Dentists’ attitudes and practices toward evidence-based dentistry: a systematic review
Bibliographic record
Abstract
INTRODUCTION AND AIMS: Considering that attitudes toward evidence-based dentistry (EBD) may predict implementation behaviors, the objective of this systematic review was to synthesize and evaluate the existing evidence related to dentists' attitudes and practices toward EBD. METHODS: We included primary studies that collected information from interviews, questionnaires, or conversation sessions with dentists. The following sources were searched: Cochrane Central Register of Controlled Trials, Embase, PubMed, Scopus, and Web of Science, in addition to gray literature. The included studies were appraised according to the assessment tools recommended by JBI for qualitative and quantitative observational studies. Data were collected in standardized tables and descriptively synthesized. RESULTS: The selection process resulted in 36 included studies. Dentists share positive opinions about EBD and predominantly report willingness to learn or adopt these practices. Despite high methodological risks and significant heterogeneity, the results collected in this review indicated that scientific journals, clinical practice guidelines, and trusted colleagues are generally perceived as influential and useful by dentists, who highly consulted these information sources. CONCLUSION: Despite supportive attitudes toward EBD, very low certainty exists about actual EBD-related practices.
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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 | 0.026 | 0.099 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".