Beliefs, identities and educational practice: a Q methodology study of general practice supervisors
Bibliographic record
Abstract
INTRODUCTION: Quality of supervisory practices varies. According to the integrative model of behaviour prediction, supervisors' beliefs may influence practice. This study aimed to examine the belief profiles of general practice supervisors, and their potential relationship with supervisory practice. METHODS: A cross-sectional study was conducted using Q-methodology to explore supervisors' beliefs and the Maastricht Clinical Teaching Questionnaire to measure self-reported supervisory practice. RESULTS: One-hundred and thirty-nine supervisors took part (76%). The most common belief profile (36.7%) comprised a proactive view of supervisors' roles, strong self-efficacy beliefs and awareness of university norms. It revealed merged identities as clinicians and teachers. The second profile (18.0%) included a belief that supervision essentially involved sharing one's experience, uncertainty about the impact of supervision and about university norms. This profile was consistent with a pre-eminence of supervisors' identities as clinicians. Supervisors with merged identities were more likely to have more experience as supervisors and to engage in other teaching activities. Differences in self-reported supervisory practice were observed but did not reach statistical significance (P = 0.053). CONCLUSIONS: Supervisors' beliefs reveal differences in the way they manage their multiple professional identities. Further research should be conducted into whether these differences are developmental and if so how development occurs.
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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 | 0.012 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".