RESEARCH ARTICLE What information is provided in transcripts and Medical Student Performance Records from Canadian Medical Schools
Bibliographic record
Abstract
Background: Resident selection committees must rely on information provided by medical schools in order to evaluate candidates. However, this information varies between institutions, limiting its value in comparing individuals and fairly assessing their quality. This study investigates what is included in candidates’ documentation, the heterogeneity therein, as well as its objective data. Methods: Samples of recent transcripts and Medical Student Performance Records were anonymised prior to evaluation. Data were then extracted by two independent reviewers blinded to the submitting university, assessing for the presence of pre-selected criteria; disagreement was resolved through consensus. The data were subsequently analysed in multiple subgroups. Results: Inter-rater agreement equalled 92%. Inclusion of important criteria varied by school, ranging from 22.2 % inclusion to 70.4%; the mean equalled 47.4%. The frequency of specific criteria was highly variable as well. Only 17.7 % of schools provided any basis for comparison of academic performance; the majority detailed only status regarding pass or fail, without any further qualification. Conclusions: Considerable heterogeneity exists in the information provided in official medical school documentation, as well as markedly little objective data. Standardization may be necessary in order to facilitate fair comparison of graduates from different institutions. Implementation of objective data may allow more effective intra- and inter-scholastic comparison.
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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 | 0.021 | 0.133 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.010 | 0.013 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".