Interest in the Use of Herbal Supplements to Close the Treatment Gap for Hazardous Alcohol Use Among Men Who Have Sex With Men: Secondary Analysis of a Cross-Sectional Study
Bibliographic record
Abstract
BACKGROUND: Hazardous alcohol consumption is highly prevalent for men who have sex with men (MSM). The 4 treatments currently approved by the Food and Drug Administration for alcohol use are reaching an alarmingly low percentage of people who would benefit from a reduction in their alcohol use. There is increasing interest in alternative methods of treatment, such as herbal supplements, to address hazardous drinking. However, research on the acceptability of alternative pharmacotherapies among MSM remains limited. OBJECTIVE: We examined the prevalence and correlates of expressing interest in using herbal supplements for alcohol treatment among MSM with hazardous alcohol consumption. METHODS: We conducted a secondary data analysis from a cross-sectional study of MSM who use alcohol, conducted from March 2015 to July 2017 in San Francisco, California, to assess the overall prevalence of interest in using herbal supplements to help reduce alcohol consumption. Associations between expressing interest in herbal supplements and demographic, social, and clinical characteristics were examined using bivariate and multivariable logistic regression models. RESULTS: One-third (66/200, 33%) of the participants expressed interest in an herbal supplement for reducing alcohol consumption. In the multivariable analyses, weekly binge drinking (adjusted odds ratio [aOR] 2.85, 95% CI 1.17-6.93), interest in abstaining from alcohol use (aOR 5.04, 95% CI 1.46-17.40), higher severity of alcohol dependence score (aOR 1.22, 95% CI 1.04-1.41), and interest in naltrexone (aOR 3.22, 95% CI 2.12-4.91) were independently associated with higher odds of being interested in using an herbal supplement to reduce alcohol consumption, adjusting for age, race or ethnicity, and education. CONCLUSIONS: We found that MSM who have hazardous drinking habits, more severe alcohol dependence, and interest in pharmacotherapy were more likely to express interest in using an herbal supplement for reducing alcohol consumption. To our knowledge, this is the first study that has evaluated correlates of interest in herbal supplements for alcohol use among MSM. As researchers implement novel alcohol treatment studies, they should focus on recruitment efforts among MSM with a motivation to reduce their alcohol use patterns.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".