Prevalence of involuntary commitment for alcohol dependence.
Bibliographic record
Abstract
BACKGROUND: Alcohol dependence is a chronic relapsing illness. While some patients respond to treatment, others continue to drink alcohol and suffer serious health effects such as delirium tremens, liver failure, heart disease, and central nervous effects. One option society has used to force treatment and abstinence is the legal mechanism of "involuntary commitment." The goal of this study was to determine the utilization of "involuntary commitment" among the 72 counties in Wisconsin. METHODS: A statewide survey was conducted using a mailed survey to assess the current use of this treatment option. RESULTS: Forty-nine counties responded to the survey (68%); the mean number of commitments in the last year was 5 with a range of 0 to 30. Of the petitioners who participated in the commitment, 98% were family members, 62% were friends, 49% were physicians, and 26% were counselors. Over half of the respondents (53%) felt that the process was effective in helping people deal with their alcoholism. DISCUSSION: The overall perception among those surveyed is that involuntary commitment for the treatment of alcohol dependence can help addicted persons, but its utilization varies by county in Wisconsin. Physicians may consider exploring the use of this legal process to assist patients struggling with alcoholism.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".