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Record W2014545005 · doi:10.1080/17457300600864421

The epidemiology of non-fatal injuries among 11-, 13- and 15-year old youth in 11 countries: findings from the 1998 WHO-HBSC cross national survey

2006· article· en· W2014545005 on OpenAlexaff
Michal Molcho, Yossi Harel, William Pickett, Peter C. Scheidt, Joanna Mazur, Mary D. Overpeck, O And

Bibliographic record

VenueInternational Journal of Injury Control and Safety Promotion · 2006
Typearticle
Languageen
FieldMedicine
TopicInjury Epidemiology and Prevention
Canadian institutionsQueen's University
Fundersnot available
KeywordsInjury preventionOccupational safety and healthPoison controlSuicide preventionMedicineHuman factors and ergonomicsEpidemiologyContext (archaeology)Cross-sectional studyEnvironmental healthInjury surveillanceMedical emergencyGeography

Abstract

fetched live from OpenAlex

The primary objective was to present a cross-country comparison of injury rates, contexts and consequences. The research design was the analysis of data from the 1998 cross-national Health Behaviour in School-aged Children survey and 52955 schoolchildren from 11 countries, aged 11, 13 and 15 years, completed a self-administrated questionnaire. A total of 41.3% of all children were injured and needed medical treatment in the past 12 months. Injury rates among boys were higher than among girls, 13.3% reported activity loss due to injury and 6.9% reported severe injury consequences. Most injuries occurred at home and at a sport facility, mainly during sport activity. Fighting accounted for 4.1% of injuries. This paper presents the first cross-national comparison of injury rates and patterns by external cause and context. Findings present cross-country similarities in injury distribution by setting and activity. These findings emphasize the importance of the development of global prevention programmes designed to address injuries among youth.

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)0.008
metaresearch head score (Gemma)0.002
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 score0.006
Threshold uncertainty score0.374

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.016
GPT teacher head0.316
Teacher spread0.300 · 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

Citations52
Published2006
Admission routes1
Has abstractyes

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