Advancing Curriculum Development and Design in Health Professions Education: A Health Equity and Inclusion Framework for Education Programs
Bibliographic record
Abstract
ABSTRACT: The COVID-19 pandemic has exacerbated pre-existing health inequities in vulnerable and marginalized patient populations. Continuing professional development (CPD) can be a critical driver of change to improve quality of care, health inequities, and system change. In order for CPD to address these disparities in care for patient populations most affected in the health care system, CPD programs must first address issues of equity and inclusion in their education development and delivery. Despite the need for equitable and inclusive CPD programs, there remains a paucity of tools and frameworks available in the literature to guide CPD and broader education providers on how best to develop and deliver equitable and inclusive education programs. In this article, we describe the development and application of a Health Equity and Inclusion (HEI) Framework for education and training grounded in the Analyze, Design, Develop, Implement, and Evaluate model for instructional design. Using a case example, specifically a hospital-wide trauma-informed de-escalation for safety program, we demonstrate how the HEI Framework can be applied practically to CPD programs to support equity and inclusion in the planning, development, implementation, and evaluation phases of education program delivery. The case example illustrates how the HEI Framework can be used by CPD providers to respect learner diversity, improve accessibility for all learners, foster inclusion, and address biases and stereotypes. We suggest that the HEI Framework can serve as an educational resource for CPD providers and health professions educators aiming to create equitable and inclusive CPD programs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.030 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".