Development and delivery of justice, equity, diversity, inclusion, and anti-oppression concepts in entry-level health professional education: a scoping review protocol
Bibliographic record
Abstract
OBJECTIVE: The objective of this scoping review is to identify the frameworks, guidelines, and models used to develop and deliver justice, equity, diversity, inclusion (JEDI), and anti-oppression (AO) principles in mandatory, entry-level health care professional program curricula (EHCPPC). A secondary objective will be to examine how these frameworks, guidelines, and models are used. INTRODUCTION: Health inequities are perpetuated globally, as observed by the suboptimal quality of care and health outcomes among equity-deserving groups. An understanding of JEDI and AO concepts is necessary in health care settings to promote culturally safe and high-quality care; however, entry-level health care programs may lack adequate integration of content and/or delivery of these principles. This scoping review will summarize the international literature on frameworks, guidelines, and models used to develop and deliver JEDI and AO concepts in EHCPPC. INCLUSION CRITERIA: This review will consider articles that discuss frameworks, models, or guidelines included in EHCPPC that guide the development and/or delivery of JEDI and AO principles in any country. Studies will be considered if they were published from 2015 to the present and are in English. All study designs will be considered for inclusion. METHODS: This review will be conducted in accordance with the JBI methodology for scoping reviews. A search of MEDLINE (Ovid), Embase (Ovid), and CINAHL (EBSCOhost) will be conducted. Two or more independent reviewers will assess titles and abstracts, screen full-text studies, and extract data from included studies. Data from the included studies will be collated into tables or figures and described in a narrative summary. REVIEW REGISTRATION: Open Science Framework osf.io/ewqf8.
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 | 0.127 | 0.108 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.009 | 0.012 |
| Bibliometrics | 0.024 | 0.021 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.007 | 0.009 |
| Research integrity | 0.009 | 0.005 |
| Insufficient payload (model declined to judge) | 0.041 | 0.010 |
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".