Impact of a postcrash first aid educational program on knowledge, perceived skills confidence, and skills utilization among traffic police officers: a single-arm before-after intervention study
Bibliographic record
Abstract
Abstract Background An overwhelming proportion of road traffic deaths and injuries in low- and middle-income countries (LMICs) occur in prehospital environments. Lay first responders such as police officers play an important role in providing initial assistance to victims of road traffic injuries either alone or in collaboration with others. The present study evaluated a postcrash first aid (PFA) educational program developed for police officers in Tanzania. Method A 16-h PFA educational program was conducted in Dar es Salaam, Tanzania, for 135 police officers. Participants completed training surveys before, immediately and 6 months after the training (before, N = 135; immediately after, N = 135; after 6 months, N = 102). The primary outcome measures were PFA knowledge, perceived skills confidence, and skills utilization. Parametric and nonparametric tests were used to analyse changes in outcome. Results The mean PFA knowledge score increased from 44.73% before training (SD = 20.70) to 72.92% 6 months after training (SD = 18.12), p < .001, N = 102. The mean PFA perceived skills confidence score (measured on a 1–5 Likert scale) increased from 1.96 before training (SD = 0.74) to 3.78 6 months after training (SD = 0.70), p < .001, N = 102. Following training, application of the recovery position skill (n = 42, 46%) and application of the bleeding control skill (n = 45, 49%) were reported by nearly half of the responding officers. Less than a quarter of officers reported applying head and neck immobilization skills (n = 20, 22%) following training. Conclusion A PFA educational program has shown to improve police officers’ knowledge and perceived skills confidence on provision of first aid. However qualitative research need to be conducted to shed more light regarding reasons for low utilization of trained first aid skills during follow-up.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".