MétaCan
Menu
Back to cohort
Record W1964129052 · doi:10.1155/2010/819687

20 Years of Research on Socioeconomic Inequality and Children's—Unintentional Injuries Understanding the Cause-Specific Evidence at Hand

2010· article· en· W1964129052 on OpenAlex
Lucie Laflamme, Marie Hasselberg, Stephanie Burrows

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueInternational Journal of Pediatrics · 2010
Typearticle
Languageen
FieldMedicine
TopicInjury Epidemiology and Prevention
Canadian institutionsCentre Hospitalier de l’Université de Montréal
Fundersnot available
KeywordsSocioeconomic statusMedicinePovertyEnvironmental healthInequalityInjury preventionSocial inequalityOccupational safety and healthSuicide preventionPoison controlHuman factors and ergonomicsMedical emergencyEconomic growthPopulation

Abstract

fetched live from OpenAlex

Injuries are one of the major causes of both death and social inequalities in health in children. This paper reviews and reflects on two decades of empirical studies (1990 to 2009) published in the peer-reviewed medical and public health literature on socioeconomic disparities as regards the five main causes of childhood unintentional injuries (i.e., traffic, drowning, poisoning, burns, falls). Studies have been conducted at both area and individual levels, the bulk of which deal with road traffic, burn, and fall injuries. As a whole and for each injury cause separately, their results support the notion that low socioeconomic status is greatly detrimental to child safety but not in all instances and settings. In light of variations between causes and, within causes, between settings and countries, it is emphasized that the prevention of inequities in child safety requires not only that proximal risk factors of injuries be tackled but also remote and fundamental ones inherent to poverty.

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.

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)0.004
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.018
Threshold uncertainty score0.331

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
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 integrity0.0000.001
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 head0.165
GPT teacher head0.438
Teacher spread0.273 · 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