Teachers' knowledge of emergency management of traumatised teeth in preschools : scientific
Bibliographic record
Abstract
Dental trauma remains one of the major oral health problems in childhood and is the cause of much pain and distress. It may occur as a result of a sports mishap, an altercation or a fall while playing inside the school premises. Prompt and appropriate management of traumatised teeth is essential for a good prognosis of an injured tooth. Aim and objective : The aim of the study was to assess teachers' knowledge of emergency management of traumatized teeth in early childhood developmental centres (ECDCs). Method : A cross-sectional self-administered questionnaire was used to collect data among teachers in ECDCs in Hillbrow and Berea suburbs, Johannesburg, South Africa. Results and Conclusion : Almost all respondents (98.1%) were female; 59.6% were between 20 and 29 years of age. Almost a quarter of the centres were not registered and 39.1% of the school teachers were not formally qualified as ECDCs teachers. A small percentage (11.5%) received dental emergency training as a part of their school health education programs. Knowledge of ECDCs teachers on the emergency management of traumatized teeth appeared inadequate; in the event of emergency dental trauma, substantial number of teachers would not be able to respond appropriately. All teachers should have training on basic management of dental trauma.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".