Which pedagogical principles should clinical teachers know? Teachers and education experts disagree Disagreement on important pedagogical principles
Bibliographic record
Abstract
BACKGROUND: In a previous study, a group of non-clinician medical education experts identified 30 pedagogical principles, knowledge of which might enhance clinical teaching effectiveness. AIMS: To assess expert teachers? perceptions of which basic pedagogical principles, if known and understood, would enhance their teaching effectiveness. METHOD: We conducted an on-line Delphi consensus-building study with 25 expert clinical teachers who rated the importance to teaching effectiveness of each of the 30 principles. RESULTS: There was agreement between clinicians and PhD education experts on the importance of several of the principles but there was major disagreement between the 2 groups for many principles, including those related to assessment and those relevant to clinical teachers? day to day teaching activities. CONCLUSIONS: The lack of concordance between clinical teachers and education experts with respect to how the 30 principles rank in importance may have serious implications for faculty development and for the design, development, and assessment of educational programs. Program directors and curriculum designers should exploit the strengths of both clinician and non-clinician educators to assure the success of educational programs.
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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 | 0.095 | 0.206 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".