Investigating Canadian medical school attrition metrics to inform socially accountable admissions planning
Bibliographic record
Abstract
OBJECTIVE: Attrition from Canadian medical degree programs was never described despite differences in admissions requirements at the 17 faculties of medicine. Knowledge on attrition metrics could help the faculties evaluate new avenues for addressing the Association of Faculties of Medicine's (AFMC) Future of Medical Education in Canada (FMEC MD) recommendation to enhance admissions practices with the goal to improve social accountability and student diversity. METHOD: AFMC databases were used to track medical degree completion of all Canadian M.D. students who enrolled between 2003 and 2007. Students were followed and assigned an M.D. completion status as of by July 1, 2013. Bivariate statistics were used to evaluate if demographic, admission and degree progression variables were associated with medical school attrition. RESULTS: Of 11,454 students enrolled in Canadian M.D. programs from 2003 to 2007, only 197 (1.7%) did not complete. Québec had significantly higher attrition than other jurisdictions with age, educational attainment at time of enrolment, MCAT completion and struggling academically associated with attrition. CONCLUSION: Attrition from Canadian MD programs is rare and associated with differences in admission requirements and possibly suggests an optimum life stage for medical studies. Improved knowledge of attrition-related factors may offer an additional level of evidence for improving the alignment between admissions policies and the social accountability objectives of medical schools.
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 | 0.024 | 0.087 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.016 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".