsample, only data
relevant to
the subsample were extracted. When findings were included on
services that are not clearly interventions or treatments (e.g.,
respite
services, recreational activities, or title of professional) data
were not extracted on these services. Data on school-based
services or
specific classes of medication were not extracted, since these
treatments are not clearly chosen by parents. Data on use of any

medication (in general) were extracted. The synthesis involved
observing trends and providing a narrative overview of the sig-
nificance of the associations between each factor and treatment
use. Appendix B presents an overview by study of the p-values
and
odds ratios (where applicable) for each implicit factor.
2.4.2. Declared factors
Declared factors were identified by tabulating reasons or
influences for treatment choice cited by parents. For qualitative
studies,
this was achieved by listing the key themes identified by
authors. For survey studies, key themes or percentages relating
to declared
reasons were extracted. The most common reasons declared by
parents across studies were presented in a narrative synthesis.
3. Results
3.1. Search results
The database search produced 1167 records. A further 475
records were identified through forward citation searching and
hand-
searching of references. With duplicates removed, 1034 records
remained. Titles and abstracts were screened for eligibility by
the
first author (MW) and 25% of abstracts were screened by the
third author (TW). The agreement between screeners was
92.4%.
Discrepancies were resolved by checking the papers in question.
The full texts of 147 studies were checked for eligibility by the
first

(2018) 17–35
19
author (MW) and 96 were excluded. Therefore, a total of 51
studies were included in the review. Fig. 1 presents a PRISMA
flow chart
summarising the phases of the search. Included studies are
denoted in the reference list by an asterisk after the title.
3.2. Quality assessment
The Standard Quality Assessment Criteria for Evaluating
Primary Research Papers consists of items designed to measure
research
quality. The scorer assigns a 2 (yes), 1 (partial) or 0 (no) for
each item. A summary score for each study is calculated by
totalling the
item scores and dividing by the total possible score. Possible
scores range from 0 to 1, with higher scores indicating higher
meth-
odological quality. Due to the exploratory nature of review, the
assessment was completed to provide an overall indication of
strengths and weaknesses in the literature and to identify
quality issues that could be considered in future investigations.
Exceeding a
quality threshold or cut-off score was not a requirement for
inclusion in the current review.
Records identified through
database searching
(n = 1167)

S
cr
ee
ni
ng
In
cl
ud
ed
E
lig
ib
ili
ty
Id
en
tif
ic
at
io
n
Additional records identified through
other sources
(n = 475)
Records after duplicates removed
(n = 1034)
Records screened
(n = 1034)

Records excluded
(n = 887)
Full-text articles assessed
for eligibility
(n = 147)
Full-text articles excluded,
with reasons
(n = 96)
No relevant factor or treatment
(n = 39)
Review or dissertation
(n = 26)
No parent respondents
(n = 12)
ASD not primary diagnosis
(n = 10)
Studies included in prior review
(n = 7)
Not in English
(n = 2)
Studies included in
qualitative synthesis
(n = 51)
Fig. 1. PRISMA flow-chart summary of search strategy and
results.
Table 1
Characteristics of included studies (n = 51).

Study characteristics Number of studies (%)
Mean age of children with ASD Under 5 years 11 (21.6)
5–12 years 22 (43.1)
13–15 years 2 (3.9)
Sample 18 or younger* 13 (25.5)
Not specified 3 (5.9)
Sample size (N) < 50 8 (15.7)
50–249 20 (39.2)
250–499 10 (19.6)
> 500 13 (25.5)
Treatment type investigated Conventional**/CAM*** 24 (47.1)
CAM 16 (31.4)
Conventional 5 (9.8)
Medications 4 (7.8)
Communication interventions 2 (3.9)
* Mean age not reported.
** Educational or behavioural therapies (including speech
therapy and occupational therapy).
*** Treatment approaches other than educational or behavioural
therapies.
(2018) 17–35
20
For the present review summary scores for quantitative studies
(n = 45) ranged between 0.67 and 1.00. Most studies adequately
specified an objective and design. In 67.7% of studies, sampling

procedures were not well defined or were likely to have
introduced
bias (e.g., convenience sampling). Participant characteristics
were well described in most studies. In 35.6% of studies the
outcome
measures were not well described (e.g., the categorisation of
treatments was unclear). The majority of studies adequately
reported the
results and conclusions. Summary scores for qualitative papers
(n = 6) ranged between 0.60 and 0.85. In most of these studies
the
research question and design were well described. Data
collection procedures, analysis and use of verification strategies
were suf-
ficient for most studies. The majority of studies were rated less
than adequate for items regarding sampling procedures,
reflexivity of
the account, and clarity of the conclusions. Appendix C
provides the obtained quality summary scores for each study.
3.3. Description of included studies
All studies used a survey or interview to obtain information on
treatment use from parents. Nine studies (18%) were based on
retrospective survey or interview data. The search was limited
to studies published after 1993, however, all of those included
were
published after 1999. A summary of included studies is
provided in Table 1.
3.4. Implicit factors
3.4.1. Child factors
3.4.1.1. Age. Of the 25 studies which included child age as a
variable, 13 (Alnemary, Aldhalaan, Simon-Cereijido, &
Alnemary, 2017;

Bowker, D’Angelo, Hicks, & Wells, 2011; Goin-Kochel, Myers,
& Mackintosh, 2007; Memari, Ziaee, Beygi, Moshayedi, &
Mirfazeli,
2012; Mire, Gealy, Kubiszyn, Burridge, & Goin-Kochel, 2015 ;
Mire, Nowell, Kubiszyn, & Goin-Kochel, 2014; Mire, Raff,
Brewton, &
Goin-Kochel, 2015; Owen-Smith et al., 2015; Pringle, Colpe,
Blumberg, Avila, & Kogan, 2012; Rosenberg et al., 2010;
Salomone et al.,
Table 2
Summary of findings on the relationship between child
characteristics and treatment use.
Child characteristic No. of studies Findings
Age 25 Mixed results
Gender 17 NS* related to treatment use**
Diagnostic subtypes 11 Mixed results
ASD severity 11 Mixed results
Comorbidity 10 Mixed results
Cognitive/adaptive behaviour 8 Mixed results
Child medication use 5 Mixed results
Time since diagnosis 5 Mixed results
Age at diagnosis 4 Mixed results
Challenging behaviour 3 Scores indicating challenging
behaviour were associated with the use of CAM treatments
* NS = not significant.
** One study reported that girls were more likely to use mind-
body treatments.
Table 3
Summary of findings on the relationship between parent

Parent characteristic No. of studies Findings
Education level 23 Mixed results
Age 7 NS* associated with treatment use
ASD beliefs 5 Associated with treatment use
Marital status 4 Mixed results
Stress 3 Associated with treatment use
* NS = not significant.
Table 4
Summary of findings on the relationship between family
Family characteristic No. of studies Findings
Ethnicity 14 Mixed results
Income 11 Mixed results
Location 4 Mixed results
Family size 3 NS associated with treatment use*
Family member with ASD 3 NS associated with treatment use
* In one study, family size was associated with CAM when
“spiritual healing” was later excluded from the analysis.
(2018) 17–35
21
2016; Thomas, Ellis, McLaurin, Daniels, Morrissey, 2007;
Witwer & Lecavalier, 2005) reported at least one significant

association
between age and treatment use. There were two trends which
emerged across a number of these studies; older children were
more
likely to use drug treatments (Goin-Kochel et al., 2007; Memari
et al., 2012; Mire et al., 2014; Mire, Raff et al., 2015;
Rosenberg et al.,
2010; Thomas, Ellis et al., 2007; Witwer & Lecavalier, 2005),
and younger children were more likely to use behavioural or
conventional interventions (Bowker et al., 2011; Goin-Kochel et
al., 2007; Mire, Raff et al., 2015; Pringle et al., 2012; Salomone
et al.,
2016).
Another nine studies which focussed specifically on CAM
(Bilgiç et al., 2013; Granich, Hunt, Ravine, Wray, &
Whitehouse, 2014;
Hanson et al., 2007; Levy, Mandell, Merhar, Ittenbach, & Pinto-
Martin, 2003; McIntyre & Barton, 2010; Salomone, Charman,
McConachie, & Warreyn, 2015; Winburn et al., 2014; Wong &
Smith, 2006; Wong, 2009) reported no significant associations
with
child age. A further three studies (Dardennes et al., 2011; Irvin,
McBee, Boyd, Hume, & Odom, 2012; Miller et al., 2012)
reported no
association between child age and any type of treatment,
including both conventional and CAM interventions.
3.4.1.2. Gender. Gender was not associated with treatment use
across 16 studies (Alnemary et al., 2017; Bilgiç et al ., 2013;
Granich
et al., 2014; Hanson et al., 2007; Irvin et al., 2012; Levy et al.,
2003; Memari et al., 2012; Owen-Smith et al., 2015; Patten,
Baranek,
Watson, & Schultz, 2013; Perrin et al., 2012; Rosenberg et al.,
2010; Salomone et al., 2016; Valicenti-McDermott et al., 2014;

