Identifying and Promoting Best Practices in Residency Application and Selection in a Complex Academic Health Network
Bibliographic record
Abstract
Medical education institutions have a social mandate to produce a diverse physician workforce that meets the public's needs. Recent reports have framed the admission process outcome of undergraduate and postgraduate medical education (UGME and PGME) programs as a key determinant of the collective contributions graduating cohorts will make to society, creating a sense of urgency around the issue of who gets accepted. The need for evidence-informed residency application and selection processes is growing because of the increasing size and diversity of the applicant pool and the need for equity, fairness, social accountability, and health human resource planning. The selection literature, however, is dominated by a UGME focus and emphasizes determination of desirable qualities of future physicians and selection instrument reliability and validity. Gaps remain regarding PGME selection, particularly the creation of specialty-specific selection criteria, suitable outcome measures, and reliable selection systems.In this Perspective, the authors describe the University of Toronto's centralized approach to defining system-level best practices for residency application and selection. Over the 2012-2013 academic year, the Best Practices in Application and Selection working group reviewed relevant literature and reports, consulted content experts, surveyed local practices, and conducted iterative stakeholder consultations on draft recommendations. Strong agreement arose around the resulting 13 principles and 24 best practices, which had either empirical support or face validity. These recommendations, which are shared in this article, have been adopted by the university's PGME advisory committee and will inform a national initiative to improve trainees' transition from UGME to PGME in Canada.
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 | 0.302 | 0.347 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.013 | 0.011 |
| Scholarly communication | 0.017 | 0.008 |
| Open science | 0.007 | 0.020 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.001 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".