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Record W2153097401 · doi:10.1076/icsp.8.3.179.3350

Adolescent injuries in relation to economic status: An international perspective

2001· article· en· W2153097401 on OpenAlexaboutno aff
Joanna Mazur, Peter C. Scheidt, Mary D. Overpeck, Yossi Harel, Michal Molcho

Bibliographic record

VenueInjury Control and Safety Promotion · 2001
Typearticle
Languageen
FieldMedicine
TopicInjury Epidemiology and Prevention
Canadian institutionsnot available
Fundersnot available
KeywordsFlemishContext (archaeology)Suicide preventionPoison controlInjury preventionOccupational safety and healthHuman factors and ergonomicsPsychological interventionMultinational corporationEpidemiologyMedicineEnvironmental healthDemographyPsychologyGeographyPolitical scienceNursingSociology

Abstract

fetched live from OpenAlex

Injuries account for a large proportion of morbidity and needs for medical care in otherwise generally healthy school children. Improved understanding of the social context of injuries could help to focus more effective injury prevention interventions. The Health Behavior in School-Aged Children (HBSC) study is a multinational quadrennial school-based survey conducted since 1983, using representative samples of 11, 13 and 15-year-old students. In 1997–98, 12 countries (Flemish Belgium, Canada, England, Estonia, Hungary, Israel, Lithuania, Poland, Republic of Ireland, Sweden, Switzerland, and the USA) collected information regarding the epidemiology of medically attended non-fatal injuries among school children, thus providing an opportunity to investigate the relationship between the influence of social and economic status and of risk of injury. This study also provides an opportunity to examine the relationships from an international perspective and to show similarities and differences between different countries. The application of the same standard questionnaire in different countries permits analyses of a large sample by combining results obtained from all participating HBSC members.

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)0.001
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 score0.112
Threshold uncertainty score0.435

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.014
GPT teacher head0.321
Teacher spread0.306 · 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

Citations5
Published2001
Admission routes1
Has abstractyes

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