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Record W4296498724 · doi:10.1093/pch/21.supp5.e78b

Evaluation of a Child Safety Program Based on the Safe Community Model in Canada

2016· article· en· W4296498724 on OpenAlexaboutno aff
E Beaulieu, C Cyr, M Santschi

Bibliographic record

VenuePaediatrics & Child Health · 2016
Typearticle
Languageen
FieldMedicine
TopicInjury Epidemiology and Prevention
Canadian institutionsnot available
Fundersnot available
KeywordsPedestrianCyclingMedicinePopulationDemographyEnvironmental healthGerontologyGeographyForestrySociology

Abstract

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Abstract BACKGROUND: Despite national safety programs, unintentional injuries remain an important health problem in children and adolescents. Cycling, pedestrian and young drivers injuries are particularly influenced by specific community and city aspects. The World Health Organization (WHO) developed community based programs that have been effectively implemented as complements to national safety programs to prevent injuries. OBJECTIVES: The aim of this study was to evaluate the effectiveness of a local program based on a WHO Safe Community model to reduce cycling, pedestrian and young drivers injuries. DESIGN/METHODS: A population based quasi-experimental design was used. Pre-implementation and post-implementation data of cycling and pedestrian injury rates (0-15 years) and young drivers injury rates (16-24 years) were collected in the intervention area (Sherbrooke) and in two control communities (Trois-Rivières and Gatineau) in Québec, Canada. RESULTS: Sherbrooke, Gatineau and Trois-Rivières had respectively 69, 82 and 119 cycling and pedestrian injury rate per 100000 children-year in the pre implementation period. Despite its already lower pre-implementation rate, Sherbrooke showed a statistically significant reduction in the post-implementation injury rate compared to Trois-Rivières (Sherbrooke: 49:100000 children-year (OR comparing pre-post rates 0.70 ; 95%CI : 0.45-1.08) and Trois-Rivières 80:100000 children-year (OR comparing pre-post rates 0.68; 95%CI: 0.46-1.0). Gatineau showed a cycling and pedestrian post-implementation injury rate of 63:100000 (OR 0.77; 95%CI: 0.58-1.02). Sherbrooke had the largest young drivers injury reduction with rates of 2912:100000 young driver-year (pre) to 2121: 100000 young driver-year (post) (OR 0.73; 95%CI: 0.66-0.8). Gatineau and Trois-Rivieres showed respectively young drivers injury rate lowering from 2383: 100000 to 2099: 100000 young driver-year (OR 0.88; 95%CI: 0.81-0.95) and from 3447: 100000 to 3295: 100000 young driver-year (OR 0.96; 95%CI 0.87-1.05). CONCLUSION: Safe Community program established in Sherbrooke was associated with favorable results in injury prevention. Despite its lower pedestrian and cycling injury rate before the intervention, post-implementation injury rate in Sherbrooke was significantly lower compared to Trois-Rivieres. Concerning young drivers injury rates, Sherbrooke showed the biggest reduction, compared to Trois-Riviere and Gatineau. In addition to national injury prevention programs, communities should be encouraged to adopt WHO safe community programs to reduce to a minimum unintentional injury rates in children and adolescents.

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)0.004
metaresearch head score (Gemma)0.003
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 score0.070
Threshold uncertainty score0.264

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0030.001
Scholarly communication0.0010.000
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.051
GPT teacher head0.350
Teacher spread0.299 · 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

Citations0
Published2016
Admission routes1
Has abstractyes

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