Improving psychosocial health and employment outcomes for individuals receiving methadone treatment: a realist synthesis of what makes interventions work
Bibliographic record
Abstract
BACKGROUND: For over 50 years, methadone has been prescribed to opioid-dependent individuals as a pharmacological approach for alleviating the symptoms of opioid withdrawal. However, individuals prescribed methadone sometimes require additional interventions (e.g., counseling) to further improve their health. This study undertook a realist synthesis of evaluations of interventions aimed at improving the psychosocial and employment outcomes of individuals on methadone treatment, to determine what interventions work (or not) and why. METHODS: The realist synthesis method was utilized because it uncovers the processes (or mechanisms) that lead to particular outcomes, and the contexts within which this occurs. A comprehensive search process resulted in 31 articles for review. Data were extracted from the articles, and placed in four templates to assist with analysis. Data analysis was an iterative process and involved comparing and contrasting data within and across each template, and cross checking with original articles to determine key patterns in the data. RESULTS: For individuals on methadone, engagement with an intervention appears to be important for improved psychosocial and/or employment outcomes. The engagement process involves attendance at interventions as well as an investment in what is offered. Three intervention contexts (often in some combination) support the engagement process: a) client-centered contexts (or those where clients' psychosocial and/or employment needs/issues/skills are recognized and/or addressed); b) contexts which address clients' socio-economic conditions and needs; and, c) contexts where there are positive client-counselor and/or peer relationships. There is some evidence that sometimes ongoing engagement is necessary to maintain positive outcomes. There is also some evidence that complete abstinence from drugs (e.g., cocaine, heroin) is not necessary for engagement. CONCLUSIONS: It is important to consider how the contexts of interventions might elicit and/or support clients' engagement. Further research is needed to explore how an individual's background (e.g., involvement with different interventions over an extended period) may influence engagement. Long-term engagement may be necessary to sustain some positive outcomes although how long is unclear and requires further research. Engagement can occur without complete abstinence from such drugs as cocaine or heroin, but additional research is required as engagement may be influenced by the extent and type of drug use.
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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 | 0.067 | 0.153 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.014 | 0.011 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.013 | 0.007 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| 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".