Guiding Principles for Patient and Public Engagement in the Educational Missions of Medical Schools
Bibliographic record
Abstract
PURPOSE: The purpose of this research was to cocreate with patients and the public a set of evidence-informed guiding principles for their authentic, responsive, ongoing, and sustainable engagement in the mission, goals, curriculum, and delivery of medical education. METHOD: A set of guiding principles of relevance to medical education was identified from the literature. Eight focus groups with patients and community members representing a wide variety of perspectives were conducted in April and May 2022. Participants reviewed, prioritized, and discussed the principles and described successful engagement, resulting in 8 guiding principles in priority order. A summary report was circulated to participants for feedback. The principles were reviewed and endorsed by senior leaders in the medical school. RESULTS: The 8 focus groups were attended by 38 people (age range, mid-20s to postretirement; 7 male, 27 female, and 4 unknown gender). Accountability (19%), inclusion (18%), reciprocity (17%), and partnership and shared decision-making (14%) were chosen as the most important principles. Participants want evidence that their contributions are valued and have made a difference. They want the medical school to include and support a diversity of perspectives that reflect the populations being served by the health care system. They want the medical school to invest in building trusting and respectful long-term relationships with patients and the public. CONCLUSIONS: The guiding principles could be used by medical schools as a starting point to build relationships with their local communities to increase the authentic and sustainable engagement of patients and the public in the educational mission of the medical school.
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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 | 0.225 | 0.147 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.014 | 0.044 |
| Scholarly communication | 0.019 | 0.010 |
| Open science | 0.005 | 0.024 |
| Research integrity | 0.011 | 0.017 |
| Insufficient payload (model declined to judge) | 0.002 | 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".