Witwer
& Lecavalier, 2005; Wong & Smith, 2006; Wong, 2009)
investigating CAM, conventional or both. As an exception,
Salomone et al.
(2015) found that girls were more likely than boys to use mind-
body practices (e.g., sensory integration therapy, auditory
integration
training, or massage). The authors noted that this finding should
be interpreted with caution given that girls constituted a
minority of
the sample.
3.4.1.3. Diagnostic subtypes. The DSM-IV conceptualised
different subtypes of ASD (i.e., autism, Aspergers and PDD-
NOS). These
subtypes are sometimes used as a proxy for the severity of the
ASD traits. In eight studies (Bowker et al., 2011; Christon,
Mackintosh,
& Myers, 2010; Goin-Kochel et al., 2007; Green et al., 2006;
Hanson et al., 2007; Perrin et al., 2012; Rosenberg et al., 2010;
Thomas,
Ellis et al., 2007) the use of particular treatments was
associated with diagnostic category. A pattern emerged in four
studies (Goin-
Kochel et al., 2007; Green et al., 2006; Perrin et al., 2012;
Thomas, Ellis et al., 2007) which all found that children with
Asperger’s
were less likely to have tried special diets, relative to children
with autism. Another three studies (Bilgiç et al., 2013; Granich
et al.,
2014; Owen-Smith et al., 2015), which examined CAM use,
found no association between diagnostic subtype and CAM.
3.4.1.4. ASD severity. In one study (Horovitz, Matson, &
Barker, 2012) it was reported that a group of children using
psychotropic

medications had higher scores on a measure of ASD severity
(i.e., the Baby and Infant Screen for Children with Autism
Traits – BISCUIT,
Part 1). In two studies (Christon et al., 2010; Hall & Riccio,
2012) it was found that use of CAM treatments was more
frequent among
children with higher ASD severity, measured by parents’ report
of severity. The remaining eight studies which reported on this
factor
(Alnemary et al., 2017; Dardennes et al., 2011; Granich et al.,
2014; Irvin et al., 2012; McIntyre & Barton, 2010; Memari et
al., 2012;
Patten et al., 2013; Pickard & Ingersoll, 2015) reported no
association between ASD severity and treatment use (CAM or
conventional).
3.4.1.5. Comorbidity. The presence of comorbid conditions,
such as intellectual disability, ADHD, anxiety, depression,
allergies or
epilepsy, were examined for a relationship with treatment use in
ten studies. Some studies reported significant associations
between
comorbidities and psychotropic medication use (Rosenberg et
al., 2010; Zablotsky et al., 2015) or other treatments including
CAM
(Levy et al., 2003; Perrin et al., 2012; Thomas, Ellis et al.,
2007; Valicenti-McDermott et al., 2014; Zablotsky et al., 2015).
Both
studies which examined medications reported that use was more
likely when comorbidities were present. In other studies no
association was found between comorbidities and CAM
(Harrington, Rosen, Garnecho, & Patrick, 2006; Memari et al.,
2012; Wong,
2009), or treatments in general (Alnemary et al., 2017).
3.4.1.6. Cognitive and adaptive behaviour. Scores on cognitive

measures (e.g., Mullen Scales of Early Learning) or adaptive
behaviour
measures (e.g., Vineland Adaptive Behaviour Scales) were
explored for associations with treatment use in eight studies.
Three (Mire,
Gealy et al., 2015; Mire et al., 2014; Witwer & Lecavalier,
2005) reported that treatment use was associated with cognitive
or
adaptive behaviour scores. One study found that lower scores on
a cognitive scale was associated with the use of medication
(Mire
et al., 2014). Another reported that children with higher
adaptive behaviour scores were less likely to use modified diets
(Witwer &
Lecavalier, 2005). Higher scores on a verbal cognitive scale
were associated with the use of intensive behavioural therapy
(Mire,
Gealy et al., 2015). Other studies found that scores on cognitive
or adaptive behaviour scales were not related to CAM use
(Akins,
Krakowiak, Angkustsiri, Hertz-Picciotto, & Hansen, 2014;
McIntyre & Barton, 2010), private speech or occupational
therapy (Irvin
et al., 2012), or treatments in general (Carter et al., 2011; Patten
et al., 2013).
3.4.1.7. Child medication use. Child medication use was
associated with CAM use in four studies (Granich et al., 2014;
Owen-Smith et al.,
2015; Perrin et al., 2012; Salomone et al., 2015). In three of
these investigations those taking prescription medications were
more likely to use
other CAM treatments, alternatively Perrin et al. (2012)
reported that children taking prescription medications had a
lower use of special
diets. Another study, Valicenti-McDermott et al. (2014),

reported that CAM use was not related to medication use.
3.4.1.8. Time since diagnosis. In two studies (Hanson et al.,
2007; Salomone et al., 2016) an association between time since
diagnosis
and treatment use was reported. Hanson et al. (2007) found the
likelihood of CAM use increasing with time since diagnosis.
Salomone
(2018) 17–35
22
et al. (2016) found that time since diagnosis predicted the use of
behavioural, developmental, relationship-based and speech
intervention. Two studies (Bilgiç et al., 2013; Valicenti -
McDermott et al., 2014) reported no association between time
since diagnosis
and the use of CAM. Another investigation (Miller et al., 2012)
reported no association between time since diagnosis and
empirically
supported treatments.
3.4.1.9. Age at diagnosis. In one study (Zuckerman, Lindly, &
Chavez, 2017) it was reported that the use of a behavioural
intervention
was less likely and psychotropic medication was more likely
amongst children diagnosed later in childhood (relative to those
diagnosed early in childhood). Across three other studies,
child’s age at diagnosis was not found to be associated with
CAM use
(Granich et al., 2014; Valicenti-McDermott et al., 2014) or
treatment type in general (Alnemary et al., 2017).

