Exploring the role of classroom-based learning in professional identity formation of family practice residents using the experiences, trajectories, and reifications framework
Bibliographic record
Abstract
BACKGROUND: Classroom-based learning such as academic half day has undervalued social aspects. We sought to explore its role in the professional identity development of family medicine residents. METHODS: In this case study, residents and faculty from four training sites in the University of British Columbia Department of Family Practice were interviewed. The "experiences, trajectories, and reifications (ETR) framework" was used as a sensitizing tool for modified inductive (thematic) analysis of the transcripts. RESULTS: Classroom-based learning provided a different context for residents' interpretation of their clinical experiences, characterized as a "home base" for rotating urban residents, and a connection to a larger academic community for residents in rural training sites. Both these aspects were important in creating a positive trajectory of professional identity formation. Teaching directed at the learning needs of family physicians, and participation of family practice faculty as teachers and role models was a precipitation of a curriculum "centered in family medicine." Interactions between family medicine residents and faculty in the classroom facilitated the necessary engagements to reify a shared understanding of the discipline of family practice. CONCLUSIONS: Classroom-based learning has substantial impact on professional identity formation at an individual and collective level.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".