Systemic discrimination and racism mitigation in U.S. health professions graduate education: a scoping review protocol
Bibliographic record
Abstract
Objective This protocol describes methods of collecting and analyzing evidence for a Scoping Review, describing effective interventions that redress systemic discrimination and mitigate racism in U.S. health professions graduate education.Introduction Physical Therapy leaders have committed to creating anti-racist and anti-bias policies, programming, and actions that will support equity, diversity, and inclusion in academic programs. Best practices can be informed by an analysis of currently available evidence.Methods The scoping review will follow JBI methodology and the PRISMA-ScR extension. Databases will include MEDLINE PubMed, SportDiscus, Web of Science, PAIS Index, Google Scholar, and Thesis and Dissertations. Data will be organized within a policy framework (i.e. policy, standard, guideline, or procedure), as well as conceptual layer (Culture & Climate, Access & Advancement, Faculty/Clinician Education & Training, and Student/Trainee Education and Training).Dissemination The published Scoping Review will inform best practices to address systemic discrimination and racism within U.S. health professions education.
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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 | 0.159 | 0.138 |
| Meta-epidemiology (narrow) | 0.005 | 0.005 |
| Meta-epidemiology (broad) | 0.010 | 0.013 |
| Bibliometrics | 0.022 | 0.017 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.009 | 0.006 |
| Insufficient payload (model declined to judge) | 0.075 | 0.015 |
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".