3.4.1.10. Challenging behaviour. Scores on children’s behaviour
scales were reported to be associated with a higher use of CAM
treatments across three studies. Witwer and Lecavalier (2005)
adopted the Nisonger Child Behaviour Rating Form, NCBRF
(Aman,
Tassé, Rojahn, & Hammer, 1996) and found that lower scores
on the compliant/calm subscale and higher scores on the
hyperactivity
subscale were predictive of the use of psychotropic medication.
No association was found between NCBRF scores and vitamins
or
supplement use. Perrin et al. (2012) found that higher total
scores on the Child Behaviour Checklist (Achenbach &
Rescorla, 2000)
were associated with the use of CAM treatments. Valicenti -
McDermott et al. (2014) reported that higher scores on the
Aberrant
Behaviour Checklist (Aman, Singh, Stewart, & Field, 1985)
were associated with the use of CAM treatments. Table 2
summarises
findings on child characteristics and treatment use.
3.4.2. Parent factors
3.4.2.1. Education level. In eight studies which focused on
CAM, it was reported that children’s use was higher when
parents had a
higher level of education (Akins et al., 2014; Bilgiç et al., 2013;
Hall & Riccio, 2012; Hanson et al., 2007; Owen-Smith et al.,
2015;
Patten et al., 2013; Salomone et al., 2015; Wong & Smith,
2006). In another three studies (Alnemary et al., 2017;
Salomone et al.,
2016; Thomas, Ellis et al., 2007) other associations were found
between years of education and the use of specific treatments
(e.g.,

one investigation reported that the use of a picture exchange
system and hippotherapy was more likely when parents had a
college
education). In twelve studies (Al Anbar, Dardennes, Prado-
Netto, Kaye, & Contejean, 2010; Dardennes et al., 2011;
Granich et al.,
2014; Harrington et al., 2006; McIntyre & Barton, 2010;
Memari et al., 2012; Miller et al., 2012; Pickard & Ingersoll,
2015; Rosenberg
et al., 2010; Valicenti-McDermott et al., 2014; Wong, 2009;
Zuckerman, Lindly, Sinche, & Nicolaidis, 2015) there was no
association
between treatment use (CAM or conventional) and parent
education level.
3.4.2.2. Age. Parent age was not associated with the use of
conventional or CAM treatments across seven studies (Al Anbar
et al.,
2010; Alnemary et al., 2017; Dardennes et al., 2011; Miller et
al., 2012; Valicenti-McDermott et al., 2014; Wong & Smith,
2006;
Wong, 2009).
3.4.2.3. ASD beliefs. The Revised Illness Perception
Questionnaire – Modified for Autism (IPQ-RA) was used to
measure health beliefs
about ASD in three studies (Al Anbar et al., 2010; Mire, Gealy
et al., 2015; Zuckerman et al., 2015) and another two
investigations
(Bilgiç et al., 2013; Dardennes et al., 2011) enquired about
parents’ beliefs regarding ASD aetiology. Three of these studies
(Al Anbar
et al., 2010; Bilgiç et al., 2013; Dardennes et al., 2011) found
that some specific causal beliefs were related to the treatments
that
parents chose. For example, Bilgiç et al. (2013) found that

genetic or congenital causal beliefs were related to a lower ra te
of CAM use
and immunisation causal beliefs were related to more frequent
CAM use. Three of the studies (Al Anbar et al., 2010; Mire,
Gealy et al.,
2015; Zuckerman et al., 2015) reported significant associations
between other beliefs about ASD and treatment use. For
example,
Zuckerman et al. (2015) indicated that parents who considered
ASD to be a lifelong condition were more likely to use
psychotropic
medications, while Mire, Gealy et al. (2015) found that parents
who considered ASD to be a lifelong condition were less likely
to use
speech therapy as an intervention.
3.4.2.4. Marital status. In one study which focused on CAM, it
was reported that parents who were married were more likely to
access
CAM for their children with ASD (Hall & Riccio, 2012).
Another study (Owen-Smith et al., 2015) found a bivariate
association
between married parents and CAM use. Other studies found that
parental marital status was not related to psychotropic
medication
use (Memari et al., 2012) or the uptake of the EarlyBird
intervention program (Birkin, Anderson, Seymour, & Moore,
2008).
3.4.2.5. Stress. Parental stress has been measured with the
Parenting Stress Index (Abidin, 1995) or the Questionnaire for
Resources and
Stress (Friedrich et al., 1983). Valicenti-McDermott et al.
(2014) reported that higher levels parent stress were associated
with a
greater use of CAM. Similarly, Thomas, Ellis et al. (2007)

found that higher parent stress was associated with the use of
medication
and supplements, the Picture Exchange Communication System
(PECS) and hippotherapy. Irvin et al. (2012) found that parents
with
higher stress were more likely to utilise private occupational
therapy for their child. Table 3 summarises the findings on the
relationship between parent characteristics and treatment use.
3.4.3. Family factors
3.4.3.1. Ethnic background. Analyses regarding ethnicity
typically investigated differences in treatment use between
those of
Caucasian, Hispanic, and African American family
backgrounds. Studies reported associations between ethnicity
and CAM (Akins
et al., 2014; Levy et al., 2003; Valicenti-McDermott et al.,
2014), psychotropic medication (Rosenberg et al., 2010;
Zuckerman et al.,
(2018) 17–35
23
2015) and other interventions (Birkin et al., 2008; Thomas, Ellis
et al., 2007). In most investigations, children from minority
groups
were less likely to use CAM and other treatments. As an
exception, Levy et al. (2003) indicated that children with a
Latino
background were more likely to use CAM treatments. In another
five studies which focused on CAM treatments, ethnicity was
not

associated with CAM use (Granich et al., 2014; Hall & Riccio,
2012; Hanson et al., 2007; Harrington et al., 2006; Owen-Smith
et al.,
2015). A final two investigations (Irvin et al., 2012; Patten et
al., 2013) found no association between ethnicity and any
treatment
(CAM or conventional).
3.4.3.2. Income. In three studies (Alnemary et al., 2017; Pickard
& Ingersoll, 2015; Thomas, Ellis et al., 2007) income was
related to
treatment choice. Alnemary et al. (2017) reported that lower
income was associated with using fewer non-medical treatments
(e.g.,
ABA therapy or sensory integration therapy) and Thomas, Ellis
et al. (2007) found that higher income was related to increased
chances of accessing speech/language therapy. Pickard and
Ingersoll (2015) reported that level of income predicted the use
of
evidence-based practices. Some studies reported that income
was not associated with CAM (Granich et al., 2014; Harrington
et al.,
2006; McIntyre & Barton, 2010; Owen-Smith et al., 2015),
psychotropic medication (Memari et al., 2012) or treatment in
general
(Miller et al., 2012; Patten et al., 2013; Zuckerman et al., 2015).
3.4.3.3. Location (urban/rural). Alnemary et al. (2017) found
that those living in a major city used more non-medical
treatments.
Rosenberg et al. (2010) found that those living in larger
metropolitan areas used less psychotropic medications (not
significant in
multivariate analysis). Another two studies (Birkin et al., 2008;
Thomas, Ellis et al., 2007) found that urban or rural living was
not

associated with the use of treatment.
3.4.3.4. Family size. Family size (Bilgiç et al., 2013; Birkin et
al., 2008; Wong, 2009) was not related to the use of any
treatment
across studies.
3.4.3.5. Family member with ASD. Having a sibling or other
family member with ASD or DD (Levy et al., 2003; Valicenti -
McDermott
et al., 2014; Wong & Smith, 2006) was not associated with
treatment use. Table 4 summarises findings on family
characteristics and
treatment use.
3.4.4. Factors not frequently examined across studies
Factors which appeared in two or fewer studies were:
vaccination status of the child, parent gender, location of
treatments, ASD
knowledge, socio-economic status, knowledge of treatments,
empirical support, immediacy of outcome, cost, availability,
parent age
at child's birth, CAM characteristics, US born parents or other,
seeing another provider prior to intake, appointment wait time,
number of services received, ABA hours, service hours, school
hours, atypical behaviours, parent college major or occupation,
in-
surance type, ASD core features, age of problem onset,
classroom type, progression of ASD, number of ER visits,
sensory processing
difficulties, social networks, country median income,
identifying with a major treatment approach (e.g., ABA), and
religion. Two
studies which met inclusion criteria (Call, Delfs, Reavis, &
Mevers, 2015; Thomas, Morrissey, & McLaurin, 2007) did not

include any
of the common factors included in the synthesis.
3.5. Declared factors
There were 11 studies which reported on factors declared by
parents to influence treatment decisions for their children. In
six of
these investigations, a qualitative interview approach was used.
The other five investigations surveyed parents as part of a
larger
interview or questionnaire. In total, there were seven factors
that were reported on by three or more studies. Of these, four
factors
(recommendations, child’s individual needs, practicalities and
side effects) were also identified as main findings in the recent
review
on parent-declared factors (Carlon et al., 2013). In addition,
three new factors (hope for cure or recovery; child’s age; and
hope for
improvement) which were identified by only one or two studies
in the previous review, emerged more prominently in the current
review.
3.5.1. Child’s individual needs
Individual child’s needs were identified by parents in four
studies as an influential factor. Carlon, Carter, and Stephenson
(2015)
asked parents to rate how important a variety of factors were in
their early intervention decision-making. The particular needs
of a
child was rated as the most important in a list of provided
factors. Two qualitative investigatio ns (Finke, Drager, &
Serpentine, 2015;
Serpentine, Tarnai, Drager, & Finke, 2011) found that a child’s

