Introducing Dental Students to Evidence‐Based Decisions in Dental Care
Bibliographic record
Abstract
Practicing evidence-based dentistry is a process of lifelong and self-directed learning. Teaching evidence-based dentistry to dental students is the key to increasing the uptake of evidence-based treatments and practices in dentistry. This article describes the procedures undertaken to teach undergraduate dental students at the University of Toronto Faculty of Dentistry how to produce systematic reviews as a module in clinical epidemiology. Nine selected reports have been summarized as examples of the outputs of this module. At the end of the module, students are asked to participate in a survey and anonymously fill out a questionnaire to evaluate the module. Students' evaluation of the module in the 2005-06 (n= 64) and 2006-07 (n=57) academic years were extracted for data analysis. Overall, the majority of students found the module an enjoyable way of learning that has improved their ability to gather information, apply existing evidence to a clinical question, evaluate information, and further develop their communication skills. This module was also effective in raising students' awareness of the importance of evidence-based clinical practice. It is essential to establish the fundamentals of evidence-based practice during the undergraduate curriculum to assist dental students in learning the skills to practice evidence-based dentistry.
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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 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 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".