Bridging the representation gap in the surgical workforce: a scoping review protocol of programmes and interventions to support surgical careers for underrepresented minority learners
Bibliographic record
Abstract
INTRODUCTION: Despite increasing proportions of underrepresented minority (URM) medical school graduates, their progression into surgical training and leadership remains disproportionately low. Barriers such as financial constraints, limited mentorship and implicit bias contribute to this disparity, creating a disconnect between the diversity of patient populations and those providing care. While interventions such as mentorship programmes and pipeline initiatives have been implemented, their overall effectiveness has not been systematically evaluated. The primary aim of this scoping review is to map the current landscape of interventions, programmes and policies designed to enhance access to surgical careers for URM learners. METHODS AND ANALYSIS: Searches will be conducted on EMBASE, Web of Science and OVID MEDLINE. Three independent reviewers will screen references, extract data and perform analyses with disagreements adjudicated by a fourth reviewer. This review will include studies conducted across all levels of training: secondary (high school or secondary school), postsecondary (undergraduate, medical school) and postgraduate (residency, fellowship), with no geographical restrictions. The definition of URM will be accepted as reported within each individual study, allowing for variability in racial, ethnic, gender, socioeconomic or other criteria. The review will include any structured interventions, programmes or policies aimed at increasing URM representation in surgical education. Data on the nature, duration and target population of each intervention will be extracted. The primary outcome will be the reported impact of interventions on URM representation or participation in surgical education. Secondary outcomes will include characteristics of the study participants, definitions of URM status and any qualitative or quantitative evaluations of intervention effectiveness. ETHICS AND DISSEMINATION: Research ethics approval is not required under University of Toronto policy. Study results will be reported according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews guidelines. Results will be disseminated to relevant stakeholders at conference presentation(s) and submitted for publication in a peer-reviewed journal.
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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 | 0.144 | 0.149 |
| Meta-epidemiology (narrow) | 0.006 | 0.006 |
| Meta-epidemiology (broad) | 0.022 | 0.017 |
| Bibliometrics | 0.021 | 0.017 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.007 | 0.007 |
| Research integrity | 0.012 | 0.006 |
| Insufficient payload (model declined to judge) | 0.038 | 0.009 |
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".