Recovery Within a Community-based Residential Treatment for Substance Use Problems: Program Theory Development and the Impact of COVID-19
Bibliographic record
Abstract
Community-based residential treatment for substance use disorders (SUDs) has mixed effectiveness in reducing substance use and substance-related problems, which has been hypothesized to be due to the variability of program offerings across treatments. Further, due to the recent COVID-19 pandemic, community-based residential treatment centres have had to adapt their programming in order to follow necessary infection control mandates, leading to considerable changes in programming. The purpose of the present dissertation was twofold: 1) to develop a realist program theory of a community-based residential treatment centre in order to understand what components of treatment are most effective, who benefits from treatment, and why; and 2) to document and synthesize the experiences of stakeholders at a treatment centre during COVID-19. The present dissertation was completed in collaboration with Renascent, a community-based residential treatment program located in Toronto, Canada. In order to meet the first purpose, a realist evaluation was conducted using qualitative interviews (N=27) with program stakeholders. The results of the thematic analysis identified four overarching organizational contexts and 11 Context-Mechanism-Outcome (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. Study two involved a qualitative investigation of the experiences of key stakeholders at the treatment centre during COVID-19 using an inductive approach. Interviews were conducted with stakeholders (N=13) and analyzed using thematic analysis. The interviews and subsequent analysis identified a number of themes (i.e., service accessibility, difficulties with interpersonal connection, and increased stress experienced by frontline staff). Across the two studies, the importance of building peer recovery relationships, creating structure, and inter-agency collaborations was highlighted. The results of both studies provide a template as to how implementation of novel practices can occur and how tensions can be overcome through collaboration. The results of the present dissertation offer a significant contribution to the literature in understanding how, why, and for whom community-based residential treatment for SUDs is effective, while also providing timely data on the experiences of attending and implementing treatment during a pandemic.
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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 | 0.022 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.019 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| 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".