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Record W4361279059 · doi:10.1037/adb0000913

Frequency and factors related to substance use among Black individuals aged 15–40 years old in Canada: The role of everyday racial discrimination.

2023· article· en· W4361279059 on OpenAlexafffundabout
Jude Mary Cénat, Élisabeth Dromer, Emmanuelle Auguste, Rose Darly Dalexis, Wina Paul Darius, Cary S. Kogan, Mireille Guerrier

Bibliographic record

VenuePsychology of Addictive Behaviors · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicRacial and Ethnic Identity Research
Canadian institutionsUniversity of Ottawa
FundersPublic Health Agency of Canada
KeywordsPsycINFOCannabisReligiosityPsychologySubstance useSubstance abusePsychological resilienceDemographyInjury preventionSuicide preventionProtective factorPoison controlClinical psychologyPsychiatryMedicineMEDLINESocial psychologyEnvironmental healthInternal medicine

Abstract

fetched live from OpenAlex

OBJECTIVE: Despite evidence that Black individuals are confronted with various types of racial discrimination that put them at risk for alcohol and substance use disorders, no study in Canada has assessed the frequency and factors related to substance use in Black communities. This study thus aims to examine the frequency and factors related to substance use in Black communities in Canada. METHOD: A total of 845 Black individuals in Canada (76.6% female) completed questionnaires assessing substance use (i.e., alcohol, cannabis, and other drugs), everyday racial discrimination, resilience, religious involvement, and sociodemographic information. Multivariable regression analyses were used to determine factors related to substance use among Black individuals. RESULTS: < .001). CONCLUSIONS: Racial discrimination is associated with substance use among Black individuals in Canada. The study findings inform potential prevention and intervention strategies by examining protective factors related to substance use (e.g., religiosity, resilience, gender) among Black individuals. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

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 score0.135
Threshold uncertainty score0.357

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
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 head0.039
GPT teacher head0.351
Teacher spread0.312 · 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
Published2023
Admission routes3
Has abstractyes

Explore more

Same venuePsychology of Addictive BehaviorsSame topicRacial and Ethnic Identity ResearchFrench-language works237,207