1. Quality management statement
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I. Contents of quality management statement
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The overall quality objective is to ensure that St John Ambulance delivers a consistently high
level of servicethroughout our extensive and diverse business and charitable activities.
St John Ambulance is committed to continuous improvement and implementing appropriate
quality managementsystems and processes to enable us to deliver the highest practicable quality
services. We will therefore:
 work with our customers and partners to develop our services to meet their needs
 conduct our business in a way that reflects our core values
 create an environment that promotes continuous improvement and knowledge sharing
across all stakeholders
 ensure compliance with legal and other applicable standards
 educate and train our people to support the delivery of high quality services.
We recognise the importance of monitoring and reviewing our quality management systems and
through continual monitoring and planned reviews we have the opportunity to identify
improvements to our service and maintain compliance with legal and ethical standards. This will
effectively provide our customers with the confidence that the provision of service will
be delivered consistently to predetermined high standards. Where appropriate we seek
andattain external accreditation for the services we provide.
Our commitment to quality is supported by individual policies and procedures that address the
activities central to delivering our services, which include;

2. Corporate governance
The St John Ambulance Trustee Board is supported by a structured set of specialist sub-
committees, including a review committee which continually keeps
the operation and effectiveness of the governance structure under
review. Strategic and operational planning, risk management, and budget
planning processes are in place and reviewed annually. An internal audit function
provides assurance to the Board on risk management activities and control systems in
operation and provides and input to continuous improvement processes.
Clinical governance
St John Ambulance operates a national clinical governance framework to provide an
improving quality of service which meets the changing demands of law, regulation and
standards of best practice including those embraced by the Department of Health
( DOH ) National Institute of Health and Clinical Excellence (NICE) and any other
appropriate bodies. We implement appropriate evidence based performance
standards and have an audit process to monitor our success in meeting these standards.
People management
In 2006 St John Ambulance became an Institute of Leadership
and Management ( ILM ) accredited centre with approval to teach level 2 and
3 leadership and management programmes. Since then SJA has received further approval
to deliver both classroom and blended learning programmes for levels 4 and 5. The
policies and procedures in place around the quality management of ILM accredited
qualifications enabled SJA to obtain accredited centre status for Edexcel, the UK’s
largest provider of qualifications. As such, as of this year SJA is now also accredited to
deliver BTECs.
Disability ‘Two Ticks’ Symbol has been awarded to recognise our commitments
regarding the employment, retention, training and career development of disabled
employees.
First aid training
The quality of our first aid training is continually monitored and ISO9001:
2000 certification is maintained for our commercial training services.
Public event cover
Our On Duty Manual provides a framework for organising event cover, ensuring
consistently high standardsare maintained when delivering first aid services. An
established operations working party is tasked with reviewing and updating this manual
to ensure it continues to reflect best practice.
Child protection

3. Our handbook, Working Safely with Young People, covers a whole range of issues
from risk assessments of child related activities to a number
of training and awareness initiatives. St John Ambulance is the only national
organisation to have been awarded a Certificate of Recognition by the NSPCC as part of
their Staying Safe Commitment Scheme reflecting the quality of our child protection
framework.
All of us who are involved in the delivery of St John Ambulance services are fully
committed to our objectives and to attain the appropriate training in all aspects of our
responsibilities.
==================
III. Quality management tools
1. Check sheet
The check sheet is a form (document) used to collect data
in real time at the location where the data is generated.
The data it captures can be quantitative or qualitative.
When the information is quantitative, the check sheet is
sometimes called a tally sheet.
The defining characteristic of a check sheet is that data
are recorded by making marks ("checks") on it. A typical
check sheet is divided into regions, and marks made in
different regions have different significance. Data are
read by observing the location and number of marks on
the sheet.
Check sheets typically employ a heading that answers the
Five Ws:
 Who filled out the check sheet
 What was collected (what each check represents,
an identifying batch or lot number)
 Where the collection took place (facility, room,
apparatus)
 When the collection took place (hour, shift, day
of the week)
 Why the data were collected

4. 2. Control chart
Control charts, also known as Shewhart charts
(after Walter A. Shewhart) or process-behavior
charts, in statistical process control are tools used
to determine if a manufacturing or business
process is in a state of statistical control.
If analysis of the control chart indicates that the
process is currently under control (i.e., is stable,
with variation only coming from sources common
to the process), then no corrections or changes to
process control parameters are needed or desired.
In addition, data from the process can be used to
predict the future performance of the process. If
the chart indicates that the monitored process is
not in control, analysis of the chart can help
determine the sources of variation, as this will
result in degraded process performance.[1] A
process that is stable but operating outside of
desired (specification) limits (e.g., scrap rates
may be in statistical control but above desired
limits) needs to be improved through a deliberate
effort to understand the causes of current
performance and fundamentally improve the
process.
The control chart is one of the seven basic tools of
quality control.[3] Typically control charts are
used for time-series data, though they can be used
for data that have logical comparability (i.e. you
want to compare samples that were taken all at
the same time, or the performance of different
individuals), however the type of chart used to do
this requires consideration.
3. Pareto chart

