Six ways to get a grip on co-creating curriculum with patients
Bibliographic record
Abstract
There is growing recognition of the value and importance of patient engagement in medical education. In this work, we reflect on both the literature on patient engagement and our experiences with a recent initiative focused on the co-creation of educational curricula with patient and healthcare professional partners and offer recommendations for educators and researchers interested in engaging in patient partnerships to develop medical education curriculum. We adopted a co-creation approach, in which patient and healthcare professional Subject Matter Experts (SMEs) were provided an opportunity to co-create curricular material. During the curricular development period, we experienced successes and challenges that allowed us to develop six recommendations to “get a grip” on adopting co-creation approaches to curriculum development in medical education. By applying these recommendations, medical educators can help foster meaningful and sustainable patient partnerships.
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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 | 0.147 | 0.127 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.016 | 0.022 |
| Scholarly communication | 0.028 | 0.033 |
| Open science | 0.006 | 0.033 |
| Research integrity | 0.012 | 0.025 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".