Using patients’ charts to assess medical trainees in the workplace: A systematic review
Bibliographic record
Abstract
OBJECTIVES: The objective of this review is to summarize and critically appraise existing evidence on the use of chart stimulated recall (CSR) and case-based discussion (CBD) as an assessment tool for medical trainees. METHODS: Medline, Embase, CINAHL, PsycINFO, Educational Resources Information Centre (ERIC), Web of Science, and the Cochrane Central Register of Controlled Trials were searched for original articles on the use of CSR or CBD as an assessment method for trainees in all medical specialties. RESULTS: Four qualitative and three observational non-comparative studies were eligible for this review. The number of patient-chart encounters needed to achieve sufficient reliability varied across studies. None of the included studies evaluated the content validity of the tool. Both trainees and assessors expressed high level of satisfaction with the tool; however, inadequate training, different interpretation of the scoring scales and skills needed to give feedback were addressed as limitations for conducting the assessment. CONCLUSION: There is still no compelling evidence for the use of patient's chart to evaluate medical trainees in the workplace. A body of evidence that is valid, reliable, and documents the educational effect in support of the use of patients' charts to assess medical trainees is needed.
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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 | 0.013 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".