Research, Evidence-Based Practice, and Education
Concepts to master
Understand levels of evidence, study designs (randomized controlled trials, cohort, case-control, cross-sectional, systematic reviews), measures of association and effect (sensitivity, specificity, predictive values, likelihood ratios, relative risk, odds ratio, number needed to treat), and the difference between statistical and clinical significance. Know how to appraise validity, bias, and generalizability.
Applying evidence
Evidence-based practice integrates best evidence, clinical expertise, and patient values. Understand that a highly sensitive test is useful for ruling out disease when negative, and a highly specific test is useful for ruling in disease when positive. Positive and negative predictive values depend on prevalence, unlike sensitivity and specificity.
Education concepts
Know principles of adult learning, feedback, simulation-based education, and competency assessment. Recognize that formative feedback differs from summative evaluation.
Traps to watch
- Confusing sensitivity with positive predictive value.
- Assuming a statistically significant result is clinically meaningful.
- Ignoring prevalence when interpreting a screening test.
- Treating a single study as definitive without considering design and bias.
Rule of thumb
"SnNout and SpPin": a highly Sensitive test, when Negative, rules out; a highly Specific test, when Positive, rules in. Always ask about the population in which the test was studied.
Worked example: A test with 95% sensitivity and 60% specificity is best used to rule out disease when negative; a positive result in a low-prevalence population still carries a meaningful false-positive rate.