The expert patient as teacher: an interprofessional Health Mentors programme
Bibliographic record
Abstract
BACKGROUND: To meet future health care needs, medical education must increase the emphasis on chronic illness care, interprofessional teamwork, and working in partnership with patients and families. One way to address these needs is to involve patients as teachers in longitudinal interprofessional educational programmes grounded in principles of patient-professional partnerships and shared decision-making. CONTEXT: The University of British Columbia has a history of initiatives designed to bring patient and community voices into health professional education. Increasing opportunities for interprofessional education has become important because of accreditation requirements. INNOVATION: We describe preliminary findings from a 3-year pilot of an interprofessional Health Mentors programme, an elective patient-as-teacher initiative in which groups of four students from different disciplines learn together, with and from a mentor with a chronic condition (an 'expert by experience') over three semesters. The goals, achieved through six themed meetings and a symposium, are to learn about living with a chronic condition from the patient's perspective and to develop interprofessional competencies. Groups are given suggested topics for each meeting, but function as self-managed learning communities, and are encouraged to explore their own questions. Faculty members support direct learning between students and mentors through setting broad objectives and responding to the student reflections written after each group meeting. Students and mentors rate the programme highly, and a wide range of important learning outcomes have been documented. Medical education must increase the emphasis on chronic illness care, working in partnership with patients IMPLICATIONS: Key characteristics, generalisable to other educational programmes, include the role of faculty staff in supporting learning between students and patients, a minimalist structure to promote ownership and creativity, and flexible delivery.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.018 | 0.003 |
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".