Process and outcome changes with relapse prevention versus 12‐Step aftercare programs for substance abusers
Bibliographic record
Abstract
AIMS: Presumptive support was sought for mechanisms of action whereby two conceptually distinct aftercare programs, relapse prevention (RP) and 12-Step facilitation (TSF), impact upon substance abusers. PATIENTS AND DESIGN: Adults who had just completed intensive treatment were assigned randomly to either RP (n=61) or TSF (n=70) aftercare programs. SETTING: Three residential treatment facilities. INTERVENTIONS: Trained counselors delivered to small groups a manualized aftercare program which focused either upon the utilization of cognitive-behavioral processes to orchestrate change through an individualized treatment plan (i.e. RP) or which sought to facilitate utilization of AA's 12 Steps (i.e. TSF). MEASUREMENTS: Process measures developed specifically to quantify either: (a) the changes in self-efficacy process in RP or (b) the utilization of AA's principles in TSF, as well as psychosocial and substance abuse indices were administered to all patients pre- and post-aftercare and at 6-month follow-up. FINDINGS: A significant relationship between changes in measures of self- efficacy for RP participants as well as a trend for a relationship between process-specific change for TSF participants partially satisfied the first condition for presumptive support. The fact that the intervention-specific mediators covaried with several outcome indices, and that removal of such mediators attenuated prediction of outcome met, respectively, the second and third conditions for presumptive support. CONCLUSION: Carefully orchestrated RP and TSF aftercare programs yield process changes that are related positively to improved outcome.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".