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Record W2130660101 · doi:10.12927/whp.2013.22066

Evaluation of a School-Based Intervention to Reduce Traffic-Related Injuries among Adolescents in Beijing

2010· article· en· W2130660101 on OpenAlexvenueno aff
Chen Zhang, Yan Hong, Xiurong Liu, Yuqing Li, Jun Yang

Bibliographic record

VenueWorld health & population · 2010
Typearticle
Languageen
FieldMedicine
TopicInjury Epidemiology and Prevention
Canadian institutionsnot available
Fundersnot available
KeywordsBeijingIntervention (counseling)Environmental healthMedicineSuicide preventionInjury preventionPoison controlOccupational safety and healthMedical emergencyGeographyChinaNursing

Abstract

fetched live from OpenAlex

OBJECTIVE: Millions of adolescents are killed or injured in traffic accidents on the world's roads each year, but data on traffic-injury prevention programs targeting adolescents are limited, especially from developing countries. The aim of the study was to evaluate the effectiveness of a traffic-injury prevention program targeting adolescents in China. METHODS: We conducted a school-based traffic-safety intervention program with 2,759 students in two middle schools and two high schools in Beijing. An open-cohort, pre-post design with intervention and control groups was used to evaluate the intervention effect. RESULTS: Compared with the control group, the intervention group reported a significant increase in knowledge and awareness of traffic safety and a decrease in self-reported unsafe traffic behaviours. Students in middle school and girls reported better intervention effects than their high school and male counterparts. CONCLUSION: This study suggests that school-based traffic-injury prevention programs may increase participants' knowledge of traffic signs and awareness of traffic safety issues. The high traffic mortality in China, particularly in Chinese adolescents, suggests that more age- and culture-appropriate traffic safety promotion programs are needed.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.082
Threshold uncertainty score0.859

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.049
GPT teacher head0.428
Teacher spread0.379 · 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 teacher head, 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

Citations11
Published2010
Admission routes1
Has abstractyes

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