Patients as educators: Interprofessional learning for patient-centred care
Bibliographic record
Abstract
BACKGROUND: Patients with chronic conditions have unique expertise that enhances interprofessional education. Although their active involvement in education is increasing, patients have minimal roles in key educational tasks. A model that brings patients and students together for patient-centred learning, with faculty playing a supportive role, has been described in theory but not yet implemented. AIMS: To identify issues involved in creating an educational intervention designed and delivered by patients and document outcomes. METHOD: An advisory group of community members, students and faculty guided development of the intervention (interprofessional workshops). Community educators (CEs) were recruited through community organizations with a healthcare mandate. Workshops were planned by teams of key stakeholders, delivered by CEs, and evaluated by post-workshop student questionnaires. RESULTS: Workshops were delivered by CEs with epilepsy, arthritis, HIV/AIDS and two groups with mental health problems. Roles and responsibilities of planning team members that facilitated control by CEs were identified. Ten workshops attended by 142 students from 15 different disciplines were all highly rated. Workshop objectives defined by CEs and student learning both closely matched dimensions of patient-centredness. CONCLUSIONS: Our work demonstrates feasibility and impact of an educational intervention led by patient educators facilitated but not controlled by faculty.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".