Facilitating admissions of diverse students: A six-point, evidence-informed framework for pipeline and program development
Bibliographic record
Abstract
INTRODUCTION: Several national level calls have encouraged reconsideration of diversity issues in medical education. Particular interest has been placed on admissions, as decisions made here shape the nature of the future physician workforce. Critical analysis of current practices paired with evidence-informed policies may counter some of the barriers impeding access for underrepresented groups. METHODS: We present a framework for diversity-related program development and evaluation grounded within a knowledge translation framework, and supported by the initiation of longitudinal collection of diversity-related data. We provide an illustrative case study for each component of the framework. Descriptive analyses are presented of pre/post intervention diversity metrics if applicable and available. RESULTS: The framework's focal points are: 1) data-driven identification of underrepresented groups, 2) pipeline development and targeted recruitment, 3) ensuring an inclusive process, 4) ensuring inclusive assessment, 5) ensuring inclusive selection, and 6) iterative use of diversity-related data. Case studies ranged from wording changes on admissions websites to the establishment of educational and administrative offices addressing needs of underrepresented populations. CONCLUSIONS: We propose that diversity-related data must be collected on a variety of markers, developed in partnership with stakeholders who are most likely to facilitate implementation of best practices and new policies. These data can facilitate the design, implementation, and evaluation of evidence-informed diversity initiatives and provide a structure for continued investigation into 'interventions' supporting diversity-related initiatives.
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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 | 0.173 | 0.104 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.011 | 0.007 |
| Science and technology studies | 0.009 | 0.022 |
| Scholarly communication | 0.017 | 0.012 |
| Open science | 0.007 | 0.021 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".