Faculty perspectives on supervising endocrinology residents in the longitudinal clinic setting
Bibliographic record
Abstract
Introduction: Longitudinal clinic supervision is complex but key to training residents, such as endocrinology residents. Supervisors report difficulties providing learners with the right balance of supervision and autonomy. Cognitive apprenticeship theory applies to teaching in workplace-based settings, such as the endocrinology longitudinal clinic. While this theory identifies effective strategies for supervising medical students in longitudinal settings, little is known about how and why supervisors adapt their strategies when offering longitudinal supervision to endocrinology residents. Objective: To understand supervisors' perspectives on supervision of endocrinology residents over time in the longitudinal clinic. Methods: This is a qualitative study set in the endocrinology division of a Canadian multi-site academic centre in 2023-2024 using semi-structured interviews of endocrinology longitudinal clinic supervisors. Transcripts were analyzed using inductive and deductive thematic analysis, with cognitive apprenticeship theory as a sensitizing concept. Results: Five main themes were identified articulating how participants perceive their supervision and its changes over time. These include supervisors treating residents as colleagues and making clinic experiences reflect real life when possible. Supervisors also tailor clinic experiences to resident needs and career goals. They provide residents with support but also autonomy to execute their own management plans even if discordant with that of supervisors provided that patient safety is maintained. Similarly, supervisors encourage residents to take patient ownership. Together, these strategies enable supervisors to support resident development into independent professional endocrinologists. Conclusion: Overall, supervisors try to encourage residents to self-regulate their learning, but also offer support and autonomy to enhance their longitudinal growth and development.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| 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".