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Record W2166768014 · doi:10.1080/15389588.2010.532523

Prediction of Seat Belt Use Among Iranian Automobile Drivers: Application of the Theory of Planned Behavior and the Health Belief Model

2011· article· en· W2166768014 on OpenAlexaff
Sedigheh Sadat Tavafian, Teamur Aghamolaei, David Gregory, Abdoulhossain Madani

Bibliographic record

VenueTraffic Injury Prevention · 2011
Typearticle
Languageen
FieldEngineering
TopicTraffic and Road Safety
Canadian institutionsUniversity of Lethbridge
Fundersnot available
KeywordsSeat beltTheory of planned behaviorOccupational safety and healthPoison controlHuman factors and ergonomicsInjury preventionSuicide preventionEngineeringForensic engineeringPsychologyApplied psychologyComputer securityEnvironmental healthComputer scienceAutomotive engineeringMedicineControl (management)Artificial intelligence

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.020
GPT teacher head0.221
Teacher spread0.201 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations65
Published2011
Admission routes1
Has abstractyes

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