Systematic review of evidence-based medicine tests for family physician residents.
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Evidence-based medicine (EBM) is increasingly important in resident education, and reliable and valid tests of competence for family medicine residents are needed. METHODS: MEDLINE, PsycINFO, ERIC, ERC, and the Research & Development Resource Base (University of Toronto) were searched from inception to June 2014 to identify competence tests of family medicine and general practice residents in EBM. Two authors independently assessed all titles, abstracts, and full texts and abstracted data. RESULTS: Three EBM courses were evaluated by the Fresno test. Seven other authors designed EBM interventions and individual tests to evaluate them. Content validity was assessed by nine studies, construct validity by five, face validity by three, and concurrent validity by one. Internal reliability was reported by seven studies, inter-rater by four, item difficult and item discrimination by two, and intra-rater by one. Eight studies reported that knowledge scores increased significantly after the intervention. CONCLUSIONS: Content validity and internal reliability were the most frequently assessed measures. The basic EBM activities of identifying Population, Intervention, Comparison, Outcome, and Study Design (PICOS questions) and computing sensitivity, specificity, and number needed to treat (NNT) are unlikely to change. However, guidelines are often used in EBM courses, and they are updated regularly, which will involve new clinical scenarios, PICOS questions, and statistical computations. The Fresno test has been evaluated with three groups of family medicine residents, has the best documentation of validity and reliability, and is the best candidate for future development. Evaluation tools also need to be developed to measure if care received by patients is EBM.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.017 | 0.108 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.007 |
| Bibliometrics | 0.016 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".