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Record W6983636265

Neighborhood crime and adolescent cannabis use in Canadian adolescents

2015· article· en· W6983636265 on OpenAlexaboutno aff

Bibliographic record

VenueUtrecht University Repository (Utrecht University) · 2015
Typearticle
Languageen
FieldMedicine
TopicSubstance Abuse Treatment and Outcomes
Canadian institutionsnot available
Fundersnot available
KeywordsNeighbourhood (mathematics)Psychological interventionSocioeconomic statusCannabisMultilevel modelLogistic regressionPoison controlSuicide prevention
DOInot available

Abstract

fetched live from OpenAlex

Although neighbourhood factors have been proposed as determinants of adolescent behaviour, few studies document their relative etiological importance. We investigated the relationship between neighbourhood crime and cannabis use in a nationally representative sample of Canadian adolescents. Data from the 2009/10 Canadian Health Behaviour in School-aged Children (HBSC) survey (n = 9,134 14- and 15-year-olds) were combined with area-level data on crime and socioeconomic status of the neighbourhood surrounding the schools (n = 218). Multilevel logistic regression analyses showed that, after individual and contextual differences were held constant, neighbourhood crime related to cannabis use (OR 1.29, CI 1.12-1.47 per 1.0 SD increase in crime). This association was not moderated by parental support nor having cannabis-using friends. The amount of explained variance at the neighbourhood level was 19%. Neighbourhood crime is an important factor to consider when designing interventions aimed at reducing adolescent cannabis use. Interventional research should examine the effectiveness of community-based interventions that target adolescents through parents and peers.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.213
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.026
GPT teacher head0.207
Teacher spread0.181 · 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.

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

Citations0
Published2015
Admission routes1
Has abstractyes

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