Developing a Training Program to Improve Supervisor-Resident Relationships, Step 1: Defining the Types of Issues
Bibliographic record
Abstract
BACKGROUND: By some estimates, the teacher-learner relationship explains roughly half of the variance attributed to the effectiveness of teaching. Despite this, relationships largely have been ignored in the educational literature. PURPOSE: This qualitative pilot study sought to identify factors in the supervisor-resident relationship that hinder learning among University of Toronto psychiatry residents. METHOD: Thirteen postgraduate-year residents in Years 2-5 and their supervisors were interviewed regarding interactions that either assisted or adversely affected learning. RESULTS: Qualitative analysis of the interview data led to the identification of 5 types of issues affecting the supervisory relationship: goals and individual differences, communication and feedback, power and rivalry, support and collegiality, and role modeling and expertise. Face validity was supported when typed anonymous written feedback obtained from annual supervisor evaluations also could be organized into the 5 categories. CONCLUSIONS: Recognition of the types of interpersonal interactions that assist or hinder learning may contribute to enhanced teaching effectiveness.
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".