An Innovative Course on Involving Patients in Health Professions Education
Bibliographic record
Abstract
Background & Need for Innovation: Patients can be actively involved in various aspects of health professions education (HPE). However, learners in HPE graduate programs have minimal opportunities to learn how to involve patients in HPE. Steps Taken for Development and Implementation of Innovation: We designed, implemented, and evaluated a 12-week asynchronous, online graduate course that provides learners such opportunities. We established an advisory committee of patients, clinician-educators, and professors to guide course development. Using Thomas et al.'s framework, we established the general and targeted need for the course, identified the learning outcomes, determined the learning activities, and implemented and evaluated the course. It is offered within the asynchronous, online Diploma and Master in HPE at the University of Ottawa, Canada. Evaluation of Innovation: Forty learners participated in the course between 2020 and 2022. Using a survey with closed- and open-ended items, learners reported satisfaction with all course components, and they valued the patient narrative videos created for the course. After course completion, learners reported that the course is relevant to their professional practice. They also reported confidence in their abilities to actively involve patients in HPE. Based on the culminating assignment assessment data, learners attained course expectations. Critical Reflection: Although patients who participated in the narrative videos represented diverse age ranges, health conditions, and experiences in HPE, they were often Caucasian, educated, and from a higher socio-economic background. Also, the level of engagement between patients and learners in the course was limited. We are committed to improving our own patient involvement efforts.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".