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Record W1972652110 · doi:10.1093/eurpub/ckv026

Trend in injury-related mortality and morbidity among adolescents across 30 countries from 2002 to 2010

2015· article· en· W1972652110 on OpenAlexafffund
Michal Molcho, S. Walsh, Peter Donnelly, Margarida Gaspar de Matos, William Pickett

Bibliographic record

VenueEuropean Journal of Public Health · 2015
Typearticle
Languageen
FieldMedicine
TopicInjury Epidemiology and Prevention
Canadian institutionsQueen's University
FundersCanadian Institutes of Health Research
KeywordsDemographyMedicineEnvironmental healthSociology

Abstract

fetched live from OpenAlex

BACKGROUND: The aim was to examine temporal trends in injury mortality and morbidity across 30 countries in Europe and North America, and the impact of regional geography and adolescent risk behaviours (including substance use and physical fighting) on such trends. METHOD: s: Data were obtained for 30 countries in 2002, 2006 and 2010. Mortality data were obtained from the World Health Organization's (WHO) Health for all database. Trends over time were described by WHO Regions using standardized rates comparisons and Poisson regression analyses. RESULTS: Injury-related mortality, but not morbidity, declined over time across all countries (from 10 to 8 deaths per 100 000 between 2001 and 2010), with notable differences observed by Regions (e.g. from 48 to 39 deaths in Russia). Risk behaviours included in the models were consistently and significantly associated with injury morbidity, with substance increasing the risk for injury by 1.15 to 1.36 among girls, and physical fighting increasing the risk by 1.21 to 1.31 among boys across WHO Regions. Risk behaviours did not explain the observed temporal trends. CONCLUSIONS: Injury mortality and morbidity represent different health phenomena. Efforts that have been made to make societies safer for children have seemed to be successful in reducing injury morbidity.

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)0.021
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 score0.031
Threshold uncertainty score0.736

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0210.001
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 head0.141
GPT teacher head0.401
Teacher spread0.260 · 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

Citations34
Published2015
Admission routes2
Has abstractyes

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