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Record W2146190132 · doi:10.1136/ip.2010.028225

Child pedestrian injuries and urban change

2010· article· en· W2146190132 on OpenAlexaffabout
Nikolaos Yiannakoulias, Darren M. Scott, Brian H. Rowe, Donald C. Voaklander

Bibliographic record

VenueInjury Prevention · 2010
Typearticle
Languageen
FieldEngineering
TopicTraffic and Road Safety
Canadian institutionsUniversity of AlbertaMcMaster University
Fundersnot available
KeywordsPedestrianPoison controlInjury preventionHuman factors and ergonomicsSuicide preventionOccupational safety and healthForensic engineeringEngineeringTransport engineeringMedical emergencyMedicine

Abstract

fetched live from OpenAlex

BACKGROUND: The health impacts of rapid changes in urban environments due to economic growth and/or retraction are not widely known. This study looks at the effects of urban change on the risk of child pedestrian injury in Edmonton, Alberta, a city that has experienced large economic and population growth following the expansion of the oil and gas industry in Canada. METHODS: A longitudinal ecological study design was used to model the relationships between several built and social environmental variables and the risk of child pedestrian injury and severe child pedestrian injury between 1996 and 2007. RESULTS: The incidence of child pedestrian injury was stable, but the incidence of severe injury increased over the study period. Areas with higher proportions of families on low incomes had higher injury incidence. While new residential development is associated with a lower incidence of injury in most areas, in poor areas, new residential development is associated with a higher incidence, even after controlling for urban planning features and traffic intensity. CONCLUSION: While suburban areas have a lower incidence of child pedestrian injury, residential development in poorer areas is associated with a higher child pedestrian injury risk. Child pedestrians may be less able to adapt to changes in the urban environment due to rapid growth and increasing income, and as a result, may be at greater risk of injury.

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)0.000
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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.918
Threshold uncertainty score0.330

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.009
GPT teacher head0.230
Teacher spread0.221 · 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 designOther design
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

Citations9
Published2010
Admission routes2
Has abstractyes

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