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Record W2789737835 · doi:10.1177/2167696817748106

University Students’ Perceptions of Links Between Substance Use and Mental Health

2018· article· en· W2789737835 on OpenAlexaff
Amanda Hudson, Kara Thompson, Parnell Davis MacNevin, Meredith Ivany, Michael D. Teehan, Heather Stuart, Sherry H. Stewart

Bibliographic record

VenueEmerging Adulthood · 2018
Typearticle
Languageen
FieldPsychology
TopicMental Health Treatment and Access
Canadian institutionsQueen's UniversitySt. Francis Xavier UniversityDalhousie University
Fundersnot available
KeywordsMental healthSubstance useAddictionPsychologyThematic analysisPerceptionFocus groupSubstance abusePsychiatryClinical psychologyQualitative research

Abstract

fetched live from OpenAlex

There is a consensus among addictions researchers and clinicians that mental health concerns and substance use problems are often interrelated. It is less clear to what extent the general public, and university students in particular, understand connections between substance use and mental health. The current study aimed to understand university students’ perceived links between substance use and mental health by conducting three semistructured focus groups ( N = 24 participants, 67% female). Thematic analysis of the data yielded five themes: (1) Students use substances to cope with mental health issues, (2) substance use can lead to mental health problems, (3) links between mental health and substance use are cyclical, (4) substance use is an aspect/indicator of mental health, and (5) substance use and mental health are not always linked. Findings provide insight into the understudied area of perceived links between substance use and mental health and have implications for campus programming.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.005
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.003
Scholarly communication0.0040.001
Open science0.0000.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.040
GPT teacher head0.371
Teacher spread0.332 · 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 source (direct Gemma or distilled Codex), 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

Citations10
Published2018
Admission routes1
Has abstractyes

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