Involving patients in undergraduate health professions education: What’s in it for them?
Bibliographic record
Abstract
OBJECTIVES: Patients have become more involved in research, policy, and health professions education. They are involved in teaching students competencies required for person-centred care, but patient benefits have not received proper attention. This exploratory study identifies how patient involvement in health professions education help patients to practice self-management and shared decision-making. METHODS: Individual interviews were conducted with patients (hereafter 'experts by experience') (N = 11) who participated in the Patient As a Person Module, organised for students of health professions in The Netherlands. Additionally, one of their healthcare professionals (N = 10) and family members (N = 9) were interviewed. Directed content analysis was used. RESULTS: Participants reported that sharing lived experiences helped experts by experience to reflect on their preferences regarding health and healthcare, accept their changed selves, and obtain a renewed sense of purpose. They reported gaining insight into the perspectives of healthcare professionals, which yielded more equal healthcare professional-patient relationships. CONCLUSIONS: Sharing their lived experiences with health and health care with students could help patients in practising effective self-management and participate in shared decision-making. PRACTICE IMPLICATIONS: Approaching patient involvement in health professions education from both the perspectives of students and experts by experience, as opposed to students alone, optimises its societal impact.
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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 | 0.014 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.006 | 0.008 |
| 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".