Supporting Resident Wellness Through Reflection on Professional Identity
Bibliographic record
Abstract
Background: Interventions to address distress among medical trainees often include reflective practice, as well as peer support. Few, however, have emphasized the role of professional identity formation, increasingly recognized as critical to wellness. The structural aspects of curricular interventions have also received little attention. A novel curriculum was therefore designed and evaluated with the goal of understanding how best to support resident wellness through reflection on professional identity. Materials and Methods: The curriculum consisted of 8 2-hour sessions, each focusing on a theme commensurate with residents’ professional identity at the time of its delivery. Two Family Medicine sites at the University of Toronto participated, with residents divided into small groups by residency year. Qualitative data were collected through feedback forms, and resident and faculty focus groups, transcripts of which were subjected to pragmatic thematic analysis. Results: Four major themes were developed relating to 1) the curriculum's ability to support resident wellness, 2) the importance of protecting reflection, 3) the impact of participants’ professional developmental stage, and 4) the critical role of facilitators. Conclusions: A curriculum encouraging reflection on professional identity appears to support resident wellness. To optimize impact, structural factors such as robust curricular integration, confidentiality and group member continuity, require care.
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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 | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".