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Record W4285389016 · doi:10.1080/07448481.2022.2098030

Trends in the co-occurrence of substance use and mental health symptomatology in a national sample of US post-secondary students from 2009 to 2019

2022· article· en· W4285389016 on OpenAlexaff
Jillian Halladay, Christina E. Freibott, Sarah Ketchen Lipson, Sasha Zhou, Daniel Eisenberg

Bibliographic record

VenueJournal of American College Health · 2022
Typearticle
Languageen
FieldPsychology
TopicSuicide and Self-Harm Studies
Canadian institutionsMcMaster UniversityImpact
FundersEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentNational Institute of Mental HealthNational Institutes of Health
KeywordsMental healthCannabisAnxietySuicidal ideationPsychiatryDepression (economics)PsychologyPsychological interventionClinical psychologySubstance abuseLogistic regressionPsychoeducationMedicineSuicide preventionPoison controlEnvironmental health

Abstract

fetched live from OpenAlex

Objective: This study examined joint trends over time in associations between substance use (heavy drinking, cannabis, and cigarette smoking) and mental health concerns (depression, anxiety, and suicidal ideation) among US post-secondary students. Participants: Data came from 323,896 students participating in the Healthy Minds Study from 2009 to 2019, a national cross-sectional survey of US post-secondary students. Weighted two-level logistic regression models with a time by substance interaction term were used to predict mental health status. Results: Use of each substance was associated with a greater odds of students endorsing depression, anxiety, and suicidal ideation. Over time, the association with mental health concerns strengthened substantially for cannabis, modestly for heavy drinking, and remained stable for smoking. Conclusion: Given co-occurrence is common and increasing among post-secondary students, college and university health systems should prioritize early identification, psychoeducation, harm-reduction, and brief interventions to support students at risk.

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 score0.041
Threshold uncertainty score0.989

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
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 head0.034
GPT teacher head0.384
Teacher spread0.350 · 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

Citations27
Published2022
Admission routes1
Has abstractyes

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