Change & Grow® Therapeutic Model for Addiction: Preliminary Results of an Interventional Study
Bibliographic record
Abstract
The last years have seen a paradigm shift concerning addictive disorders, indicating the necessity to study alternative therapeutic models. In this longitudinal study, the objective was to explore the impact of the Change & Grow® therapeutic model developed and used by VillaRamadas on certain psychological variables that frequently appear associated with addiction. A repeated measures (first and last weeks of treatment) design was used, and the psychological measurements were Beck’s Depression Inventory II (BDI-II), Suicide Ideation Questionnaire (SIQ), State-Trait Anxiety Inventory (STAI), and Montreal Cognitive Assessment (MoCA). Results include 26 (16 male and 10 female) patients. Age varied between 17 and 64 years (M = 35.62, SD = 12.60) and duration of treatment between 91 and 193 days (M = 147.35, SD = 27.05). The MoCA total result was significantly higher in the last week of treatment. The results of BDI-II, SIQ, and STAI (both state and trait) were all significantly lower. Neither duration of treatment nor self-reported motivation presented significant correlation values with the difference between measures for any of the variables. The Change & Grow® therapeutic model appears to have an impact on relevant psychological variables in patients admitted into treatment for addictive disorders.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".