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Record W2511883997 · doi:10.1080/1067828x.2015.1115795

Nonmedical Use of Prescription Opioids and Injury Risk Among Youth

2016· article· en· W2511883997 on OpenAlexaffabout
Ariel Pulver, Colleen Davison, Alyssa S. Parpia, Eva Purkey, William Pickett

Bibliographic record

VenueJournal of Child & Adolescent Substance Abuse · 2016
Typearticle
Languageen
FieldMedicine
TopicInjury Epidemiology and Prevention
Canadian institutionsKingston General HospitalQueen's UniversityUniversity of Toronto
Fundersnot available
KeywordsMedical prescriptionDemographicsMedicineSubstance abuseOpioidInjury preventionRecreationOpioid abusePsychiatryPoison controlEmergency medicineInternal medicineDemographyPharmacology

Abstract

fetched live from OpenAlex

This study examined relations between prescription opioid abuse and risk of injury among grade 9–10 students in the nationally representative Canadian Health Behavior in School-Aged Children study (weighted N = 9,974). Students were asked about past-year injury, the activity when the injury occurred, and recreational opioid use. Injury among users was twice that of nonusers and was more frequently fighting-related. Prescription opioid misuse was associated with a moderate increase in serious injury risk adjusted for demographics, peer drug use, and other substance use. Explanations may include physiological effects of opioids, multiple risk-taking tendencies, and inadequate parental supervision.

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 score0.012
Threshold uncertainty score0.372

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 integrity0.0000.001
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 head0.018
GPT teacher head0.262
Teacher spread0.244 · 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

Citations12
Published2016
Admission routes2
Has abstractyes

Explore more

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