5. A Pareto chart, named after Vilfredo Pareto, is a type
of chart that contains both bars and a line graph, where
individual values are represented in descending order
by bars, and the cumulative total is represented by the
line.
The left vertical axis is the frequency of occurrence,
but it can alternatively represent cost or another
important unit of measure. The right vertical axis is
the cumulative percentage of the total number of
occurrences, total cost, or total of the particular unit of
measure. Because the reasons are in decreasing order,
the cumulative function is a concave function. To take
the example above, in order to lower the amount of
late arrivals by 78%, it is sufficient to solve the first
three issues.
The purpose of the Pareto chart is to highlight the
most important among a (typically large) set of
factors. In quality control, it often represents the most
common sources of defects, the highest occurring type
of defect, or the most frequent reasons for customer
complaints, and so on. Wilkinson (2006) devised an
algorithm for producing statistically based acceptance
limits (similar to confidence intervals) for each bar in
the Pareto chart.
4. Scatter plot Method
A scatter plot, scatterplot, or scattergraph is a type of
mathematical diagram using Cartesian coordinates to
display values for two variables for a set of data.
The data is displayed as a collection of points, each
having the value of one variable determining the position
on the horizontal axis and the value of the other variable
determining the position on the vertical axis.[2] This kind
of plot is also called a scatter chart, scattergram, scatter
diagram,[3] or scatter graph.
A scatter plot is used when a variable exists that is under
the control of the experimenter. If a parameter exists that

6. is systematically incremented and/or decremented by the
other, it is called the control parameter or independent
variable and is customarily plotted along the horizontal
axis. The measured or dependent variable is customarily
plotted along the vertical axis. If no dependent variable
exists, either type of variable can be plotted on either axis
and a scatter plot will illustrate only the degree of
correlation (not causation) between two variables.
A scatter plot can suggest various kinds of correlations
between variables with a certain confidence interval. For
example, weight and height, weight would be on x axis
and height would be on the y axis. Correlations may be
positive (rising), negative (falling), or null (uncorrelated).
If the pattern of dots slopes from lower left to upper right,
it suggests a positive correlation between the variables
being studied. If the pattern of dots slopes from upper left
to lower right, it suggests a negative correlation. A line of
best fit (alternatively called 'trendline') can be drawn in
order to study the correlation between the variables. An
equation for the correlation between the variables can be
determined by established best-fit procedures. For a linear
correlation, the best-fit procedure is known as linear
regression and is guaranteed to generate a correct solution
in a finite time. No universal best-fit procedure is
guaranteed to generate a correct solution for arbitrary
relationships. A scatter plot is also very useful when we
wish to see how two comparable data sets agree with each
other. In this case, an identity line, i.e., a y=x line, or an
1:1 line, is often drawn as a reference. The more the two
data sets agree, the more the scatters tend to concentrate in
the vicinity of the identity line; if the two data sets are
numerically identical, the scatters fall on the identity line
exactly.

7. 5.Ishikawa diagram
Ishikawa diagrams (also called fishbone diagrams,
herringbone diagrams, cause-and-effect diagrams, or
Fishikawa) are causal diagrams created by Kaoru
Ishikawa (1968) that show the causes of a specific
event.[1][2] Common uses of the Ishikawa diagram are
product design and quality defect prevention, to identify
potential factors causing an overall effect. Each cause or
reason for imperfection is a source of variation. Causes
are usually grouped into major categories to identify these
sources of variation. The categories typically include
 People: Anyone involved with the process
 Methods: How the process is performed and the
specific requirements for doing it, such as policies,
procedures, rules, regulations and laws
 Machines: Any equipment, computers, tools, etc.
required to accomplish the job
 Materials: Raw materials, parts, pens, paper, etc.
used to produce the final product
 Measurements: Data generated from the process
that are used to evaluate its quality
 Environment: The conditions, such as location,
time, temperature, and culture in which the process
operates
6. Histogram method

8. A histogram is a graphical representation of the
distribution of data. It is an estimate of the probability
distribution of a continuous variable (quantitative
variable) and was first introduced by Karl Pearson.[1] To
construct a histogram, the first step is to "bin" the range of
values -- that is, divide the entire range of values into a
series of small intervals -- and then count how many
values fall into each interval. A rectangle is drawn with
height proportional to the count and width equal to the bin
size, so that rectangles abut each other. A histogram may
also be normalized displaying relative frequencies. It then
shows the proportion of cases that fall into each of several
categories, with the sum of the heights equaling 1. The
bins are usually specified as consecutive, non-overlapping
intervals of a variable. The bins (intervals) must be
adjacent, and usually equal size.[2] The rectangles of a
histogram are drawn so that they touch each other to
indicate that the original variable is continuous.[3]
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