Incorporating evidence-based principles in medical training. Sharing experience with McMaster
Bibliographic record
Abstract
This article was migrated. The article was marked as recommended. Background: This workshop was the second activity of the collaboration between the McMaster University, Botucatu Medical School- São Paulo State University (UNESP) and Pontifical Catholic University of Minas Gerais – PUC Minas that took place in Botucatu, Brazil between March 27th to 28th 2017. Aims: Its prime purpose was to share with the Brazilian professors and students how to include evidence-based concepts in their daily teaching activities. Methods: The participants were involved and guided in discussions on how to explore evidence-based techniques to improve their understanding and their willingness to include new teaching strategies in the future. Results: A final evaluation survey completed by the participants indicated that they were highly satisfied with the workshop experience and that they gained an enhancement of knowledge about evidence-based medicine. Conclusion: Participants had an increase in their self-confidence to implementevidence-based concepts in their future lecture 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 | 0.014 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.050 | 0.005 |
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".