Prediction of Seat Belt Use Among Iranian Automobile Drivers: Application of the Theory of Planned Behavior and the Health Belief Model
Bibliographic record
Abstract
OBJECTIVE: Seat belt use plays an important role in traffic safety by reducing the severity of injuries and fatality rates during vehicle accidents. The aim of this study was to investigate predictors of self-reported seat belt use in a sample of automobile drivers in Bandar Abbas, Iran. The theory of planed behavior and the health belief model served as the conceptual framework for the study. METHODS: The convenience sample consisted of 284 eligible automobile drivers who frequented 8 petrol stations in different geographical areas of the city. Of the drivers approached to participate in the study, 21 declined to take part in the study and 12 other questionnaires were incomplete. Thus, a total of 251 questionnaires were analyzed (response rate=88.4%). A self-administered questionnaire including demographic characteristics and items arising from the theory of planed behavior and health belief model constructs were used to collect data. Data were analyzed using SPSS 16 (version 16, Chicago, IL, USA). RESULTS: The subjects' mean age was 31.6 years (SD=8.7), mostly male (72.9%), and 53.4 percent of them reported that they used their seat belt "often." Multiple regression analyses revealed that from the theory of planed behavior, attitude, subjective norms, and perceived behavioral control significantly predicted intention to use a seat belt (R2=0.38, F=51.1, p<.001); and subjective norms, perceived behavioral control, and behavioral intention significantly predicted seat belt use (R2=0.43, F=45.7, p<.001). Arising from the health belief model, perceived benefits and perceived barriers significantly predicted seat belt use (R2=0.39, F=26.2, p<.001). CONCLUSION: This study revealed that automobile drivers who perceived more subjective norms, more behavioral control, greater intention to use seat belts as well as more benefits and fewer barriers were more likely to use their seat belts.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".