SKILLS AND SAFETY: INVESTIGATING FIRST AID TRAINING IN STUDENTS WITH SPECIAL EDUCATIONAL NEEDS
Bibliographic record
Abstract
educational needs and examine the retention of their first aid knowledge and skills over time. Methods: We conducted an experimental longitudinal study in randomly chosen schools in Thesprotia, Greece. Twelve children, aged 4-8, and with special educational needs, underwent first aid training. We assessed their knowledge and skills using a questionnaire and specific scenarios one day before, one day after, and two- and seven-months post-training. Results: Before the training, the children did not answer the questionnaire correctly. However, after the training, all students got every question right. In the 2-month follow-up, their responses showed that the training kept their improvements. In the 7-month follow-up, there was a drop in the percentage of correct answers compared to the immediate post-test at 2 months. Based on the scenario-based assessment, before the training, the children did not know how to respond to a choking incident, but after training, all students reacted correctly. However, after two and seven months, students with special educational needs students had difficulty applying first aid skills correctly to a choking incident. Conclusions: Children with special educational needs can learn first aid, but they tend to forget some of this information after two months. However, additional research is needed to confirm these observations and explore similar studies involving children of different ages and various levels of special educational needs. Article visualizations:
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".