Bibliographic record
Abstract
PURPOSE: How to best select future doctors and the implications of selection for equity and access are timely, relevant, and complex issues that fundamentally affect other aspects of medical education such as curriculum design and social accountability. The authors thus conducted an environmental scan of practices related to access and selection in Canadian medical schools. METHOD: The authors drew and built on a literature review, key informant interviews, and expert panel discussions conducted as part of the 2008-2009 Future of Medical Education in Canada project to detail the empirical basis for prioritizing the study of access and selection, the evidence base of current practices, and implications for medical schools. RESULTS: Data clustered around four principles: (1) selection criteria must address current attributes and future potential, (2) access to medical school and diversity within the class are linked to a school's social accountability framework, (3) sound instruments and protocols are necessary to maximize reliability and validity, and (4) medical schools must be accountable for the effectiveness of their admissions processes. Although initiatives addressing barriers exist, ongoing challenges include recruitment and selection for overall diversity, adoption of better criteria for nonacademic achievement, and empirical validation of selection processes. CONCLUSIONS: Evidence-based selection processes can optimize the provision of broadly competent physicians for a given population. Schools must work to minimize systematic barriers for specific groups. Although this analysis provides a Canadian perspective, the principles and implications are relevant to medical education institutions elsewhere.
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 | 0.058 | 0.188 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.035 | 0.005 |
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".