need was important to choosing communication interventions.
An-
other qualitative study (Hebert, 2014) found that the individual
needs of a child influenced decisions made for treatments in
general.
3.5.2. Recommendations
Recommendations from others was reported to be important to
parents’ treatment choices in four studies. Carlon et al. (2015)
reported that advice from therapists, service providers, tea chers,
doctors, other parents and friends and relatives were all rated
important by parent participants. According to Wong (2009),
42.5% of parents took into account advice from family members
and
32.5% of parents considered the advice of medical
professionals. In two qualitative studies (Finke et al., 2015;
Grant, Rodger, &
Hoffmann, 2016), advice was revealed as a key theme.
(2018) 17–35
24
3.5.3. Practicalities (affordability, availability and accessibility)
In four studies, parents recognised the importance of the
practicalities of treatment (e.g., affordability, availability and
accessi-
bility) when making treatment decisions. Carlon et al. (2015)
found that parents rated availability, funding, cost, and
accessibility as
important factors in their early intervention decision-making. In

two qualitative studies (Hebert, 2014; Serpentine et al., 2011),
cost
was identified as a key theme. In another study (Tzanakaki et
al., 2012), 20% of parents reported that the availability of the
treatment
was a part of their decision to pursue an intensive behaviour
intervention for their child.
3.5.4. Cure or recovery
Parents indicated that the hope for a cure was influential in
their treatment decisions in four studies. According to Provenzi,
Saettini, Barello, and Borgatti (2016), 58.1% parents chose
CAM treatments hoping that they would bring about a cure for
ASD.
Similarly, Carlon et al. (2015) reported that parents rated hope
for a cure as an important factor in their early intervention
decision-
making. Two qualitative studies (Finke et al., 2015; Hebert,
2014) identified hope for a cure as a key theme.
3.5.5. Child age
In three studies child age was identified as a factor relevant to
choosing treatments. Parents in one investigation (Carlon et al.,
2015) rated child age as important to their early intervention
decision-making. Two qualitative studies (Hebert, 2014;
Serpentine
et al., 2011) identified child’s age as a key theme.
3.5.6. Hope for improvement
In three studies hope for improvement was identified as an
important factor. In one study (Carlon et al., 2015), parents
rated hope
that the intervention will work as important in their early

intervention decisions. Finke et al. (2015) identified hope for
improvement
as a key theme for choosing communication interventions in a
qualitative investigation. Tzanakaki et al. (2012) reported that
16.7%
of parents in their sample identified hope for their child as part
of their reason for pursuing an early intensive intervention
program.
3.5.7. Concerns about side effects
Concerns about the side effects of other treatments appeared in
three studies. Carlon et al. (2015) reported that parents rated
consideration of side effects as an important factor in their early
intervention decision-making. In contrast, two studies found
only a
relatively small number of parents concerned about this factor.
Wong (2009) reported that 12.5% of parents hoped that CAM
would
lower the toxicity of conventional medicine. Bilgiç et al. (2013)
indicated that only 6% of parents chose CAM treatments to
avoid the
side effects of pharmacotherapy.
3.5.8. Factors not frequently examined across studies
A number of declared factors were cited by parents in two or
fewer studies. These factors were: empowerment, confidence,
self-
reliance, resourcefulness, wanting to do anything that might
help, parenting style, parents’ intuition, parents’ personal
experiences,
preference for natural therapies, perceptions of ASD, child
enjoyment, ideas about how children learn, better outcomes,
improving
general health, relaxation, to address particular symptoms,

comorbidities, to integrate, enhancing conventional treatments,
quality of
life, choosing a familiar intervention, trial and error, staff
attributes, causal beliefs, lack of improvement with
conventional treat-
ments, program philosophy, service characteristics, ASD
specific programs, program intensity, commitment required,
specific in-
formation sources, perceived effectiveness, and compatibili ty
with other treatments. There were two studies (Edwards,
Brebner,
McCormack, & MacDougall, 2016; Granich et al., 2014) that
met inclusion criteria, but did not examine any of the
synthesised
common factors.
4. Discussion
The aim of this systematic review was to synthesise factors
associated with parents’ selected treatments for their children
with
ASD. A search of the literature identified 51 studies which
examined implicit or declared factors related to treatment
choice.
4.1. Implicit factors
There are three factors, child challenging behaviour, parental
stress, and parents’ beliefs about ASD, that were consistently
associated with treatment use. Mixed findings emerged for most
other implicit factors, making it difficult to draw conclusions
about
their role in treatment decisions.
Challenging behaviour was related to psychotropic medication
use (Witwer & Lecavalier, 2005) and the use of CAM in general

(Perrin et al., 2012; Valicenti-McDermott et al., 2014).
Interestingly, conceptually similar factors (ASD severity and
diagnostic
subtype) were not consistently associated with any treatments.
It may be that it is not the severity of ASD specific traits that
lead
parents to select alternative treatments, but instead, challenging
behaviours in general. Parents may not necessarily be targeting
core
ASD features (e.g., social-communication impairments and
repetitive behaviours) through intervention. This notion is
supported by
Granich et al. (2014) who reported that parents most often chose
CAM to treat non-core ASD symptoms (e.g., hyperactivity or
aggression) rather core ASD features. Similarly, (Green, 2007)
asked 14 parents about their child’s experience of using a
combination
of vitamin B6 and magnesium and noted that four of the parents
were mainly using the treatment for health reasons and did not
necessarily consider it a treatment for ASD.
(2018) 17–35
25
Parental stress is another factor found to be associated with a
higher likelihood of using specific treatments, including
conven-
tional and CAM (Irvin et al., 2012; Thomas, Ellis et al., 2007;
Valicenti-McDermott et al., 2014). Matson and Williams (2015)
identified that parents may feel anxiety about choosing
treatments and an urge to try anything that might be helpful.
This approach

can lead to accessing a number of treatment options
simultaneously.
Causal beliefs about ASD (Al Anbar et al., 2010; Bilgiç et al.,
2013; Dardennes et al., 2011) were found to be related to
treatment
use. Some beliefs were related to the likelihood of choosing
conventional treatments. For example, Dardennes et al. (2011)
found that
parents who endorsed early trauma as a causal factor were less
likely to use behaviour therapy and PECS. Other beliefs were
associated with CAM use, such as Bilgiç et al., (2013) who
found that the rate of CAM was lower in parents who suspected
the causal
role of genetic factors and higher for those who held
immunisation casual beliefs. Additionally, beliefs about the
course of ASD (e.g.,
belief that ASD is chronic) were found to be associated with the
choice of specific treatments (Al Anbar et al., 2010; Mire,
Gealy et al.,
(2015); Zuckerman et al., 2015). There were different
associations presented in each study and no clear pattern
emerged. Further
research is warranted to explore the influence of specific beliefs
to understand the overall impact of beliefs on decision-making.
Of note, these three factors relate to the experience of parents
(i.e., parental stress, beliefs and perceptions of their child’s
behaviour). Since parents are the primary decision-makers in
their child’s treatment, it makes sense that their experience
would be
related to their chosen treatments. In addition, these three
factors are modifiable. The potential to make positive change in
these
areas has implications for guiding parents with decision-
making. Recognising when parents are under stress and

