Promoting Self‐Change With Alcohol Abusers: A Community‐Level Mail Intervention Based on Natural Recovery Studies
Bibliographic record
Abstract
BACKGROUND: By using a public health approach to the treatment of alcohol problems, this study analyzed the efficacy and cost analysis of two versions of a community-level mail intervention to promote self-change among alcohol abusers who had never sought help or treatment. METHODS: A total of 825 participants who responded to media solicitations were randomly assigned to one of two interventions: (a) for bibliotherapy/drinking guidelines (n = 411), they were given two pamphlets with information about the effects of alcohol and guidelines for low-risk drinking and self-monitoring, and (b) for motivational enhancement/personalized feedback (n = 414), personalized advice/feedback was provided on the basis of the participants' assessment of their drinking and related behaviors. RESULTS: Although both groups exhibited significant reductions in drinking from 1 year before to 1 year after intervention, there were no significant differences between the two interventions for any variable. This suggests that the materials, irrespective of whether they were personalized, facilitated the reduction of drinking. Cost analysis revealed that a brief mail intervention could reduce drinking at a very low cost per participant (US$46 to US$97). CONCLUSIONS: A brief community-level mail intervention for problem drinkers who had never sought treatment resulted in sizable reductions in alcohol use over the year after the intervention compared with the year before. Furthermore, many of those with poorer outcomes engaged in a natural stepped-care process by seeking help. These results, coupled with the low cost to deliver the intervention, suggest that public health campaigns could have a substantial effect on reducing alcohol problems and associated costs as well as getting some individuals into treatment. Such an approach would represent a shift from the alcohol field's long-standing clinical focus to a broader public health perspective.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".