Health Profiles of Clients in Substance Abuse Treatment: A Comparison of Clients Dependent on Alcohol or Cocaine With Those Concurrently Dependent
Bibliographic record
Abstract
UNLABELLED: The purpose of this study was to assess whether, among clients receiving substance abuse treatment (n = 616), those dependent on alcohol or cocaine differed significantly from those concurrently dependent on both drugs in terms of physical, mental, social, and economic harms as well as substance use behaviors. METHODS: Clients from five substance abuse treatment agencies presenting with a primary problem of cocaine or alcohol were classified into three groups as dependent on: (1) alcohol alone, (2) cocaine alone, or (3) both cocaine and alcohol (i.e. concurrent dependence). Participants completed a self-administered questionnaire that included details of their drug and alcohol use, physical health, mental health, social health, economic health, and demographic characteristics. RESULTS: The concurrent group drank similar amounts of alcohol as those in the alcohol group and used similar amounts of cocaine as the cocaine group. The alcohol group had significantly (p < .05) poorer health profiles than the concurrent group across most variables of the four health domains. An exception was significantly more accidental injuries (p < .05) in the alcohol group. In both bivariate and multivariate analyses, the concurrent group had significantly (p < .05) more accidental injuries, violence, and overdoses than the cocaine group. As well, the concurrent group had significantly (p < .05) higher scores on the anxiety and sexual compulsion scales than the cocaine group, controlling for demographic variables. CONCLUSION: These findings can aid health care professionals to better respond to issues related to concurrent dependence of cocaine and alcohol.
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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 | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".