Professional Identity Formation: A Role for Patients as Mentors
Bibliographic record
Abstract
PURPOSE: Medical education should foster professional identity formation, but there is much to be learned about how to support learners in developing their professional identity. This study examined the role that patients can play in supporting professional identity development during the University of British Columbia Interprofessional Health Mentors Program (HMP), a longitudinal preclinical elective in which patients, or their caregivers, act as mentors and educate students about their lived experience of a chronic condition or disability. METHOD: The authors interviewed 18 medical residents in 2016, 3 to 4 years after they completed the HMP. Professional identity was explored by asking participants how the HMP had influenced their ideas about the ideal physician and the kind of doctor they aspire to become. The authors analyzed the data using the identify status paradigm as a conceptual framework. RESULTS: The authors identified 7 themes: patient as more than disease, patient as autonomous, patient as expert, doctor as partner, doctor as collaborator, self-aware doctor, and empathic doctor. They found firm commitments to patient partnership, interprofessional collaboration, and holistic care for patients rooted in the exploration of professional values that was prompted by patient mentors during HMP. CONCLUSIONS: Patient mentors can help medical students begin to construct their professional identity during the preclinical period by supporting exploration of and commitment to the professional values that society expects of physicians.
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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 | 0.025 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.011 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 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".