providing appro-
priate supports might help parents to receive accurate
information about treatment options. Identifying and discussing
misconcep-
tions about ASD could lead to more informed treatment choices.
Further, discussing parents’ concerns about challenging aspects
of
their child’s behaviour may lead to a better understanding of
parents’ priorities when selecting treatments.
The findings related to child challenging behaviour, parent
stress and parents’ beliefs about ASD should be considered pre -
liminary, since these factors were only investigated in a small
number of studies (n = 3–5). It is also important to consider that
the
direction of the relationship is not established by these findings
(e.g., it could be that accessing a particular intervention results
in
higher parental stress). Nevertheless, the pattern of findings
suggests that parent perceptions are associated with treatment
choice
and play an important role in decisions.
For the majority of implicit factors (i.e., child age, diagnostic
subtype, ASD severity, comorbidities, cognitive/adaptive
behaviour,
child medication use, time since diagnosis, age at diagnosis,
parent education level, marital status, ethnicity, income and
location) the
findings were mixed. Even so, there were some factors (i.e.,
parent age, child gender, family size, and having a family
member with
ASD) that were not associated with treatment selection across
studies. Overall, these findings suggest that it is almost
impossible to
predict which families are more likely to choose CAM

treatments.
4.2. Declared factors
Across studies, seven main factors were declared by parents as
instrumental in their treatment choice. Four of the most
commonly
cited factors (i.e., recommendations, practicalities, needs of the
child, and side effects) were also identified as important in a
previous
review (Carlon et al., 2013). This indicates that these are
relatively stable factors in parent decision-making.
Child age emerged as a declared factor in the current review. In
contrast, as an implicit factor, the findings on the relationship
between child age and treatment use were mixed. This finding
suggests that parents consider their child’s age when selecting
an
intervention, but whether this consideration leads to differences
in treatment use is less clear. A trend noted among some studies
was
that families with younger children were more likely to use
conventional treatments and families with older children tended
to favour
drug treatments. It could be that parent decision-making
changes as children grow. Parents of older children may have
exhausted
certain treatment options, noticed a change in their child’s
needs, or discovered a new treatment type that seems promising.
Understanding the relationship between child age and treatment
choices warrants further investigation since it is important to
ensure
that evidence-based practices remain a priority as children grow
into adolescents and adults.
Hope for improvement and hope for a cure were cited as

common reasons for choosing treatments in the present review.
In a
previous review (Carlon et al., 2013) these factors were only
identified in one unpublished study. These factors may indicate
that
parents focus on anticipated outcomes when they choose
treatments. It appears that it would be helpful for clinicians to
explore
parent hopes during times of intervention decision-making.
Green (2007) investigated parents experience of using
treatments with
varying levels of empirical support (i.e., ABA, sensory
integration and vitamin B6-Mg), and found that expectations
varied between
treatments. For example, parents using sensory integration with
their child had hopes specifically related to improving their
child’s
sensory experience. Across all types of treatments, some parents
had specific hopes (e.g., “I wanted my child to learn to hold a
conversation”) whereas others had very general hopes (e.g., “I
wanted improvement”). When clinicians understand what
parents
hope to achieve from an intervention, they might be better able
to communicate the way that the intervention works, set goals
for
desired outcomes, and manage expectations.
Overall, the findings on declared factors in the present review
revealed that parents cited diverse reasons for choosing
treatments
and many reasons were cited in two or fewer papers. There is
scope for future research to explore what parents prioritise
when
making treatment decisions. Given the wide range of factors
considered by parents, it would be beneficial for clinicians to
discuss

treatment choice in the context of each family’s individual
situation (e.g., their resources, perceived needs, hopes and
expectations of
outcome).
(2018) 17–35
26
4.3. Limitations & strengths
Methodological limitations within studies were revealed by the
quality assessment and should be considered when interpreting
these results. In many studies, convenience samples were used.
Partly due to the use of internet survey methods in many
studies, the
diagnosis of children with ASD was often based on parent
report and not independently confirmed by researchers.
Many studies analysed broad categories of treatment type (e.g.,
CAM), rather than specific treatment modalities. The measure-
ment of outcome variables was not clear in some cases and it
may be that some measures resulted in under or over-reporting
of
treatments used. In future investigations, it would be helpful to
ask parents about the treatments their child has used and
additionally
present a list of options to review. In some instances, parents
may not recall all of the approaches that have been tried or they
may not
consider a non-clinical approach (e.g., taking vitamins) as a
“treatment”. Given that there can be overlap and confusion
pertaining to

names of ASD treatments, it is also worth ensuring that parents
have an accurate understanding of the treatment type, perhaps
by
providing a description.
There are limitations which apply to the synthesis of the current
review. Methodologies varied substantially among the included
studies. First, the definition and categorisation of treatments
varied across studies. For example, two studies (Hanson et al.,
2007;
Valicenti-McDermott et al., 2014) categorised sensory
integration therapy as a conventional treatment, due to its
general acceptance
and wide use. A second limitation is that studies varied in the
way that information about treatment use was obtained and the
way
that “treatment use” was operationalized (e.g., current use
verses ever used). Although treatments not clearly chosen by
parents (e.g.,
school-based treatments) were not included in this review, in
some studies the location of delivery was not specified. It was
also not
possible to ascertain the degree of choice parents had when
selecting treatments. Treatments may have been selected
because they
were the only ones available. As a consequence, the synthesis
was only able to explore broad trends in the available literature
and a
quantitative or meta-analysis was not possible.
Many implicit factors (e.g., child and family characteristics)
have been explored in the existing literature, however, they
have not
previously been investigated in the context of a systematic
review. The methodology used for this paper has provided an
important

contribution by ensuring that the available data on both implicit
and declared decision-making factors was located, evaluated
and
synthesised. The strength of this approach is that it has resulted
in a comprehensive examination of all factors that have been
found to
be associated with the use of a diverse range of treatments. The
breadth of information resulting from this work will be helpful
both to
support parent decision-making and to extend the related
research.
4.4. Future research
In order to understand the impact of factors with mixed findings
in relation to treatment use (e.g., parent education or child age)
it
would be useful for future systematic reviews to adopt a
narrower focus (e.g., an investigation of ASD symptom severity
and
treatment use).Further exploration of the findings of this review
could be achieved by examining the role of child challenging
behaviour, parent ASD beliefs, and stress in treatment selection.
This could involve investigation of the relative and combined
impact
of these factors on decision-making. Mediating and moderating
effects between factors could be explored to obtain more
specific
information on how these relationships function. For example,
perhaps child age is only associated with treatment choice
within a
diagnostic subtype, or the combined impact of co-morbidities
and low cognitive scores could lead to particular choices.
Models aimed
to explain the choice of particular treatments could be
hypothesised and tested. In particular, the relative impact of the

child’s
presentation (e.g., age, level of functioning) and the attributes
of the parent as the decision-maker (e.g. parent cognitions,
beliefs and
stress) could be explored.
There are many factors (both implicit and declared) that were
identified in very few studies (two or fewer) and were not
included
in the synthesis. In terms of child and family factors, two areas
that seem to be prevalent are the presentation of the ASD (e.g.,
age of
onset, observed features) and parents’ approaches to decision-
making (e.g., problem solving approach, resilience). In terms of
de-
clared factors, future investigations could identify the attributes
that parents are looking for in a service (e.g., number of hours,
staff
attributes and physical environment).
Given the lack of research evidence and possible risk, it is
unsurprising that many of the studies on parent decisions have
focussed
on CAM treatment. There are far fewer studies that examine
parent decision-making regarding conventional treatments. A
better
understanding of how parents come to choose conventional,
evidence-based interventions will be an important future
direction. This
knowledge can inform ways to encourage use of evidence-based
approaches and thus increase the numbers of children receiving
these
interventions.
4.5. Conclusion

A systematic review of the literature identified that a number of
implicit factors have been associated with parents’ treatment
choices for their children with ASD. Factors relating to the
experience of parents (i.e., child challenging behaviour,
parental stress and
beliefs about ASD) were associated with the use of particular
treatments. Mixed findings were revealed for most implici t
factors.
Many reasons were identified by parents for their treatment
choices including, child’s individual needs, recommendations,
practi-
calities of accessing treatment, child age, hope for cure, hope
for improvement, and concerns about sideeffects. Knowledge of
both
implicit and declared factors is important to understanding
treatment choice and has implications for educational
approaches to
support parents with this complex decision-making process.
(2018) 17–35
27
Conflict of interest
None declared.
Acknowledgments
The first author (M. Wilson) received an Australian Government
Research Training Program Scholarship. The funding source had
no role in the study design, analysis or interpretation of data.

