(034) EDI QUALITY IMPROVEMENT IN REPRODUCTIVE MEDICAL EDUCATION: A WORKED MODEL
Bibliographic record
Abstract
Abstract Introduction Countless patients face challenges obtaining holistic generalist care and experience health disparities related to race/ethnicity and social structural determinants of health (SSDOH). Revisions to medical curriculum are necessary to ensure that graduates can competently provide care that is socially accountable and aligns with the needs of patients. Evidence supports the inclusion of generalist and equity, diversity and inclusion (EDI) content in curriculum to foster such competency. However, a 2022 study conducted at our medical school revealed a lack of generalism and EDI content in the case based learning (CBL) curriculum. An evidence-based process for assessing existing CBL curriculum for generalist content exists, but no process for revision has been described. While a process to revise EDI content in CBL curriculum exists, no studies to date have focused on reproductive medicine and urology content and the unique challenges associated with this area of medicine, such as the additional impact of stigma, trauma and discrimination related to pregnancy-related choice, sexual orientation or gender diversity. Objective To enhance generalist and EDI content within the Reproductive Medicine and Urology Course (RMUC) CBL curriculum. Further, to develop a worked model for enhancing generalist and EDI content in an existing CBL reproductive medicine and urology curriculum. Lastly, to provide medical graduates with the tools to provide care that is socially accountable and aligns with the current needs of patients. Methods We evaluated and revised five CBL cases in our medical school’s RMUC curriculum. We employed the Toronto Generalism Assessment Tool (T-GAT) to assess generalism, and adapted guidelines from Krishnan et al. to assess and implement the necessary changes related to EDI. Upon completion, we re-evaluated the RMUC CBL curriculum to ensure the incorporation of generalism and EDI content. We also used existing revision processes described by Bruner et al. to guide our overall approach. Results RMUC CBL cases were modified and our methods documented. Through this process, several challenges were identified and include difficulties with: (1) obtaining images due to lack of commercially available content that includes members of equity-deserving groups, (2) the time needed to appropriately incorporate SDOH already present in the cases, and (3) detecting content that was subtly paternalistic or missing a trauma-informed approach. Conclusions Generalism and EDI were lacking within the RMUC curriculum. Development of a worked model, accompanied by reflections on navigating encounters challenges, is underway. This will serve as a guide to others wishing to incorporate generalism and EDI into their reproductive medicine education programs. Disclosure No.
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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 | 0.017 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| 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".