EXAMINING TREATMENT USE AMONG ALCOHOL-DEPENDENT INDIVIDUALS FROM A POPULATION PERSPECTIVE
Bibliographic record
Abstract
AIMS: To assess the prevalence of treatment use in lifetime and past year alcohol dependent respondents. To establish the proportion of problem drinkers who use alcohol treatment that just go to one treatment versus attending multiple different types of treatment in the same year. To explore what treatments are most likely to form part of a multiple treatment package. METHOD: Analysis of the 2001-2002 National Epidemiologic Survey of Alcohol and Related Conditions, a large (N = 43 039), representative survey of the non-institutionalized adult population of the USA. There were 4781 respondents who met criteria for a lifetime definition of alcohol dependence and 1484 respondents who met criteria for past year alcohol dependence. RESULTS: Prevalence of lifetime use of alcohol treatment was 25% among those with a lifetime diagnosis of alcohol dependence. Prevalence of past year use of alcohol treatment was 12% among respondents with past year alcohol dependence. Only one-third of past year treatment users had accessed just one type of alcohol treatment. CONCLUSIONS: While treatment services are only used by the minority of people with alcohol dependence, those people who do access alcohol treatment are likely to use several different alcohol treatment services in the same year.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".