Concordance of judgement: A tool to foster the development of professionalism in dentistry
Bibliographic record
Abstract
INTRODUCTION: Developing professionalism notably involves learning how to make professional judgements in ambiguous situations. The Concordance of Judgement Test (CJT) is a learning tool that was proposed to develop professionalism competencies, but it was never performed in dentistry or used with a synchronous methodology. The present study evaluated the feasibility of the use of CJT in the context of dental education, to foster professionalism and stimulate reflexivity and discussion. MATERIALS AND METHODS: After different steps of optimization, a questionnaire presenting 12 vignettes was submitted to 33 Canadian students. Second, after an additional optimization, a questionnaire of 7 vignettes was submitted to 87 French students. An immediate educational feedback was proposed after each vignette to promote reflexivity and discussions during the experience. RESULTS: The overall experience of the students was reported as good, thanks to the feedback of real-life situations. This promoted reflexivity and stimulated discussion between students and educators regarding professionalism issues. The students considered CJT as a relevant and well-adapted tool, and reported positive feelings regarding the inter-university aspect of the activity. The mean score of the panel members was close to 80/100 and the mean score of the students was 5 to 10 points lower, which is in agreement with docimological performance. CONCLUSION: The results suggested that the use of CJT in a synchronous way was a feasible and relevant tool to motivate the students to improve their professionalism, and to stimulate their reflexivity and discussion. The students reported positive experience with CJT, and we believe that this tool can be integrated in the dental curriculum.
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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 | 0.016 | 0.077 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".