Comparing outcomes for alcohol and drug abuse clients: A 6-month follow-up of clients who completed a residential treatment programme
Bibliographic record
Abstract
The present study examined the impact of an in-patient addiction treatment program and whether client-related treatment outcomes were moderated by addiction type: (1) alcohol only; (2) cocaine only or with alcohol; (3) cocaine with other substances; and (4) prescription drugs and/or cannabis. Clients completed self-reports of their substance use and quality of life during their first week in treatment and at 6-months post-discharge. Pre-treatment motivation and post-treatment aftercare attendance were also assessed. Overall, a positive impact of the addiction treatment programme was noted as clients reported a significant reduction in substance use and improvement in quality of life. Results also demonstrated that drug of choice impacted recovery status such that compared with cocaine poly substance clients, alcohol clients obtained significantly higher scores on quality of life measures at both pre- and 6-months post-treatment. However, cocaine poly substance clients were also significantly younger than alcohol only clients and were less likely to be married or employed. In general, substance use clients responded well to treatment. Some variability was noted among substance use groups—namely that cocaine poly-drug users obtained lowest levels of post-treatment reduction in substance use. The implications of such findings are discussed.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".