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Y1 Tp2 Pgh > 2. critical appraisal > Flashcards

2. critical appraisal Flashcards

1
Q

Research definition

A

systematic and rigorous process of enquiry which aims to describe phenomena and to develop and test explanatory concepts and theories
- aims to contribute to a scientific body of knowledge

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2
Q

purpose of research

A

identify or test a theory/ hypothesis

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3
Q

what approach is taken for research

A

methodological approach

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4
Q

Audit

A

a count or measurement of current activity/ practice/ performance
- does not address a question or add substantial new knowledge

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5
Q

Evaluation

A

may involve research methods, or audits
examine either methods or activities
- may lead to new understanding

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6
Q

positivist approach

A

deductive, - testing through hypothesis development
testing theory - to give explanation, verification, prediction
objective reality - facts

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7
Q

interpretivist approach

A

inductive - build through empirical examples
developing theory- develop understanding
multiple interpretations of reality - observable symbolic meaning

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8
Q

approach

A

view of the researcher

overall perspective of the study

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9
Q

methodology

A

coherent and defined set of methods

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10
Q

methods

A

practical activity used to achieve the studys aim

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11
Q

scientific method

A 
approach = positivist 
methodology = quantitative, deductive 
methods = surveys, experiments, observations
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12
Q

understanding method ??

A 
approach = interpretivist
methodology = qualitative, inductive
methods = interviews, participants observation, focus groups, document study
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13
Q

healthcare research

A

focus on treatment

implementation
experience (acceptability, is it a good option)
efficiency - cost and equity
effectiveness - efficacy does it work well

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14
Q

how to find which interventions are effective

A

experimental design

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15
Q

how to find out what will happen (prognosis) lead to population studies

A

observational designs

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16
Q

how to find out how things occur or are experienced in a clinical setting

A

qualitative designs

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17
Q

4 characteristics of answerable questions

A

PICO

patient or population
intervention or exposure variable
comparison intervention or exposure variable
outcome

to give a testable hypothesis

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18
Q

hypothesis definition

A

an educated guess or proposition that attempts to explain a set of facts or natural phenomena

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19
Q

T - test

A

T-test compares the means between two samples of normally distributed data

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20
Q

ANOVA

A

• ANOVA compares the means between more than two samples of normally distributed data

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21
Q

odds ratio equation

A

• Odds are calculated by calculating the number of times an event happens by the number of times it
does not happen
• Odds ratios are calculated by dividing the odds of exposure in cases by the odds of exposure in the
control group

Odds of exposure in
cases = a/c
Odds of exposure in
controls = b/d
Odds ratio =
(a/c) / (b/d)
22
Q

Odds ratio values

A

OR of 1 = no difference in risk between the groups
OR > 1 = the rate of the event in experimental group is increased in people who
have been exposed to risk factor
OR < 1 = the rate of the event in experimental group is reduced in people who
have been exposed to risk factor
If confidence interval crosses 1 then the OR is not statistically significant

23
Q

risk ratios equation

A

Risk itself is the probability that an event will happen i.e. divide the number of events by the number of
people at risk
• Risk ratio is calculated by dividing the risk in the treated or exposed group by the risk in the control or
unexposed group

24
Q

risk ratio value

A

RR of 1 = no difference in risk between the groups
RR > 1 = the rate of the event in experimental group is greater than in control
group
RR < 1 = the rate of the event in experimental group is reduced compared to
control group
If confidence interval crosses 1 then the RR is not statistically significant

25
Q

absolute risk reduction

A

Difference between the event rate in the treatment
group to that in the control group.
•ARR allows you to differentiate between
something being statistically significant vs clinically
significant.

26
Q

number needed to treat

A

Used to find out how often a treatment works rather than just whether it works
• Number of people who must be treated to result in benefit for one person

Absolute Risk Reduction (undesirable) =
Control Event Rate – Experimental Event Rate
Absolute Risk Reduction (desirable) =
Experimental Event Rate – Control Event Rate

27
Q

mean

A

Definition: Sum of all the values, divided by the number of values

28
Q

when to use the mean

A

If the spread of data is normally distributed i.e. fairly similar on either side of the
mid-point.

