The Incidence Rate and Causes of Accidents among the Students of Shiraz Guidance Schools
Bibliographic record
Abstract
Abstract: \nIntroduction: The great population of about 16 million students in our country and their health problems require more attention to the health of students. Students due to low age and consequently lack of adequate experience are exposed to various types of accidents in schools. Accidents are the leading cause of death in 5 to 17 year old children. The aim of this study was to determine the incidence rate of accidents and their causes in Shiraz guidance schools. \nMethod: In this study about 7137 guidance schools students in four educational districts of Shiraz were randomly selected. Data were collected by a questionnaire. \nResults: The total accident incidence rate was 1.2% with 0.6% and 1.49% in girls and boys respectively. The highest incidence rate was in winter (0.6%). Among all, 17 injured students (19.8%) were hospitalized. The highest rate of injuries was found in limbs. \nConclusion: The total accident incidence rate in guidance schools of Shiraz was 1.2% that is significantly lower comparing to what have been reported in Canada (P<0.001). The highest rate of accidents in the students of grade one requires special attention to younger students in order to lead them toward low risk and healthy activities. Moreover providing adequate educational environment and training students for proper behavior at school during the break time may lead to a considerable decrease in the rate of accidents in schools. \n \nKeywords: Accidents, Incidence rate, Guidance school, Injury
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".