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Recent trends in child and youth emergency department visits because of pedestrian motor vehicle collisions by socioeconomic status in Ontario, Canada

2019· article· en· W2939875488 on OpenAlexaffabout
Linda Rothman, Colin Macarthur, Andrew S. Wilton, Andrew Howard, Alison Macpherson

Bibliographic record

VenueInjury Prevention · 2019
Typearticle
Languageen
FieldEngineering
TopicTraffic and Road Safety
Canadian institutionsYork UniversityInstitute for Clinical Evaluative SciencesSickKids FoundationHospital for Sick Children
Fundersnot available
KeywordsPedestrianSocioeconomic statusPoison controlOccupational safety and healthMotor vehicle crashInjury preventionSuicide preventionHuman factors and ergonomicsEmergency departmentMedical emergencyEnvironmental healthForensic engineeringEngineeringTransport engineeringMedicinePsychiatryPopulation

Abstract

fetched live from OpenAlex

BACKGROUND: Children in lower-income households have higher injury rates. Trends in emergency department (ED) visits by children 0-19 years because of pedestrian motor vehicle collisions (PMVCs) in Ontario, Canada (2008-2015) by socioeconomic status were examined. METHODS: PMVC ED data were obtained from the Institute for Clinical Evaluative Sciences for children age 0-19 years over the period 2008-2015. Age-adjusted rates were calculated using Ontario census data. Household income quintiles were determined from the Registered Persons Database. Poisson regression was used to model ED visit rates by year, age and income quintile. RESULTS: The frequency of child PMVC ED visits in Ontario decreased from 1562 in 2008 to 1281 in 2015. Age-adjusted rates were unchanged over time (IRR 1.00, 95% CI 0.99 to 1.00); however, rate disparities by income status persisted with an IRR of 0.52 (0.50 to 0.55) comparing the highest with the lowest income level. CONCLUSIONS: Exposure to traffic may play a role in rate disparities by income status in child PMVC; however, less safe traffic environments in lower income areas may also be strong contributors. These findings highlight the potential impact of roadway safety modifications in lower income areas to mitigate disparities in injury rates by socioeconomic status.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.547
Threshold uncertainty score0.578

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 head0.007
GPT teacher head0.216
Teacher spread0.210 · 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

Citations13
Published2019
Admission routes2
Has abstractyes

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