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Attitudes and perceptions towards substances among people with mental disorders: a systematic review

2012· review· en· W1887181264 on OpenAlexaboutno aff
Louise Thornton, Amanda Baker, Martin P. Johnson, Terry J. Lewin

Bibliographic record

VenueActa Psychiatrica Scandinavica · 2012
Typereview
Languageen
FieldMedicine
TopicSubstance Abuse Treatment and Outcomes
Canadian institutionsnot available
Fundersnot available
KeywordsPsychological interventionPsychologyCannabisPleasureAffect (linguistics)PopulationPerceptionSubstance useMoodClinical psychologyAddictionPsychiatryMedicinePsychotherapistEnvironmental health

Abstract

fetched live from OpenAlex

OBJECTIVE: To develop effective interventions for people with coexisting mental disorders (MD) and substance use, it may be beneficial to understand their attitudes and perceptions of substances. METHOD: A systematic literature search regarding attitudes and perceptions towards tobacco, alcohol or cannabis among people with MD was conducted. Studies' methodological quality was assessed using the Newcastle-Ottawa Scale. RESULTS: Twenty-one papers were included in the review and found to have generally low methodological quality. Papers investigated reasons for substance use, substance use expectancies, substances' perceived effects and reasons for quitting. People with psychotic disorders reported using substances primarily for relaxation and pleasure. Among people with mood disorders, alcohol was used primarily for social motives and tobacco for negative affect reduction. CONCLUSION: For substance use interventions among people with MD to be more effective, it may be necessary to tailor interventions specifically for this population and customize by substance type. Gaps in the literature regarding attitudes and perceptions towards substance use among people with MD were identified, which future research should aim to address. These include designing and conducting methodologically rigorous research, investigating perceived harmfulness and knowledge of substances, and broadening recruitment of participants to include people with MD other than psychosis.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.073
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0040.001
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 head0.022
GPT teacher head0.325
Teacher spread0.303 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations18
Published2012
Admission routes1
Has abstractyes

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