Exploring equity, diversity, and inclusion strategies in geriatric healthcare education: A scoping review
Bibliographic record
Abstract
This scoping review explores Equity, Diversity, and Inclusion (EDI) initiatives within geriatric healthcare professional education, aiming to understand strategies, outcomes, and challenges. The aging global population necessitates healthcare systems that are culturally competent and inclusive, prompting a closer examination of educational interventions. Eight articles met inclusion criteria, predominantly utilizing qualitative and mixed-methods designs. Initiatives ranged from active learning to online simulations, targeting physicians and allied healthcare providers. Participants generally reported high satisfaction and improved attitudes toward diversity and inclusion post-training. Challenges such as resource constraints and curriculum updates were noted. Multidisciplinary training and technological advancements emerged as key strategies, alongside recommendations for enhanced resource allocation and inclusivity in content and faculty. The findings underscore the increased uptake and desire to integrate EDI principles into geriatric healthcare education to prepare professionals to provide equitable care to racial, ethnic, socioeconomic, and gender diverse older adults. This review provides valuable insights for educators and policymakers seeking to foster a culturally competent and inclusive healthcare workforce capable of meeting the evolving needs of aging populations worldwide.
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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 | 0.009 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".