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Record W4360620775 · doi:10.33137/utmj.v100i1.38056

Differential impacts of perceived social support on alcohol and cannabis use in young adults: lessons from the COVID-19 pandemic

2023· article· en· W4360620775 on OpenAlexafffundvenueabout
Michelle J Blumberg, Lindsay A. Lo, Geoffrey Harrison, Alison Dodwell, Samantha H. Irwin, Mary C. Olmstead

Bibliographic record

VenueUniversity of Toronto Medical Journal · 2023
Typearticle
Languageen
FieldPsychology
TopicCOVID-19 and Mental Health
Canadian institutionsUniversity of TorontoQueen's University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsCannabisPandemicYoung adultPsychologySocial supportSubstance useCoronavirus disease 2019 (COVID-19)Mental healthEnvironmental healthMedicinePsychiatryDevelopmental psychologySocial psychologyDisease

Abstract

fetched live from OpenAlex

Coronavirus (COVID-19) lockdowns provided a unique opportunity to examine how changes in the social environment impact mental health and wellbeing. We addressed this issue by assessing how perceived social support across COVID-19 restrictions alters alcohol and cannabis use in emerging adults, a population vulnerable to adverse outcomes of substance use. Four hundred sixty-three young adults in Canada and the United States completed online questionnaires for three retrospective timepoints: Pre-Covid, Lockdown and Eased Restrictions. Sociodemographic factors, perceived social support, and substance use were assessed. Overall, alcohol use decreased while cannabis use increased during Lockdown. Interestingly, social support negatively predicted alcohol use and positively predicted cannabis use during Lockdown. These findings suggest a difference in motives underlying alcohol and cannabis use in emerging adults. Importantly, these changes were not sustained when restrictions eased, suggesting that emerging adults exhibit resiliency to the impacts of COVID-19 restrictions on substance use.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.165
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.000

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 head0.079
GPT teacher head0.388
Teacher spread0.310 · 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

Citations1
Published2023
Admission routes4
Has abstractyes

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