How patient educators help students to learn: An exploratory study
Bibliographic record
Abstract
INTRODUCTION: Benefits of the active involvement of patients in educating health professionals are well-recognized but little is known about how patient educators facilitate student learning. METHOD: This exploratory qualitative study investigated the teaching practices and experiences that prepared patient educators for their roles in a longitudinal interprofessional Health Mentors program. Semi-structured interviews were conducted with eleven experienced health mentors. Responses were coded and analyzed for themes related to teaching goals, methods, and prior experiences. RESULTS: Mentors used a rich variety of teaching methods to teach patient-centeredness and interprofessionalism, categorized as: telling my story, stimulating reflection, sharing perspectives, and problem-solving. As educators they drew on a variety of prior experiences with teaching, facilitation or public speaking and long-term interactions with the health-care system. CONCLUSIONS: Patient educators use diverse teaching methods, drawing on both individualistic and social perspectives on learning. A peer-support model of training and support would help maintain the authenticity of patients as educators. The study highlights inadequacies of current learning theories to explain how patients help students learn.
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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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".