29
Q

median

A

Definition: The middle point in the dataset that has half the values above
and half the values below

30
Q

when to use median

A

It is used to represent the average when the data are skewed i.e. not symmetrical.
Often given alongside interquartile range (more on this later)

31
Q

mode

A

Definition: The most common value within a dataset

32
Q

when to use the mode

A

If we need a label for the most frequently occurring event.
Some papers make reference to a ‘bi-modal’ distribution i.e. where there are two
peaks within the dataset

33
Q

measures of dispersion/ variability - what do they show

A

❑ Refer to how spread out the data is within a distribution
❑ Can also be called measures of dispersion
❑ Different measures of variability relate to different measures of central tendency

34
Q

measures of dispersion/ variability - 4 examples

A

Variance
Standard Deviation
Range
InterquartileRange

35
Q

Variance

A

• Definition: The average of the squared differences from the mean

36
Q

when to use variance

A

Often as precursor to calculating the standard deviation (more on next slide) to give you an idea of
how spread out your data is from the mean.
❑ Samples with low variance have data that is clustered closely about the mean.
❑ Samples with high variance have data that is clustered far from the mean.
❑ Variance is often used to compare the distribution of two data sets.

37
Q

Standard Deviation

A

Definition: The standard deviation measures the spread of the data
about the mean value. It is the square root of the variance.

38
Q

when to use standard deviation

A

❑ SD is used for data which are normally distributed

39
Q

calculating SD and variance

A

on the slide

40
Q

Range

A

Definition: The difference between the maximum and minimum

values in a dataset

41
Q

Interquartile Range (IQR)

A

Definition: The difference between the upper and lower quartiles

42
Q

cohort studies and epidemiology

A

Cohort studies are of particular value in epidemiology, helping to
build an understanding of what factors increase or decrease the
likelihood of developing disease.

43
Q

exposure
outcome
confounder

A

research q = is there a causal relationship btw exposure and outcome
does confounder influence the outcome
is the confounder associated with the exposure

44
Q

Strengths of cohort studies

A

•Gather data regarding sequence of events; can assess
causality.
•Examine multiple outcomes for a given exposure.
•Can calculate rates of disease in exposed and unexposed
individuals over time (e.g. incidence, relative risk).
•Observational by nature so participants are not manipulated
in any way.

45
Q

Weaknesses of cohort studies

A

You may have to follow large numbers of participants for a
long time.
•They can be very expensive and time consuming.
•They are not good for rare diseases or diseases with a long
latency.
•Differential loss to follow up can introduce selection and
attrition bias

46
Q

Quantitative Research:

A

Deductive
Objective
Generalising

47
Q

Qualitative Research:

A

Inductive
Subjective
Contextual

48
Q

• CONVERGENT PARALLEL

A
  1. quantitiative and qualitative Data Collection and Analysis
  2. compare or relate
  3. interpretation
49
Q

• EXPLANATORY SEQUENTIAL

A
  1. quantitiative data collection and analysis
  2. data builds to qualitative data collection and analysis
  3. interpretation
50
Q

EMBEDDED

A
  • Quant (or Qual) Design
  • Quant (or Qual) Data Collection and Analysis
  • Qual (or Quant) Data Collection and Analysis
    (before, during or after)

= interpretation

51
Q

MULTIPHASE

A
  1. study 1 qualitative
  2. informs study 2 quantitative
  3. this informs a third study used mixed methods
52
Q

4 mixed method study designs

A

• CONVERGENT PARALLEL - both data used at once

• EXPLANATORY SEQUENTIAL
- quant data informs qual data collection - interpretation

  • EMBEDDED
  • MULTIPHASE - qual deisgn then quant then mixed methods

Y1 Tp2 Pgh (4 decks)

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