Community-Based Clinical Traineeships: Exploring Physicians’ Perceptions on the Transferability of Learning to Practice
Bibliographic record
Abstract
Literature identifies several ways in which a traineeship into a non-traditional community-based clinical setting might positively impacts medical trainees. However, little is known about physicians’ ability to transfer the learning gained from such experience into other clinical contexts. This qualitative study explores, from physicians' perspectives, the application of learning gained from a traineeship within La Maison Bleue, a community-based primary care organization in Montreal, Quebec, Canada, designed for women and families experiencing social vulnerability. The study is based on 12 semi-structured interviews with primary care physicians (n=10) and residents (n=2) who completed a medical traineeship into this setting. NVivo software was used to support thematic analysis. Results show that most participants aimed to apply the learnings gained from their experience, despite organizational and structural barriers often impeding their efforts. It is thus primarily the learnings relating to the relational and patient-centered approach, which the doctor can control on a personal or an interpersonal level, that are effectively actualized in practice. Facilitating factors were perceived more on the human level, but ultimately had only a marginal effect on physicians' actual ability to apply learning. The study provides decision-makers with concrete avenues for action to better support physicians in their willingness to practice medicine differently. By highlighting these findings, the study underscores the ethical and political responsibility of healthcare decision-makers in realizing the transformational potential of medical education.
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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 | 0.014 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".