Best Practices for Dental Sealants in Community Service‐Learning
Bibliographic record
Abstract
Community service-learning (CSL) in predoctoral dental education might be an effective tool for increasing sealant use by dentists--thus benefitting underserved children while facilitating students' learning of a clinical procedure in a real-life setting. This study reviewed the scientific literature on this topic in order to 1) evaluate the reasons for low sealant use among dentists, 2) consider important aspects of sealant use in community settings, and 3) identify best practices to use as guidelines for CSL regarding sealant use. As background, the MEDLINE database was searched with the key words "dental sealants" for human and laboratory studies in the English language. A total of 205 relevant articles were identified and overviewed. We found that the low use of sealants relate to dentists' orientation toward restorations rather than prevention, distrust in sealant treatment, lack of confidence in caries risk assessment, and concern about sealing over caries. The aspects to consider in the CSL projects are acquisition of knowledge and necessary skills of operators, cost-benefit approach to sealant placement, and meticulous sealant placement procedures, including the necessity for a short-term recall.
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 | 0.012 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".