Developing a realist theory of community-based residential substance use treatment
Bibliographic record
Abstract
Community-based residential treatments for substance use disorders (SUDs) have mixed effectiveness, which has been hypothesized to be due to the variability of program offerings across treatments. Therefore, the purpose of the present study was to develop a realist program theory of a community-based residential treatment centre to understand which components of treatment are most effective, who benefits from treatment, and why. The study was completed in collaboration with a community-based residential treatment program located in Canada. A realist evaluation was conducted using qualitative interviews (N = 27) with program stakeholders. Transcripts were analyzed using thematic analysis to identify Context-Mechanism-Outcome (CMO) configurations that create the program theory. The analysis identified an overarching organizational context and 11 CMO configurations that comprise the program theory. The CMOs were grouped into three themes (i.e. relational, psychological, and diversity), underscoring the multiple pathways in which recovery is achieved for individuals with SUDs. Across the themes, the importance of building peer recovery relationships, creating structure, and inter-agency collaborations were highlighted. The results of the present study offer a contribution to the literature in understanding how, why, and for whom community-based residential treatment for SUDs is effective while offering specific recommendations to best tailor treatments to individuals.
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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 | 0.012 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.025 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| 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".