Appendix A
Medline search strategy
# Query
S16 S11 AND S14 (limit results 1994–2016)
S15 S11 AND S14
S14 S12 OR S13
S13 TI (decision* OR selection OR choice OR choose) OR AB
(decision* OR selection OR choice OR choose)
S12 (MH “Decision Making”) OR (MH “Choice Behavior”)
S11 S7 AND S10
S10 S8 OR S9
S9 TI (treat* OR intervention* OR therap*) OR AB (treat* OR
intervention* OR therap*)
S8 (MH “Early Intervention (Education)")
S7 S3 AND S6
S6 S4 OR S5
S5 TI (mother* OR father* OR parent* OR family OR families)
OR AB (mother* OR father* OR parent* OR family OR
families)
S4 (MH “Parents”) OR (MH “Single Parent”) OR (MH “Single-
Parent Family”) OR (MH “Family”) OR (MH “Mothers”) OR
(MH
“Fathers”)
S3 S1 OR S2
S2 TI (autis* OR ASD OR asperger*) OR AB (autis* OR ASD
OR asperger*)
S1 (MH “Autism Spectrum Disorder”) OR (MH “Autistic
Disorder”) OR (MH “Asperger Syndrome”)
Appendix B
See Table B1

Table B1
Key findings of included studies which reported on implicit
factors (n = 41).
Study N Age in years, mean (SD) Key findings by study
Akins et al. (2014) 453^ 3.8 (0.82) Parent education: College
degree – increased CAM, relative to parents
without a degree (indicated in text only; statistic for total
ASD/DD
sample).
Ethnicity: Hispanic ethnicity – lower CAM use, relative to those
not of
Hispanic ethnicity (indicated in text only; statistic for ASD/DD
sample).
NS: Cognitive/adaptive behaviour.
Al Anbar et al. (2010) 89 13.11 (IC 95% = 11.04–15.19) ASD
beliefs: Higher beliefs in the seriousness of the disorder –
increased
odds of educative treatments (OR = 1.28**); higher beliefs in
cyclic
timeline – increased odds of drug treatments (OR = 1.27*);
higher
beliefs in personal control – lower odds of metabolic treatments
(OR = 0.72**), special diets (OR = 0.83*), vitamins (OR =
0.77*), &
drug treatments (OR = 0.81*); higher negative perceptions –
lower odds
of using PECs (OR = 0.84*) & educative treatments (OR =
0.84*);
environmental attributions – lower odds of educative treatments
(OR = 0.83**) & increased odds of metabolic treatments (OR =
1.38***),
vitamins (OR = 1.33**), & special diets (OR = 1.33**);

hereditary
attributions – increased odds of metabolic treatments (OR =
1.50*) &
vitamin supplements (OR = 1.62**).
NS: parent education, parent age.
(continued on next page)
(2018) 17–35
28
Table B1 (continued)
Alnemary et al. (2017) 205 8.0 (3.5) Child age: An increase in
child age – increased use of non-medical
interventions (NMD)* & biomedical interventions (BMD)**.
Parent education: Fathers with ≤ high school diploma –
decreased
BMD, relative to those with higher education**; mothers
without college
degree – increased cultural or religious treatments (CR),
relative to
higher education*.
Income: Income below the sufficiency line – decreased use of
NMD
treatments*.
Location: Residents of major cities – increased use of NMD,
relative to
residents of other cities*.
NS: child gender, ASD severity, comorbidity, age at diagnosis,

parent
age.
Bilgiç et al. (2013) 172 8.8 (3.7) Parent education: Higher
maternal & paternal education – increased
CAM (p = 001 & p = .002) when ‘spiritual healing’ excluded
from
analysis.
ASD beliefs: Genetic/congenital causal beliefs – lower CAM (p
= .008);
Immunization causal beliefs – higher CAM (p = .030).
Family size: More children in the family – decreased CAM***
(when
‘spiritual healing’ was excluded from analysis).
NS: child age, child gender, diagnostic subtype, time since
diagnosis.
Birkin et al. (2008) 77 5.5 (3.2) Ethnicity: Ethnic minorities
less likely to participate in the EarlyBird
program (p = .0001).
NS: marital status (family structure), location, family size.
Bowker et al. (2011) 970 0–5 (41%), 6–12 (46%), 13–18
(9.6%), > 18 (3.4%)
Child age: Early childhood – higher rate of standard therapies,
skills
training, ABA, physiological, alternative, & relationship-based
treatments, relative to middle childhood, adolescents, & adults.
Middle
childhood – higher rate of skill-based treatments & medications,
relative
to early childhood, adolescents, & adults (indicated in text).
Diagnostic subtype: AS group – lower rate of ABA***,
vitamins, &
detoxification treatments*, & higher rate of relationship-based

treatments*** (relative to expected counts). Autistic group –
higher rate
of ABA***, & fewer relationship-based treatments***, (relative
to
expected counts). PDD-NOS group – higher rate of diets,
relationship-
based treatments, & detoxification* (relative to expected
counts).
Carter et al. (2011) 84 3.5 (0.61) NS: Cognitive/adaptive
behaviour (measure: Griffiths Mental
Developmental Scales-Extended Revised).
Christon et al. (2010) 248 8.6 (4.4) Diagnostic subtype: Autism
or PDD–NOS – tried more CAM, relative to
AS (p = .004).
ASD severity: Parent reported severe or moderate ASD – tried
more
CAM, relative to mild ASD***.
Dardennes et al. (2011) 78 13.5 (range: 2.3–44.5) ASD beliefs:
Beliefs in chemical imbalance – increased odds of special
diets (OR = 2.36*) & vitamins (OR = 2.48**); beliefs in illness
during
pregnancy – increased odds of using medications (OR =
2.76***); beliefs
in brain abnormalities – lower odds of vitamins (OR = 0.45*);
beliefs in
early trauma – lower odds of using behaviour therapy (OR =
0.69*) &
PECs (OR = 0.59**); genetic beliefs – increased odds of
TEACCH
(OR = 1.76*); food allergy beliefs – increased odds of chelation
(OR = 4.27**), special diets (OR = 2.38**) & vitamins (OR =
2.29**) &
lower odds of drug treatments (OR = 0.50**).

NS: child age, ASD severity, parent education, parent age.
Goin-Kochel et al. (2007) 479 8.3 (4.3) Child age: Early
childhood & middle childhood – more behavioural/
educational/alternative treatments, relative to adolescents***.
Adolescents tried & used more drug treatments relative to
middle
childhood & early childhood***. Early childhood had tried
more diets
than older children***.
Diagnostic subtype: Autism or PDD-NOS – had tried*** or
were using
more special diets, relative to AS (p = .029). AS had tried mor e
drug
treatments relative to autism (p < .02). AS/PDDNOS were using
more
drug treatments relative to autism. Autism had tried more diets
than
those with AS (p = .027). Autism & PDD-NOS had tried & were
using
more behavioural/educational/alternative treatments relative to
AS***.
Statistics for age & subtype group differences for specific
treatments are
also reported in paper.
Granich et al. (2014) 169 8.57 (4.8)
(2018) 17–35
29

