Knowledge and attitudes of Ball State University pre-service elementary education teachers toward emergency care in the school setting
Bibliographic record
Abstract
Unintentional injuries are the leading cause of death for children aged 5-19 Twenty-two million children are injured each year and approximately one quarter of these injuries occur on school premises. Schools must provide nursing services to children who attend school, but ratios of registered nurses to students is higher than the 1:750 recommended ratio. Current school teachers believe pre-service teachers should be trained in emergency care in teacher training programs. Yet, no research has been conducted to evaluate pre-service teachers’ knowledge and attitudes toward emergency care. The purpose of the study was to investigate pre-service teachers’ knowledge of and attitude toward emergency care in the school setting. A cross sectional group-comparison survey design was used. A 40-item questionnaire was administered to pre-service elementary teachers at Ball State University located in Muncie, IN. The questionnaire consisted of questions from “Emergencies in the school setting: Are public school teachers adequately trained to respond?” and Urban \npublic school teachers’ attitudes and perceptions of the effectiveness of CPR and automated external defibrillators. Sub-group comparisons were made using bivariate and multivariate analyses of similar demographic, attitude, and knowledge questions. Findings indicated that pre-service teachers have a positive attitude toward emergency care, low levels of knowledge about emergency care, and a low level of willingness to provide emergency care in schools. In addition, when comparing pre-service teachers who had received emergency care training to those who did not, a statistically significant difference was found in their knowledge about emergency care. Emergency care training has limited influence on pre-service teachers’ attitudes and willingness to provide care.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".