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Record W2183231954 · doi:10.1080/15389588.2015.1061661

Did Child Restraint Laws Globally Converge? Examining 40 Years of Policy Diffusion

2015· article· en· W2183231954 on OpenAlexaff
José Ignacio Nazif‐Muñoz

Bibliographic record

VenueTraffic Injury Prevention · 2015
Typearticle
Languageen
FieldMedicine
TopicInjury Epidemiology and Prevention
Canadian institutionsMcGill University
Fundersnot available
KeywordsCommonwealthGross domestic productPer capitaPopulationPoison controlVariablesGross national incomeEconomic growthEconomicsPolitical scienceLawDemographyEnvironmental healthSociologyStatisticsMedicine

Abstract

fetched live from OpenAlex

OBJECTIVE: The objective of the current study is to determine what factors have been associated with the global adoption of mandatory child restraint laws (ChRLs) since 1975. METHODS: In order to determine what factors explained the global adoption of mandatory ChRLs, Weibull models were analyzed. To carry out this analysis, 170 countries were considered and the time risk corresponded to 5,146 observations for the period 1957-2013. The dependent variable was first time to adopt a ChRL. Independent variables representing global factors were the World Health Organization (WHO) and World Bank's (WB) road safety global campaign; the Geneva Convention on Road Traffic; and the United Nation's (UN) 1958 Vehicle Agreement. Independent variables representing regional factors were the creation of the European Transport Safety Council and being a Commonwealth country. Independent variables representing national factors were population; gross domestic product (GDP) per capita; political violence; existence of road safety nongovernmental organizations (NGOs); and existence of road safety agencies. Urbanization served as a control variable. To examine regional dynamics, Weibull models for Africa, Asia, Europe, North America, Latin America, the Caribbean, and the Commonwealth were also carried out. RESULTS: Empirical estimates from full Weibull models suggest that 2 global factors and 2 national factors are significantly associated with the adoption of this measure. The global factors explaining adoption are the WHO and WB's road safety global campaign implemented after 2004 (P <.01), and the UN's 1958 Vehicle Agreement (P <.001). National factors were GDP (P <.01) and existence of road safety agencies (P <.05). The time parameter ρ for the full Weibull model was 1.425 (P <.001), suggesting that the likelihood of ChRL adoption increased over the observed period of time, confirming that the diffusion of this policy was global. Regional analysis showed that the UN's Convention on Road Traffic was significant in Asia, the creation of the European Transport Safety Council was significant in Europe and North America, and the global campaign was in Africa. In Commonwealth and European and North American countries, the existence of road safety agencies was also positively associated with ChRL adoption. CONCLUSIONS: Results of the world models suggest that the WHO and WB's global road safety campaign was effective in disseminating ChRLs after 2004. Furthermore, regions such as Asia and Europe and North America were early adopters since specific regional and national characteristics anticipated the introduction of this policy before 2004. In this particular case, the creation of the European Transport Safety Council was fundamental in promoting ChRLs. Thus, in order to introduce conditions to more rapidly diffuse road safety measures across lagging regions, the maintenance of global efforts and the creation of road safety regional organizations should be encouraged. Lastly, the case of ChRL convergence illustrates how mechanisms of global and regional diffusion need to be analytically differentiated in order better to assess the process of policy diffusion.

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)0.002
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 score0.901
Threshold uncertainty score0.763

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
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 head0.040
GPT teacher head0.342
Teacher spread0.302 · 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

Citations20
Published2015
Admission routes1
Has abstractyes

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