MétaCan
Menu
Back to cohort
Record W1982475626 · doi:10.1108/09654280410560541

Does promoting bicycle‐helmet wearing reduce childhood head injuries?

2004· article· en· W1982475626 on OpenAlexaff
Céline Farley, Marjan Vaez, Lucie Laflamme

Bibliographic record

VenueHealth Education · 2004
Typearticle
Languageen
FieldMedicine
TopicInjury Epidemiology and Prevention
Canadian institutionsInstitut National de Santé Publique du Québec
Fundersnot available
KeywordsMedicineRelative riskPoison controlInjury preventionPopulationOccupational safety and healthDemographyPediatricsConfidence intervalEnvironmental healthInternal medicine

Abstract

fetched live from OpenAlex

The objectives of the study are to assess the impact of a community‐based bicycle‐helmet program aimed at children aged 5–12 years (about 140,000). A quasi‐experimental design, including a control group, was used. Sex‐ and age‐group‐based changes in the risk of bicycle‐related head injury leading to hospitalisation were measured, using rate ratios. Compared with the pre‐program period, significant risk reductions were observed during the post‐program period among both boys ( RR = 0.56, 95 per cent CI = 0.40, 0.77) and girls ( RR = 0.52, 95 per cent CI = 0.33, 0.82), and among both younger ( RR = 0.46, 95 per cent CI = 0.31, 0.68) and older ( RR = 0.63, 95 per cent CI = 0.44, 0.89) children. A significant reduction was also observable during the program phase among the groups most at risk, i.e. boys ( RR = 0.94, 95 per cent CI = 0.66, 1.35) and younger children ( RR = 1.07, 95 per cent CI = 0.70, 1.63). The population‐based educational program significantly decreased the risk of head injuries among boys and girls despite observable differences in the voluntary adoption rate of bicycle‐helmet wearing. The impact was more pronounced among younger children.

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)0.000
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 score0.785
Threshold uncertainty score0.342

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.030
GPT teacher head0.408
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

Citations1
Published2004
Admission routes1
Has abstractyes

Explore more

Same venueHealth EducationSame topicInjury Epidemiology and PreventionFrench-language works237,207