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Record W2565556324 · doi:10.35502/jcswb.18

Prince Albert youth drug and alcohol use: a comparison study of Prince Albert, Saskatchewan, and Canada youth

2016· article· en· W2565556324 on OpenAlexaffvenueabout
Jason Georg Fenno

Bibliographic record

VenueJournal of Community Safety and Well-Being · 2016
Typearticle
Languageen
FieldMedicine
TopicSubstance Abuse Treatment and Outcomes
Canadian institutionsUniversity of Regina
Fundersnot available
KeywordsLogistic regressionSubstance abuseBivariate analysisAlcohol abuseDemographyPsychologyEnvironmental healthGerontologyMedicineGeographyCriminologySociologyPsychiatry

Abstract

fetched live from OpenAlex

Community Mobilization Prince Albert’s (CMPA) Hub and Centre of Responsibility (COR) had been dealing with high rates of youth arrest and referrals to treatment facilities stemming from youth substance abuse. To better help the CMPA craft policies to counter the high rates of youth alcohol and drug use, a study was conducted in June of 2012 that utilized a school survey of PA youth. Data were collected from four local Prince Albert high schools and compared with Saskatchewan and Canadian youth. Analyses of the data were conducted using logistic regression and bivariate correlation. The following paper will provide an overview of the study and explain why youth substance abuse was chosen for this study. Later sections will examine how the Prince Albert school survey was formulated for comparison purposes with Saskatchewan and Canadian youth data obtained from the 2010-11 Youth Smoking Survey (YSS). This will be followed by an overview of the study’s key findings, along with results of logistic regression and bivariate correlation analysis and the study’s limitations. A final section will examine the implications of this study’s findings on youth substance abuse policy and programs for the CMPA Hub and COR, along with the city of PA.

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 score0.427
Threshold uncertainty score0.858

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.023
GPT teacher head0.265
Teacher spread0.241 · 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

Citations9
Published2016
Admission routes3
Has abstractyes

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