Child medication use: Psychotropic medication more among
CAM
users, relative to non-CAM users (p = .036).
NS: child age, child gender, diagnostic subtype, ASD severity,
age at
diagnosis, parent education, ethnicity, income.
Green et al. (2006) 552 0–5 (34%), 6–10 (36%), 11–14
(18%), ≥15 (12%)
Diagnostic subtype: AS – lower use of standard therapies***,
skills
based therapies***, ABA therapies***, medications*,
physiological
therapies***, alternative diets***, relationship-based
treatments*** &
combined programs***, relative to autism.
Hall and Riccio (2012) 452 Child age not reported. ASD
severity: Severity (parent reported) – predictive of total CAM
used
(p = .006) as well as the use of specific CAM (reported in
paper)**.
Parent education: Parents with a graduate degree – more likely
to use
CAM than those with technical school/some college (p = .02).
Marital status: Married parents – more likely to use CAM,
relative to
divorced parents (p = .02).
NS: ethnicity.
Hanson et al. (2007) 112 < 5 (17%), 5–10 (49%),

> 10 (34%)
Diagnostic subtype: Children with GDD/MD & autism – higher
CAM
use relative to those with PDD-NOS or other***.
Time since diagnosis: More years since diagnosis – increased
chances
of CAM use (p = .02; sig. in multivariate analysis only).
Parent education: Higher maternal education – increased use of
CAM
(p = .04; sig. in univariate analysis only).
NS: child age, child gender, ethnicity.
Harrington et al. (2006) 77 7.2 (range: 2–19) NS: comorbidity,
parent education, ethnicity & income.
Horovitz et al. (2012) 78^ 2.3 (0.39) ASD severity: Those using
psychotropic medication – higher severity,
relative to no medication ASD group**.
Irvin et al. (2012) 137 3.97 (0.61) Parent stress: Higher level of
stress – more likely to use private OT
services (p = .031).
NS: child age, child gender, ASD severity, cognitive/adaptive
behaviour,
ethnicity.
Data on school-based services and dosage of therapy – not
extracted.
Levy et al. (2003) 284 4.6 (2.6) Comorbidity: Children with
comorbidities – lower odds (aOR = 0.3*) of
CAM use, relative to those without.
Ethnicity: Latino background – increased odds (aOR = 6.5*) of
CAM
use, relative to Caucasian reference group.
NS: child age, child gender, family member with ASD.

McIntyre and Barton
(2010)
73 4.6 (1.0) NS: Child age, ASD severity, adaptive behaviour,
parent education,
income (data on CAM use extracted).
Memari et al. (2012) 345 7–8 (39.8%), 9–10 (31.9%),
11–12 (20.4%), 13–14 (8.0%)
Child age: Increased odds (OR = 6.42*) of using 3 or more
psychotropic
medications concurrently in 11–12 years group, relative to 7–8
years.
NS: child gender, ASD severity, comorbidity, parent education,
marital
status, income.
Miller et al. (2012) 400 9.0 (6.0) NS: child age, time since
diagnosis, parent education, parent age,
income.
Mire et al. (2014) 1605 8.7 (3.3) Child age: Child age –
increased use of psychotropic medication***.
Cognitive: Higher FSIQ – lower use of psychotropic
medication***.
Mire, Gealy et al. (2015) 68 8.74 (3.7) Child age: As age
increased – lower odds of biomedical treatments
(OR = 0.789, p = .037).
Cognitive: Higher verbal cognitive scores – lower odds of using
intensive behavioural interventions (OR = 0.997, p = .013).
ASD Beliefs: Attributing child symptoms to ASD – increased
odds of
behavioural interventions (OR = 1.321, p = .027) & lower odds

of
psychotropic medication (OR = 0.820, p = .037). Perceptions of
control
over treatment – increased odds of OT (OR = 1.328, p = .008),
intensive
interventions (OR = 1.609, p = .042), & psychotropic
medications
(OR = 1.494, p = .001). Believing ASD to be chronic – lower
odds of
speech therapy (OR = 0.792, p = .008).
Only data on current study sample/main analysis extracted.
Mire, Raff et al. (2015) 2758 8.6 (3.6) Child age: 6 year olds –
more likely to use private speech therapy***,
private OT** & intensive behavioural treatment**, relative to
older
children (11 & 16 years). 11 year old & 16 year olds – more
likely to use
psychotropic medication** relative to 6 year olds.
(2018) 17–35
30
Owen-Smith et al. (2015) 1084 0–4 (9.2), 5–9 (34.2), 10–14
(37.9), 15–18 (18.6)

Child age: ≤4 years of age (aOR 3.20***) & 5–9 years (aOR =
1.97***) –
increased CAM, relative to 15–18 year old group. Younger
children –
increased odds of using CAM products (0–4 years, aOR =
3.97***; 5–9
years aOR = 1.93**) relative to 15–18 group.
Child medication use: Children using prescription medications –
increased odds of CAM (aOR = 2.16***) & CAM products
(aOR = 2.08***) relative to those not taking medications.
Parent education: Graduate college – higher odds of CAM
(aOR = 2.27*) & CAM products (aOR = 2.19**) relative to ≤
high
school.
NS: child gender, diagnostic subtype, marital status (sig. in
bivariate
analysis only), ethnicity, income.
Patten et al. (2013) 70 4.2 (1.4) Parent education: Higher
education – increased use of gluten/casein
free diets & vitamin therapy (maternal, p = .014 & paternal p =
.042).
NS: child gender, ASD severity, cognitive/adaptive behaviour,
ethnicity,
income.
Perrin et al. (2012) 3173 2–5 (56.4%), 6–11 (33.5%),
12–18 (10.2%)
Diagnostic subtype: AS or PDD-NOS – lower odds of CAM,
relative to
autism (ORs = 0.62* & 0.66*). PDD-NOS or AS – lower odds of
special
diets, relative to autism (ORs = 0.44* & 0.65*). PDD-NOS or
AS – lower
odds of other CAM, relative to autism (OR = 0.67* & 0.72*).

Comorbidity: GI problems – increased CAM use (OR = 1.88*),
special
diets (OR = 2.38*), & other CAM (OR = 1.82*). Seizures –
increased
odds of CAM (OR = 1.58*), special diets (OR = 1.97*) & other
CAM
(OR = 1.66*).
Child medication use: Reported psychotropic medication –
lower odds
of special diets (OR = 0.69*).
Challenging behaviour: Higher challenging behaviour (CBCL
score) –
increased CAM (OR = 1.29*) & special diets (OR = 1.34*).
NS: gender.
Pickard and Ingersoll
(2015)
244 6.41 (2.57) Income: Income – predictor of evidence-based
practices used**.
NS: ASD severity, parent education.
Pringle et al. (2012) 1420 Range: 6–17 years Child age:
Children 6–11 years – more likely to use speech therapy or
OT, relative to those 12–17 years*.
Rosenberg et al. (2010) 5181 0–2 (.9%), 3–5 (27.3%), 6–11
(51.6%), 12–17 (20.1%)
Child age: 6–11 years & 12−17 years increased use
psychotropic
medications, relative to 3–5 years, (ORs = 2.4 & 4.4,
respectively***).
Diagnostic subtype: AS – more likely to use psychotropic
medication**

(sig. in bivariate analysis only).
Comorbidity: ID – increased odds of psychotropic medications
(OR = 1.3, p = .012), relative to no ID. No comorbidity – lower
odds of
psychotropic medication use (OR = 0.3***), relative to any
comorbidity.
Ethnicity: Hispanic families – less likely to use psychotropic
medication,
relative to non-Hispanic families** (sig. in bivariate analysis
only).
Location: Residents of large metropolitan areas – less likely to
be using
psychotropic medication** (sig. in bivariate analysis only).
NS: child gender, parent education.
Salomone et al. (2015) 1680 4.8 (1.2) Child gender: Male –
lower odds of mind-body practices (OR = 0.68,
p = 0.010).
Child medication use: Increased odds of diets & supplements
(OR = 1.62***).
Parent education: Higher education – increased odds of diets &
supplements (OR = 1.35, p = 0.013) & mind-body practices
(OR = 1.64***).
NS: child age.
Salomone et al. (2016) 1680 4.8 (1.2) Child age: Older children
– decreased odds of behavioural,
developmental & relationship-based interventions (OR =
0.98***).
Time since diagnosis: ≥1 since diagnosis – increased odds of
behavioural, developmental & relationship interventions (OR =
1.92***)
& speech intervention (OR = 2.06***).
Parent education: Higher education – increased odds of
behavioural,
developmental & relationship-based interventions (OR =

1.54***).
NS: child gender (statistics for specific regions in Europe are
also
provided in paper).
Thomas, Ellis et al.
(2007)
383 6.0 (1.8) Child age: ≤4 years – increased odds of
supplements (OR = 2.24*),
PECs (OR = 2.09*) & speech therapy (OR = 2.49*) & lower
odds of
(2018) 17–35
31
medication (OR = 0.53*) & social skills training (OR = 0.38*),
relative
to 5–8 year olds. Children 9–11 years – lower odds of PECs
(OR = 0.24*)
& sensory integration therapy (OR = 0.38*).
Diagnostic subtype: AS – increased medication (OR = 2.11*),
lower
odds of PECS (OR = 0.32*) & special diets (OR = 0.26*),
relative to
autism.

