Associations between residency selection strategies and doctor performance: a meta‐analysis
Bibliographic record
Abstract
OBJECTIVES: The purpose of this study was to use meta-analysis to establish which of the information available to the resident selection committee is associated with resident or doctor performance. METHODS: Multiple electronic databases were searched to 4 September 2012. Two reviewers independently selected studies that met the present inclusion criteria and extracted data in duplicate; disagreement was resolved by consensus. Risk for bias was assessed using a customised bias assessment tool. Measures of association were converted to a common effect size (Hedges' g). Meta-analysis was performed using the random-effects model for each selection strategy and all outcomes without pooling. Sensitivity analysis for each selection strategy-outcome pair was performed with pooling of effect size. RESULTS: Eighty studies involving a total of 41 704 participants were included in the meta-analysis. Seventeen different selection strategies and 17 outcomes were assessed across these studies. The strongest positive associations referred to examination-based selection strategies, such as the US Medical Licensing Examination (USMLE) Step 1, and examination-based outcomes, such as scores on in-training examinations. Moderate positive associations were present for medical school marks and both examination-based and subjective outcomes. Minimal or no associations were seen for the selection tools represented by interviews, reference letters and deans' letters. CONCLUSIONS: Standardised examination performance and medical school grades show the strongest associations with current measures of doctor performance. Deans' letters, reference letters and interviews all show a lower than expected strength of association given the relative value often assigned to them during resident doctor selection. Objective selection strategies are potentially the most useful to residency selection committees based on current evaluative methods. However, reports in the literature of validated long-term doctor performance outcomes are scant.
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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 | 0.032 | 0.053 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.059 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".