Evaluation of elementary school teachers' knowledge and attitudes about immediate emergency management of traumatic dental injuries.
Bibliographic record
Abstract
PURPOSE: To investigate teachers' knowledge and attitudes about emergency management of traumatic dental injuries (TDIs) in children. MATERIALS AND METHODS: A total of 764 teachers from 13 elementary schools were included in the study. Data were collected using a self-reporting questionnaire in which teachers were asked about demographic information, previous experience with dental trauma, first-aid training, knowledge of emergency management and how they would respond to two hypothetical TDI cases. RESULTS: Of the 764 participants, 550 (71.4%) returned the questionnaire; of these, 309 (56.2%) were female and 241 (43.8%) were male. While 297 teachers reported having had first-aid training, only 13 (4.4%) of them reported emergency management of TDIs being covered in this training. Less than half of respondents (47.5%, n = 261) correctly answered the question on the appropriate response to a TDI involving a fractured tooth and only one-quarter of respondents (25.4%, n=140) correctly answered the question on the appropriate response to a TDI involving an avulsed tooth. CONCLUSION: The results of this study demonstrated teachers' low level of knowledge about the emergency treatment of TDIs in schoolchildren, suggesting that educational programmes are needed to improve proper emergency management of TDIs by teachers.
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 | 0.002 | 0.007 |
| 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 | 0.000 | 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".