Dental trauma management awareness of Singapore pre‐school teachers
Bibliographic record
Abstract
The purpose of this study was to assess the awareness of pre-school teachers concerning the management of traumatized teeth. A questionnaire survey of teachers' backgrounds, attitudes/practices and knowledge was distributed to all teachers who attended a dental education program organized by the Singapore Dental Health Foundation. Of a total of 291 teachers surveyed, 29% had received tertiary education, while the rest (71%) had received secondary education. The mean teaching experience was 6.8 +/- 6.9 years. About a quarter had previous experience in handling dental trauma. Sixty-three percent admitted having no knowledge of dental trauma; 79% were unsatisfied with their level of knowledge in this area; 95% were keen to have further education in dental trauma; 65% thought dental trauma emergency should be dealt with as soon as possible. Concerning knowledge, during-office hour emergency services were more familiar (84%) than after-office hour emergency services (15%), as was the concept of management of avulsed teeth (71%) compared to that of fractured teeth (51%). Knowledge about optimal storage media for avulsed permanent teeth was especially poor--being as low as 15%. Using multiple logistic regression analysis, it was found that teaching experience significantly influenced the respondents' self-assessed knowledge and their level of satisfaction with their knowledge (P = 0.012). Teachers with more teaching experience had better knowledge about the replantation of permanent teeth (P = 0.003). It is recommended that public education targeted at teachers should be carried out to increase dental trauma management awareness.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".