Teacher self-efficacy (TSE) of recently graduated emergency medicine physicians and the factors influencing TSE
Bibliographic record
Abstract
This study examines the Teaching Self-Efficacy (TSE) of Emergency Medicine (EM) physicians who graduated from the EM residency programs accredited by the Canadian Royal College of Physicians from 2008-2017, and evaluates the factors influencing these TSE beliefs. Eighty EM physicians participated in this study, providing data on their TSE beliefs using the Emergency Physician Teacher Self-Efficacy Scale (EP-TSES). Factors affecting TSE were assessed using the Influencing Factors of EM Physician TSE questionnaire. These factors include mastery experience, working experience, feedback on teaching performance, interpersonal support from colleague physicians, interpersonal support from department leadership, vicarious experiences, formal teaching training, and informal teaching training. The study also explores other possible factors, pertaining to the clinical environment, which could influence the TSE beliefs. Both instruments were validated before use in this study. Correlation analysis, and multiple regression analysis were conducted to answer the research questions. The results reveal that the mean EP-TSES score of participating physicians is 35.1 out of 50. The correlation analysis shows the EP-TSES score has a significant positive correlation with mastery experience, vicarious experience, informal teaching training, feedback on teaching performance, and more shifts with learners. The regression analysis reveals that mastery experience is the strongest predictor of TES of EM physicians, followed by vicarious experience, informal teaching training, and feedback on teaching performance. This study suggests that stakeholders in training EM physicians should consider employing strategies that foster TSE, to improve teaching and learning outcomes, and, by extrapolation, to improve healthcare outcomes.
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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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".