From good to great: learners’ perceptions of the qualities of effective medical teachers and clinical supervisors in psychiatry
Bibliographic record
Abstract
BACKGROUND: The shift in postgraduate medical training towards a competency-based medical education framework has inspired research focused on medical educator competencies. This research has rarely considered the importance of the learning environment in terms of both setting and specialty-specific factors. The current study attempted to fill this gap by examining narrative comments from psychiatry faculty evaluations to understand learners' perceptions of educator effectiveness. METHODS: = 324) from McMaster University. Evaluations were provided for medical teachers and clinical supervisors in classroom and clinical settings. Narrative comments were analyzed using descriptive qualitative methodology by three independent reviewers to answer: "What do undergraduate and postgraduate medical learners perceive about educator effectiveness in psychiatry?" RESULTS: Narrative comments were provided on 270/324 (83%) faculty evaluation forms. Four themes and two sub- themes emerged from the qualitative analysis. Effective psychiatry educators demonstrated specific personal characteristics that aligned with previous research on educator effectiveness. Novel themes included the importance of relationships and affective factors, including learner security and inspiration through role modeling. CONCLUSION: Contemporary discussions about educator effectiveness in psychiatry have excluded the dynamic, relational and affective components of the educational exchange highlighted in the current study. This may be an important focus for future educational research.
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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 | 0.009 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".