Professional Development in Health Sciences: Scoping Review on Equity, Diversity, Inclusion, Indigeneity, and Accessibility Interventions
Bibliographic record
Abstract
INTRODUCTION: Equity, diversity, inclusion, indigeneity, and accessibility (EDIIA) are critical considerations in the formation of professional development (PD) programs for health care workers. Improving EDIIA competency in health care serves to enhance patient health, staff confidence and well-being, delivery of care, and the broader health care system. There is a gap in the literature as to the efficacy of EDIIA-based PD programs and their individual components. The present article will review available quantitative data pertaining to EDIIA-based PD programs for health care workers as well as their effectiveness. METHODS: A scoping review of articles published in the EBSCOhost, MEDLINE, PubMed, EMBASE, and CINAHL databases was performed. We used the Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement. RESULTS: A total of 14,316 references were identified with 361 reaching full-text review. A final 36 articles were included in the scoping review with 6552 total participants (72.9% women; 26.9% men; 0.2% nonbinary). EDIIA-based PD interventions were developed around the topics of culture ( n = 22), gender ( n = 11), sexual orientation ( n = 9), indigeneity ( n = 6), race ( n = 6), ableism ( n = 1), and ageism ( n = 1). DISCUSSION: Despite an increased interest in developing EDIIA-based PD curricula for health care workers, there are glaring disparities in the quality of care received by marginalized and equity-seeking populations. The present scoping review delineated key features which were associated with increased quantitative efficacy of EDIIA-based PD training programs. Future work should focus on large-scale implementation and evaluation of these interventions across health care sectors and levels of training.
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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 | 0.044 | 0.137 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.019 | 0.021 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".