The effectiveness of interventions for reducing stigma related to substance use disorders: a systematic review
Bibliographic record
Abstract
AIMS: This study provides a systematic review of existing research that has empirically evaluated interventions designed to reduce stigma related to substance use disorders. METHODS: A comprehensive review of electronic databases was conducted to identify evaluations of substance use disorder related stigma interventions. Studies that met inclusion criteria were synthesized and assessed using systematic review methods. RESULTS: Thirteen studies met the inclusion criteria. The methodological quality of the studies was moderately strong. Interventions of three studies (23%) focused on people with substance use disorders (self-stigma), three studies (23%) targeted the general public (social stigma) and seven studies (54%) focused on medical students and other professional groups (structural stigma). Nine interventions (69%) used approaches that included education and/or direct contact with people who have substance use disorders. All but one study indicated their interventions produced positive effects on at least one stigma outcome measure. None of the interventions have been evaluated across different settings or populations. CONCLUSIONS: A range of interventions demonstrate promise for achieving meaningful improvements in stigma related to substance use disorders. The limited evidence indicates that self-stigma can be reduced through therapeutic interventions such as group-based acceptance and commitment therapy. Effective strategies for addressing social stigma include motivational interviewing and communicating positive stories of people with substance use disorders. For changing stigma at a structural level, contact-based training and education programs targeting medical students and professionals (e.g. police, counsellors) are effective.
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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 | 0.016 | 0.067 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.010 |
| Bibliometrics | 0.013 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".