Development and delivery of justice, equity, diversity, inclusion, and anti-oppression concepts in entry-level health professional education: A scoping review: BEME Guide No. 88
Bibliographic record
Abstract
PURPOSE: Justice, equity, diversity, inclusion (JEDI), and anti-oppression (AO) concepts are necessary in healthcare settings to promote culturally safe and high-quality care; however, entry-level healthcare program curricula (EHPPC) may lack adequate integration and/or delivery of these concepts. The primary aim of this scoping review is to identify what guidelines, frameworks, and models (GFMs) are used, and how they are used, to develop and deliver JEDI, and AO concepts in mandatory EHPPC. METHODS: A search of Ovid MEDLINE, Ovid EMBASE, and CINAHL was conducted for studies published in English from 2015 onwards that discuss what GFMs are included in mandatory EHPPC and how they guide the development and/or delivery of JEDI and/or AO concepts. Data from the included studies was collated into themes which were presented in tables and figures and described in narrative summaries. RESULTS: Sixty-one studies from various healthcare programs including medicine, nursing, pharmacy, dentistry, and dietetics were included in this review. Data from the studies were organized into eight categories: GFMs, concepts, methods of evaluation, length and frequency of sessions, modes of delivery, learning activities, and training of curricular developers and facilitators. CONCLUSIONS: GFMs are used in a variety of ways to integrate JEDI and/or AO concepts into health professional curriculum. Variability in the training of developers and facilitators of curricular concepts also exists. Future research is needed to determine if consistent or variable GFMs, as well as JEDI and/or AO developer and facilitator training, would be more effective for students' learning of these concepts.
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 | 0.037 | 0.123 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.023 | 0.023 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 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".