# data[].attributes.body
Recommend what elements of this paper can be translated to practice

# data[].attributes.body
Integrate these findings to create a clinical bottom line* (what should be translated into your clinical practice)

# data[].attributes.body
Judge the value of a paper

# data[].attributes.body
Determine whether there is sufficient evidence to apply to practice

# data[].attributes.body
Analyse how assessment of trustworthiness, validity, replicability and reliability

impacts interpretation of study results

# data[].attributes.body
Contextualise interpretative statistics such as Odds Ratios (ORs), Relative Risk Reduction (RRRs), Numbers Needed to Treat (NNRs), and measures of uncertainty (p values, confidence intervals etc)

# data[].attributes.body
Analyse and contextualise results as presented in a paper

# data[].attributes.body
Critically appraise a paper for trustworthiness (qualitative), validity, replicability and reliability using a critical appraisal tool where appropriate

# data[].attributes.body
Interpret results as presented in a paper

# data[].attributes.body
Calculate interpretative statistics such as Odds Ratios (ORs), Relative Risk Reduction (RRRs), Numbers Needed to Treat (NNRs), and measures of uncertainty (p values, confidence intervals etc)

# data[].attributes.body
Compare the different sources of evidence which underpin clinical practice guidelines (e.g., research evidence, clinical opinion)

# data[].attributes.body
Compare the key elements of common study designs and their inherent strengths and weaknesses, with reference to question type

# data[].attributes.body
Compare appraisal tools and how they align with different research designs and/or guidelines

# data[].attributes.body
Explain the relevance of key diagnostic study features (e.g., screening, diagnosis, sensitivity and specificity, positive and negative predictive values) to clinical diagnosis/assessment

# data[].attributes.body
Explain the difference between random error and systematic error (bias)

# data[].attributes.body
Explain the impact of each type of bias on results

# data[].attributes.body
Discuss measurement validity and reliability in interpreting results

# data[].attributes.body
Explain how key variables are measured in studies (scales, instruments, thematic analysis etc.)

# data[].attributes.body
Discuss the impact of a confounding or a mediating variable on results

# data[].attributes.body
Explain the uses and limitations of subgroup analysis

# data[].attributes.body
Explain why association does not imply causation

# data[].attributes.body
Discuss the commonly used measures of uncertainty

# data[].attributes.body
Explain the difference between “lack of evidence of an effect” and “evidence of no effect”

# data[].attributes.body
Explain the difference between statistical significance and clinical importance

# data[].attributes.body
Recognise the purpose and use of clinical prediction rules/algorithms

# data[].attributes.body
Explain how results may be presented e.g., Forest plots for meta-analysis, summary of findings tables and themes

# data[].attributes.body
Explain why conflict of interest and funding sources are important

# data[].attributes.body
Describe the purpose and processes of different quantitative and qualitative designs

# data[].attributes.body
Describe how different research designs inform the decision-making process

# data[].attributes.body
Identify if research is qualitative, quantitative, or mixed

# data[].attributes.body
Recognise the difference between systematic reviews, meta-analyses, and non-systematic reviews

# data[].attributes.body
Recognise the different sources of evidence which underpin clinical practice guidelines (e.g., research evidence, clinical opinion)

# data[].attributes.body
Describe the key elements of common study designs and their inherent strengths and weaknesses

# data[].attributes.body
Identify appraisal tools that align with different research designs and/or guidelines

# data[].attributes.body
Define key terms associated with diagnostic studies e.g., screening, diagnosis, sensitivity and specificity, positive and negative predictive values

# data[].attributes.body
State the difference between random error and systematic error (bias)

# data[].attributes.body
Identify the major categories of bias

# data[].attributes.body
Describe the concepts of measurement validity and reliability

# data[].attributes.body
Identify the key variables being measured in studies

# data[].attributes.body
Describe how key variables are measured in studies (scales, instruments, thematic analysis etc.)

# data[].attributes.body
Recognise the difference between a confounding and a mediating variable

# data[].attributes.body
Identify the basic types of data such as categorical and continuous

# data[].attributes.body
Describe what is meant by subgroup analysis

# data[].attributes.body
Describe how association does not imply causation

# data[].attributes.body
Describe what is meant by basic frequency measures

# data[].attributes.body
Describe the commonly used measures of uncertainty, in particular, p- values and confidence intervals

# data[].attributes.body
Describe measures of effect

# data[].attributes.body
Define statistical significance and clinical importance

# data[].attributes.body
Describe the use of clinical prediction rules/algorithms

# data[].attributes.body
Recognise the basics of adjustment methods and their limitations

# data[].attributes.body
Describe key methods of data presentation e.g., a forest plot, summary of findings table, Kaplan Meier

# data[].attributes.body
Recognise the importance of considering conflict of interest and funding sources