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Record W2122439761

Effect of legislation on the use of bicycle helmets.

2002· article· en· W2122439761 on OpenAlexaffabout
John C. LeBlanc, Tricia L. Beattie, Christopher Culligan

Bibliographic record

VenuePubMed · 2002
Typearticle
Languageen
FieldMedicine
TopicInjury Epidemiology and Prevention
Canadian institutionsDalhousie University
Fundersnot available
KeywordsLegislationOccupational safety and healthInjury preventionMedicinePoison controlSuicide preventionNova scotiaEnforcementHuman factors and ergonomicsRecreationMedical emergencyEnvironmental healthGeographyPolitical scienceLaw
DOInot available

Abstract

fetched live from OpenAlex

BACKGROUND: About 50 Canadian children and adolescents die each year from bicycle-related injuries, and 75% of all bicycle-related deaths are due to head injuries. Although the use of helmets can reduce the risk of head injury by 85%, the rate of voluntary helmet use continues to be low in many North American jurisdictions. We measured compliance before, during and after 1997, when legislation making the use of helmets mandatory for cyclists was enacted in Nova Scotia. METHODS: In the summers and autumns of 1995 through 1999, trained observers who had a direct view of oncoming bicycle traffic recorded helmet use, sex and age group of cyclists in Halifax on arterial, residential and recreational roads. Sampling was done during peak traffic times of sunny days. We abstracted data from the Canadian Hospitals Injury Reporting and Prevention Program database on bicycle-related injuries treated during the same period at the Emergency Department of the IWK Health Centre, Halifax. RESULTS: The rate of helmet use rose dramatically after legislation was enacted, from 36% in 1995 and 38% in 1996, to 75% in 1997, 86% in 1998 and 84% in 1999. The proportion of injured cyclists with head injuries in 1998/99 was half that in 1995/96 (7/443 [1.6%] v. 15/416 [3.6%]) (p = 0.06). Police carried out regular education and enforcement. There were no helmet-promoting mass media education campaigns after 1997. INTERPRETATION: Rates of helmet use rose rapidly following the introduction of legislation mandating the use of helmets while bicycling. The increased rates were sustained for 2 years afterward, with regular education and enforcement by police.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.839
Threshold uncertainty score0.149

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.092
GPT teacher head0.292
Teacher spread0.200 · 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

Citations87
Published2002
Admission routes2
Has abstractyes

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