Assessment of First Aid knowledge of teachers in primary education
Bibliographic record
Abstract
Background: First Aid provision represents a moral obligation as well as a social necessity in all public settings and it is particularly important in the field of Primary Education. Although there is a significant improvement in the picture in relation to the attendance of First Aid programs in Greece, teachers do not seem to have sufficient knowledge and ability to deal with accidents in the school environment. This study examined the knowledge of First Aid of teachers in Primary Education. Material and methodology: This study was conducted on teachers in primary education, teaching in both the public and private sector. Data collection was performed online, through the Google Forms platform, within a 35 months’ time framework. Statistical analysis of data was performed using R-statistics. Results: Data collected from 407 teachers in primary education teaching either in the public or private sector were analyzed and found that their knowledge of First Aid was insufficient. We also found that there is no statistically significant difference between the responses of teachers in primary education regarding teaching in public or private sector. Conclusions: This study confirms previous findings that underline that teachers are poorly trained in First Aid and furthermore lack the necessary self-confidence that one should possess when performing First Aid. Therefore, it reveals the need to conduct further similar research aimed at investigating the level of knowledge and ability to apply First Aid practices by teachers nationwide and on a larger scale. It also stresses out the need for proposals’ submission for more effective teachers’ education in First Aid.
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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 | 0.008 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".