Faculty development for teaching and evaluating professionalism: from programme design to curriculum change
Bibliographic record
Abstract
INTRODUCTION: The recent emphasis on the teaching and evaluation of professionalism for medical students and residents has placed significant demands on medicine's educational institutions. The traditional method of transmitting professional values by role modelling is no longer adequate, and professionalism must be taught explicitly and evaluated effectively. However, many faculty members do not possess the requisite knowledge and skills to teach this content area and faculty development is therefore required. PROGRAMME DESCRIPTION: A systematic, integrated faculty development programme was designed to support the teaching and evaluation of professionalism at our institution. The programme consisted of think tanks to promote consensus and "buy-in", and workshops to convey core content, examine teaching strategies and evaluation methods, and promote reflection and self-awareness. PROGRAMME EVALUATION: The programme was evaluated using a CIPP (context, input, process, product) analysis. The institution supported this initiative and local expertise was available. A total of 152 faculty members, with key educational responsibilities, attended 1 or more faculty development activities. Faculty participation resulted in agreement on the cognitive base and attributes of professionalism, consensus on the importance of teaching and evaluating professionalism, and self-reported changes in teaching practices. This initiative also led to the development of new methods of evaluation, site-specific activities and curriculum change. DISCUSSION: A faculty development programme designed to support the teaching and evaluation of professionalism can lead to self-reported changes in teaching and practice as well as new educational initiatives. It can also help to develop more knowledgeable faculty members, who will, it is hoped, become more effective role models.
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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 | 0.127 | 0.141 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".