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Truancy and injury-related mortality

2014· article· en· W2135223033 on OpenAlexaff
Amy Bailey, Gregory R. Istre, Carrie Nie, J G Evans, Reade A. Quinton, Shelli Stephens-Stidham

Bibliographic record

VenueInjury Prevention · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicYouth Substance Use and School Attendance
Canadian institutionsKronos (Canada)
Fundersnot available
KeywordsTruancyHomicidePoison controlInjury preventionMedicineSuicide preventionIntervention (counseling)Occupational safety and healthHuman factors and ergonomicsDemographyMedical emergencyPsychiatryPsychologyCriminologySociology

Abstract

fetched live from OpenAlex

Truancy has well-documented short-term and long-term consequences, but there are few studies that look at its impact on injury-related mortality. This study evaluated the rate of injury-related mortality for 2006-2010 among youth (11-17 years old) with a history of severe truancy compared with youth without such history. There were 168 injury-related deaths (51 homicide, 29 suicide and 88 unintentional injury deaths) among youth in Dallas County. Fifteen of these deaths were among youth with a history of severe truancy. Injury-related mortality was more than five times higher among youth with history of severe truancy compared with youth without such history. Youth with a history of severe truancy have an increased risk of injury-related death. Further research may be warranted to evaluate the part of less severe levels of truancy on mortality and to study the effectiveness of truancy intervention programmes on the risk of death from injuries.

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 score0.016
Threshold uncertainty score0.031

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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.022
GPT teacher head0.345
Teacher spread0.323 · 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

Citations14
Published2014
Admission routes1
Has abstractyes

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