Attitudes and perceptions towards substances among people with mental disorders: a systematic review
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".