Perceived helpfulness of treatment for alcohol use disorders: Findings from the World Mental Health Surveys
Bibliographic record
Abstract
AIM: We examined prevalence and factors associated with receiving perceived helpful alcohol use disorder (AUD) treatment, and persistence in help-seeking after earlier unhelpful treatment. METHODS: Data came from 27 community epidemiologic surveys of adults in 24 countries using the World Health Organization World Mental Health surveys (n = 93,843). Participants with a lifetime history of treated AUD were asked if they ever received helpful AUD treatment, and how many professionals they had talked to up to and including the first time they received helpful treatment (or how many ever, if they had not received helpful treatment). RESULTS: 11.8% of respondents with lifetime AUD reported ever obtaining treatment (n = 9378); of these, 44% reported that treatment was helpful. The probability of obtaining helpful treatment from the first professional seen was 21.8%; the conditional probability of subsequent professionals being helpful after earlier unhelpful treatment tended to decrease as more professionals were seen. The cumulative probability of receiving helpful treatment at least once increased from 21.8% after the first professional to 79.7% after the seventh professional seen, following earlier unhelpful treatment. However, the cumulative probability of persisting with up to seven professionals in the face of prior treatments being unhelpful was only 13.2%. CONCLUSION: Fewer than half of people with AUDs who sought treatment found treatment helpful; the most important factor was persistence in seeking further treatment if a previous professional had not helped. Future research should examine how to increase the likelihood that AUD treatment is found to be helpful on any given contact.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".