Obstacles to Implementing Evidence‐Based Dentistry: A Focus Group‐Based Study
Bibliographic record
Abstract
In many countries, questions have been raised about the use of evidence-based practice (EBP) in oral health care. The call for an increase in EBP seems to face many obstacles. Only limited empirical studies address these obstacles. We present a qualitative study that explores the obstacles that Flemish (Belgian, Dutch-speaking) dentists experience in the implementation of EBP in routine clinical work. We collected data from discussions in focus groups. Seventy-nine dentists participated. The data were analyzed using constant comparative analysis. Three major categories of obstacles were identified. These categories relate to obstacles in 1) evidence, 2) partners in health care (medical doctors, patients, and government), and 3) the field of dentistry. Our findings suggest that educators should provide communication skills to aid decision making, address the technical dimensions of dentistry, promote lifelong learning, and close the gap between academics and general practitioners (dentists) in order to create mutual understanding. The obstacles identified are considered useful to support future quantitative research that can be generalized to a broader group.
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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 | 0.035 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.011 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".