Comorbidity: ID – increased odds (OR = 2.09*) of sensory
integration
therapy, relative to those with no ID.
Parent education: College degree – increased odds of PECs
(OR = 2.19*) & hippotherapy (OR = 3.93*), relative to high
school.
Parent stress: Stress – increased odds of medications (OR =
1.08*),
supplements (OR = 1.07*), PECS (OR = 1.07*) & hippotherapy
(OR = 1.10*).
Income: Higher income – increased odds (OR = 2.49*) of
speech
therapy, relative to lower income.
Ethnicity: Minority groups – lower odds of sensory integration
therapy
(OR = 0.25*), relative to Caucasian reference group.
NS: location.
Valicenti-McDermott
et al. (2014)
50* 8.8 (3.0) Challenging behaviour: Correlations between total
CAM & child
irritability***. Children who used ≥2 types of CAM were more
likely to
have Aberrant Behaviour Checklist irritability scores above the
85th
percentile (p = .03) & hyperactivity scores above the 85th
percentile**.
Those who used CAM were more likely to have an irritability
score > 85th percentile, relative to those who do not use CAM
(p = .04).
Comorbidity: Children with food allergies were more likely to
use CAM,
relative to those without food allergies**

Parent stress: Correlation between total CAM used and
Parenting Stress
Index score***.
Ethnicity: Hispanic mothers reported using fewer types of CAM
(p = .03) & non-Hispanic families – more likely to use ≥2 CAM
types*.
NS: child gender, time since diagnosis, age at diagnosis, child
medication use, parent education, parent age, family member
with ASD.
Winburn et al. (2014) 258 < 2.11 (2%), 3–5.11 (31%),
6–11 (67%)
NS: child age (indicated in text).
Witwer and Lecavalier
(2005)
353 9.5 (3.9) Child age: Older age – increased odds of
psychotropic medication
(OR = 1.19***), younger age – increased odds of modified diet
(OR = 0.78***).
Adaptive behaviour: Higher scores on Scales of Independent
Behaviour
– lower odds of modified diet (OR = 0.48*).
Challenging behaviour: Lower calm/compliant scores –
increased odds
of psychotropic medication (OR = −0.89*) & higher
hyperactivity
scores – increased odds of psychotropic medication (OR =
1.08***).
Modified diet – lower insecure/anxious scores* (preliminary
analysis).
NS: child gender, (data on specific medication classes included
in paper).

Wong (2009) 98^ 0– < 3 (3.1%), 3– < 5
(27.6%), 5– < 10 (48.0%),
10– < 15 (17.3%), 15– < 18
(4.1%)
NS: child age, child gender, comorbidity, parent age, parent
education,
family size.
Wong and Smith (2006) 50* 9 (range 14–17) Parent education:
University degree, college or diploma – higher CAM
use, relative to those with high school or less (indicated in text
only;
statistic reported for combined ASD & control group).
NS: child age, child gender, parent age, family member with
DD.
Zablotsky et al. (2015) 1420* 6–11 (54.8%), 12–17 (45.2%)
Comorbidity: ASD & ID – increased use of medication**,
sensory
integration*, CBT***, physical therapy*, speech therapy*,
relative to ASD
only.
Children with co-occurring psychiatric diagnoses in the ASD
group –
more likely to be using medications*.
Zuckerman et al. (2015) 1420 6–8 (20.9%), 9–11 (33.7%),
12–14 (25.6%), 15–17 (19.7%)
ASD beliefs: Beliefs that ASD is a lifelong condition –
increased odds of
using psychotropic medications (aOR = 1.89, p = .003) &
beliefs that
ASD is a mystery – lower odds of behaviour intervention (aOR
= 0.66,

p = .026).
Ethnicity: Black (non-Hispanic) background – lower odds of
using
psychotropic medication (aOR = 0.41) & non-Hispanic
background –
lower odds of behavioural intervention (aOR = 0.37), indicated
in text.
NS: parent education, income.
(2018) 17–35
32
Appendix C
See Table C1
Zuckerman et al. (2017) 722 8.9 (1.5) Age at diagnosis: 4 years
or older – higher use of psychotropic
medication (aOR = 3.09***) & lower odds of behavioural
intervention
(aOR = 0.55, p = .039) relative to those diagnosed before 4
years. Older
age at diagnosis (continuous variable) – increased use of
psychotropic
medication***.

AS = Asperger’s syndrome, PDD-NOS = Pervasive
Developmental Disorder, Not Otherwise Specified, OR = odds
ratio, aOR = adjusted odds ratio, NS = not sig-
nificant.
Note: only data on synthesised factors included in table.
^ ASD subsample.
* p < .05.
** p < .01.
*** p < .001.
Table C1
Quality assessment summary scores (n = 51).
Score range Author (year) Summary score Score range Author
(year) Summary score
≥0.60 Finke et al. (2015) 0.60 ≥ 0.90 Owen-Smith et al. (2015)
0.90
Pringle et al. (2012) 0.90
≥0.65 Wong and Smith (2006) 0.67 Salomone et al. (2015) 0.90
Salomone et al. (2016) 0.90
≥0.70 Grant et al. (2016) 0.70 Al Anbar et al. (2010) 0.94
Hall and Riccio (2012) 0.72 Christon et al. (2010) 0.94
Winburn et al. (2014) 0.72 Dardennes et al. (2011) 0.94
McIntyre and Barton (2010) 0.94
≥0.75 Edwards et al. (2016) 0.75 Mire, Gealy et al. (2015) 0.94
Tzanakaki et al. (2012) 0.75 Mire et al. (2014) 0.94
Birkin et al. (2008) 0.78 Patten et al. (2013) 0.94
Miller et al. (2012) 0.78 Provenzi et al. (2016) 0.94
Wong (2009) 0.94

≥0.80 Serpentine et al. (2011) 0.80
Granich et al. (2014) 0.83 ≥ 0.95 Perrin et al. (2012) 0.95
Harrington et al. (2006) 0.83 Rosenberg et al. (2010) 0.95
Memari et al. (2012) 0.83 Zuckerman et al. (2015) 0.95
Pickard and Ingersoll (2015) 0.83 Carlon et al. (2015) 1.00
Valicenti-McDermott et al. (2014) 0.83 Green et al. (2006) 1.00
Witwer and Lecavalier (2005) 0.83 Zablotsky et al. (2015) 1.00
Zuckerman et al. (2017) 1.00
≥0.85 Bowker et al. (2011) 0.85
Hebert (2014) 0.85
Mire, Raff et al. (2015) 0.85
Akins et al. (2014) 0.86
Call et al. (2015) 0.86
Alnemary et al. (2017) 0.89
Bilgiç et al., (2013) 0.89
Carter et al. (2011) 0.89
Goin-Kochel et al. (2007) 0.89
Hanson et al. (2007) 0.89
Horovitz et al. (2012) 0.89
Irvin et al. (2012) 0.89
Levy et al. (2003) 0.89
Thomas, Ellis et al. (2007) 0.89
Thomas, Morrissey et al. (2007) 0.89
(2018) 17–35
33
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