Stigmatization of people with alcohol use disorders: An updated systematic review of population studies
Bibliographic record
Abstract
BACKGROUND: We summarize research on the public stigmatization of persons with alcohol use disorder (AUD) in comparison with other mental health conditions and embed the results into a conceptual framework of the stigma process. METHODS: We conducted a systematic search using Embase, MEDLINE, PubMed and PsycINFO (via Ovid), and Web of Science for population-based studies on the public stigma in AUD and at least 1 other mental health condition, published between October 1, 2010 and December 20, 2020, thus including all studies published since the last systematic review on this topic. The study is registered with PROSPERO (registration number: CRD42020173054). RESULTS: We identified 20,561 records, of which 24 met the inclusion criteria, reporting results from 16 unique studies conducted in 9 different countries. Compared to substance-unrelated mental disorders, persons with AUD were generally less likely to be considered mentally ill, while they were perceived as being more dangerous and responsible for their condition. Further, the public desire for social distance was consistently higher for people with AUD. We found no consistent differences in the public stigma toward persons with AUD in comparison with other substance use disorders. CONCLUSION: The stigmatization of persons with AUD remains comparatively high and is distinct from that of other substance-unrelated disorders.
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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 | 0.010 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.014 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".