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Record W4400804121 · doi:10.1080/17457300.2024.2378124

Home injuries in British Columbia: patterns across the deprivation spectrum

2024· article· en· W4400804121 on OpenAlexafffundabout
Umerdad Khudadad, Mojgan Karbakhsh, Anita Yau, Fahra Rajabali, Alex Zheng, Audrey R. Giles, Ian Pike

Bibliographic record

VenueInternational Journal of Injury Control and Safety Promotion · 2024
Typearticle
Languageen
FieldMedicine
TopicInjury Epidemiology and Prevention
Canadian institutionsUniversity of British ColumbiaBC Children's HospitalUniversity of Ottawa
FundersBC Children's Hospital
KeywordsInjury preventionPoison controlOccupational safety and healthSuicide preventionPsychological interventionNeighbourhood (mathematics)Human factors and ergonomicsVulnerability (computing)MedicineCensusGeographyEnvironmental healthDemographyGerontologyPopulationComputer securityPsychiatrySociology

Abstract

fetched live from OpenAlex

The significant burden of home injuries has become a growing concern that affect thousands of people every year across Canada. This study examined the relationship between neighbourhood deprivation and unintentional injuries occurring at home leading to hospitalizations in British Columbia (BC) between 2015 and 2019. This study used de-identified hospitalization data on unintentional home-related injuries from the Discharge Abstract Database (DAD) and population data for each dissemination area from Statistics Canada's 2016 Census Profiles. Hospitalization rates were computed for unintentional home-related injuries across four dimensions specified in the Canadian Index of Multiple Deprivation (CIMD) for BC. For three CIMD dimensions (situational vulnerability, economic dependency, and residential instability), unintentional home injury rates were higher in areas with higher deprivation, while the inverse was observed for ethno-cultural diversity. Understanding socio-economic disparities within neighbourhoods enables injury prevention partners to identify vulnerable populations and prioritize the development and implementation of evidence-based injury prevention interventions.

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)0.000
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 score0.253
Threshold uncertainty score0.284

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 head0.008
GPT teacher head0.296
Teacher spread0.289 · 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

Citations3
Published2024
Admission routes3
Has abstractyes

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