Development of an evidence-based dentistry course for dental students and its effect on their awareness, attitude and self-assessed knowledge
Bibliographic record
Abstract
Background and Aim: The aim of evidence-based dentistry (EBD) is to make best clinical decisions with the judicious and systematic uses of the best scientific evidences. The objective of the present study was to develop an EBD course for dental students, and to assess the effects of participation in this course on awareness, attitude and self-assessed knowledge of the students. The students’ satisfaction with the course was also assessed.\nMaterials and Methods: In this controlled interventional study, 65 dental students in two main state Dental Schools in Tehran were selected and were divided into two groups: 43 students in the intervention group and 22 in control group. An EBD course was developed and presented for the intervention group. The scores of awareness, attitude and self-assessed knowledge were determined in both groups before and after participation in the course by means of a questionnaire. The post-test questionnaire in the intervention group had also some questions about course evaluation. Student’s t-test and linear regression model served for statistical analysis. Statistical significance was set at 0.05.\nResults: The students participating in the EBD course showed more improvements regarding total scores of awareness, attitude and self-assessed knowledge when compared to control individuals (P < 0.0001, P < 0.005 and P < 0.0001, respectively). Of different studied factors, only students’ gender showed significant influence on the knowledge scores changes (P = 0.042).\nConclusions: The developed EBD course seemed to be effective to improve the participants’ awareness, attitude and self-assessed knowledge regarding evidence-based concepts. The results call for more emphasis on EBD in dental curriculum through designing courses on the subject.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".