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Record W2321746935 · doi:10.1093/pch/9.5.323

Socioeconomic status and injury risk in children

2004· article· en· W2321746935 on OpenAlexaff
Catherine S. Birken, Colin Macarthur

Bibliographic record

VenuePaediatrics & Child Health · 2004
Typearticle
Languageen
FieldMedicine
TopicInjury Epidemiology and Prevention
Canadian institutionsSickKids FoundationUniversity of TorontoHospital for Sick Children
Fundersnot available
KeywordsSocioeconomic statusMedicineInjury preventionRecreationOccupational safety and healthEnvironmental healthPoison controlSuicide preventionHuman factors and ergonomicsIntervention (counseling)DemographyMedical emergencyPediatricsPopulationPsychiatryPathology

Abstract

fetched live from OpenAlex

Research has consistently shown that low socioeconomic status (SES) is associated with an increased risk of poor health and death in adults and children. Studies from around the world have shown an inverse relationship between SES and childhood injury morbidity and mortality. For example, compared with children with high SES, children with low SES are at an increased risk of death from pedestrian collisions, fires, falls and drownings, and at an increased risk of hospitalization from recreation or play injuries. Research from England and Wales shows that these disparities in mortality between high and low SES children may be widening over time. This paper provides an overview of the literature on the relationship between SES and childhood injury morbidity and mortality, outlines the postulated mechanisms for this relationship, and highlights some intervention studies targeted to low SES children.

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)0.000
metaresearch head score (Gemma)0.002
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 score0.018
Threshold uncertainty score0.036

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.301
Teacher spread0.293 · 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

Citations75
Published2004
Admission routes1
Has abstractyes

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