Knowledge changes after applying evidence‐based dentistry educational interventions to dental students: A systematic review
Bibliographic record
Abstract
PURPOSE: To critically evaluate the evidence on knowledge changes observed after the application of evidence-based dentistry (EBD) educational interventions to dental students. METHODS: We included studies that assessed EBD knowledge after applying educational interventions to undergraduates. Studies that evaluated post-graduate students or professionals, that exclusively described educational interventions, programs, or the application of curriculum revisions were excluded. Electronic databases (PubMed, Embase, Scopus, and Web of Science), unpublished gray literature, and manual searches were performed. Data concerning "perceived" and "actual knowledge" was extracted. The quality of the studies was appraised according to the Mixed Methods Appraisal Tool. RESULTS: The 21 selected studies enrolled students at different stages, and the intervention formats were diverse. The educational interventions could be categorized into three modalities, that is, regular, EBD-focused disciplines or courses, and other educational interventions including one or more of the EBD principles, methods, and/or practices. Despite the format, knowledge was generally improved after the implementation of educational interventions. Overall, perceived and actual levels of knowledge increased considering EBD general concepts, principles, and/or practices, and concerning the "acquire" and "appraise" skills. Among the selected studies, two were randomized controlled trials, while most were non-randomized or descriptive studies. CONCLUSIONS: EBD-related educational interventions seem to improve dental students' perceived and actual knowledge, according to literature with a high risk of bias. Therefore, more complete, methodologically rigorous, and longer-term studies are still recommended to confirm and expand the current knowledge.
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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 | 0.019 | 0.087 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".