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Record W4410090298 · doi:10.1521/aeap.2025.37.2.142

Cannabis Use Trajectories Among People Living With HIV in the Decade Prior to Recreational Legalization in Ontario, Canada (2008-2017)

2025· article· en· W4410090298 on OpenAlexaffabout
Tanya Lazor, Marcos Sanches, Jeffrey D. Wardell, Wei Wang, Ann N. Burchell, Shari Margolese, Tsegaye Bekele, Abigail Kroch, Sergio Rueda

Bibliographic record

VenueAIDS Education and Prevention · 2025
Typearticle
Languageen
FieldMedicine
TopicCannabis and Cannabinoid Research
Canadian institutionsUniversity of TorontoOntario HIV Treatment NetworkRegent Park Community Health CentreYork UniversityMental Health Research CanadaSt. Michael's HospitalCanadian Mental Health AssociationCentre for Addiction and Mental Health
Fundersnot available
KeywordsCannabisLegalizationMedicineRecreationDepression (economics)CohortDemographyPsychiatryGerontologyInternal medicine

Abstract

fetched live from OpenAlex

We aimed to describe long-term use trajectories and predictors prior to recreational cannabis legalization in people with HIV in Ontario, Canada. We analysed interview data from the prospective Ontario HIV Treatment Network Cohort Study from 2008 to 2017. We conducted Latent Class Growth Analyses to describe cannabis use trajectories and chi-square tests to identify trajectory group predictors. Most participants (N = 3,299) were male (81%), gay (57%), current/former tobacco smokers (58%), and many had significant symptoms of depression (43%). Four cannabis use trajectory groups were identified (Low/No Use (67%); Increased Use (4%); Decreased use (2%); High Use (26%)). Relative to the Low/No Use group, membership in the High Use group was associated with several predictors such as being older age, completing university, smoking tobacco, and significant depressive symptoms. Future research should explore the relationship between cannabis use and depressive symptoms, outcomes associated with trajectory groups and changes in use trajectories following recreational legalization.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.099
Threshold uncertainty score0.919

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
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 head0.012
GPT teacher head0.288
Teacher spread0.276 · 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
Published2025
Admission routes2
Has abstractyes

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