Integration of an evidence‐based caries management approach in dental education: The perspectives of dental instructors
Bibliographic record
Abstract
PURPOSE/OBJECTIVES: Evidence-based caries management (EBCM) has developed into an internationally recognized tool for integration of comprehensive non-surgical caries treatment in dental education. However, uptake of the EBCM approach remains uneven across Canadian dental schools. Our project sought to understand how dental instructors perceive the challenges and solutions to the integration of the EBCM approach in undergraduate clinical education. METHODS: Using a qualitative descriptive design, we recruited a purposeful sample of clinical instructors supervising undergraduate dental students in caries-related dental care. Semi-structured, online interviews focused on the main characteristics of EBCM. Interviews were analyzed using the awareness, desire, knowledge, ability, and reinforcement (ADKAR) change management model to understand challenges with EBCM implementation in undergraduate education. The analysis process started with verbatim transcription; then, transcripts were coded deductively based on the interview guide and the ADKAR model domains, and inductively to generate emergent codes. Finally, thematic analysis was used to develop themes and subthemes. RESULTS: We interviewed 11 dental instructors with a wide range of clinical experience. Our results show that participants had sufficient awareness regarding the need for the EBCM approach and portrayed a strong desire to participate in bringing curricular changes. Knowledge and ability of participants depended on their training, experience, and involvement in continuing education courses. A lack of standardized caries management practices, less chairside time, and poor remuneration for instructors were major barriers in EBCM clinical implementation. Potential solutions suggested included providing continuing education courses, credits for students for non-surgical caries management, and remunerating instructors for implementation. CONCLUSIONS: In conclusion, most participants were aware of the need for a substantive change toward EBCM and demonstrated the desire to participate and improve its implementation. Our analysis showed that to facilitate full integration of the EBCM approach into the undergraduate dental clinics, organizational focus needs to be placed on the individual's knowledge and ability, with tailored efforts toward reinforcement.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.021 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".