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Record W3167846545 · doi:10.1080/14659891.2021.1941349

Motives for non-medical prescription opioid (NMPO) use among young people in a semi-rural Canadian Province

2021· article· en· W3167846545 on OpenAlexaffabout
Lillian MacNeill, Shelley Doucet, Alison Luke

Bibliographic record

VenueJournal of Substance Use · 2021
Typearticle
Languageen
FieldMedicine
TopicOpioid Use Disorder Treatment
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsOpioidMedical prescriptionYoung adultHarmMedicineHarm reductionPrescription Drug MisuseCoping (psychology)PsychiatryPsychologyFamily medicineGerontologyNursingSocial psychologyOpioid use disorderInternal medicine

Abstract

fetched live from OpenAlex

Objective In Canada, young people aged 15–24 had the fastest growing rates of hospitalization for opioid poisoning in the last decade, compared to other age groups. This cross-sectional study examined non-medical prescription opioid (NMPO) use in youth and young adults in a semi-rural Canadian province.Method Participants completed an online survey about motives for NMPO use, and knowledge and utilization of local resources.Results All participants (N = 108) were self-reported opioid users between the ages of 15–25 years. The majority of participants had been prescribed an opioid by a physician in the past. Regression analysis showed that being older, having an opioid prescription, and using opioids for pain, coping, or enhancement reasons predicted higher levels of disordered opioid use. Pain was the most common motive for NMPO use and the strongest predictor of disordered opioid use. Most participants reported having limited knowledge about harm reduction resources in their communities.Conclusions Although the opioid crisis is a wide-spread concern, understanding why youth and young adults engage in NMPO use in local contexts may facilitate the development and implementation of resources that are more useful for these individuals, which could be scalable to other regions across Canada and internationally.

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)0.000
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 score0.607
Threshold uncertainty score0.985

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.013
GPT teacher head0.251
Teacher spread0.238 · 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

Citations1
Published2021
Admission routes2
Has abstractyes

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