The literature exploring the perspectives and experiences of racialized students in entry-level health professional education programs: a scoping review protocol
Bibliographic record
Abstract
OBJECTIVE: The objective of this scoping review is to determine the breadth of literature exploring the perspectives and experiences of racialized students in entry-level health professional education programs. INTRODUCTION: Despite the implementation of equity, diversity, and inclusion policies and alternative admission criteria for minority students, racialized minorities continue to be underrepresented and have the highest attrition rate in health professional education programs. Furthermore, the students who eventually work in health care settings report experiences of microaggressions and prejudice. By not exploring the experiences of racialized students in health professional programs, equity, diversity, and inclusion policies and curricula risk becoming irrelevant or performative. INCLUSION CRITERIA: Studies exploring the perspectives and experiences of racism and discrimination, or related concepts, among non-dominant ethno-racial (eg, Indigenous, Black, South Asian, Asian, Hispanic, Pacific Islander), entry-level health professional students will be included. Experimental study designs as well as observational and qualitative studies will also be included. METHODS: The review will be conducted in line with the JBI methodology for scoping reviews. Five databases will be searched, namely, MEDLINE (Ovid), Embase (Ovid), CINAHL (EBSCOhost), ERIC, and Global Health (Ovid), with no limitations on language or year. A gray literature search of relevant websites will also be conducted. Two reviewers will independently screen articles against the inclusion criteria and extract and summarize data. Disagreements will be resolved through discussion or with a third reviewer. REVIEW REGISTRATION: Open Science Framework https://osf.io/4bhg6.
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 | 0.128 | 0.097 |
| Meta-epidemiology (narrow) | 0.005 | 0.006 |
| Meta-epidemiology (broad) | 0.013 | 0.011 |
| Bibliometrics | 0.034 | 0.022 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.007 | 0.009 |
| Research integrity | 0.011 | 0.006 |
| Insufficient payload (model declined to judge) | 0.040 | 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".