The Effect of Educational Intervention in Promoting Safe Behaviors in a Sample of Iranian Primary School Students: An Application of the Health Belief Model
Bibliographic record
Abstract
PURPOSE: Road traffic accidents account for the biggest mortality rate in Iran and children are among the main victims of these accidents. The present study was conducted to assess the effect of an educational intervention based on the Health Belief Model on behaviors associated with obeying traffic regulations in primary school students during the academic year 2014-15 in Khorramabad, Iran. METHODS: The present quasi-experimental has a pre-post-intervention design and was conducted on 106 sixth-grade primary school students selected through multi-stage sampling and divided into an intervention and a control group. The students were assessed before, a week after and two months after the intervention. Data were collected using a two-part questionnaire containing a demographic section and a section with items on the constructs of the Health Belief Model. The data obtained were then analyzed in SPSS-20. RESULTS: The mean scores of perceived susceptibility, perceived severity, perceived benefits, perceived barriers, self-efficacy and cues to action significantly improved in the intervention group compared to the control group (P<0. 01). CONCLUSION: An educational intervention based the Health Belief Model managed to effectively improve behaviors associated with obeying traffic regulations.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".