How learning-centred beliefs relate to awareness of educational identity and mission: An exploratory study among medical educators
Bibliographic record
Abstract
INTRODUCTION: Although learning-centred education would be most effective if all medical educators held learning-centred beliefs, many educators still hold teaching-centred beliefs. A previously developed theoretical model describes a relationship between beliefs, educational identity and 'mission,' meaning that which inspires and drives educators. To increase our understanding of why educators hold certain beliefs, we explored the empirical relationship between educators' beliefs and their awareness of their educational identity and mission. METHODS: A qualitative study was conducted using in-depth interviews with medical educators. We performed a deductive thematic analysis employing two existing models to examine educators' beliefs about teaching and learning and their awareness of their educational identity and mission. RESULTS: aware of both their identity and mission displayed teaching-centred beliefs, educators aware of their identity and mission displayed learning-centred beliefs. Those who were aware of their identity, but not their mission, displayed either teaching- or learning-centredness. CONCLUSIONS: Medical educators' awareness of identity and mission are related to their beliefs about education. Further research is needed into whether beliefs can change over time by increasing identity and mission awareness.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".