Dropout Rates in Psychosocial Interventions for People With Both Severe Mental Illness and Substance Misuse: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Introduction: Over the years, many psychosocial interventions for individual having both a psychotic spectrum disorder and a substance use disorder diagnoses have been developed and studied. However, there is a high dropout rate among this clinical population. Objectives: This meta-analysis aims to replicate a previous meta-analysis on the effects of psychosocial treatment for dual disorders, while including and determining the dropout rates in those type of interventions. Method: Based on a Cochrane systematic review conducted in 2019, we conducted a meta-analysis including 40 randomized clinical trials on psychosocial treatment among persons suffering from schizophrenia spectrum disorder and substance use disorder. Results: A dropout rate of 27,2% was obtained. Stimulants use significantly affected dropout rates. Age, gender, diagnosis, alcohol and cannabis abuse, and duration of treatment did not affect dropout rates. Conclusion: The 27,2% rate of dropout from psychosocial treatment highlights the need to engage participants having a dual diagnosis from the start by focusing on therapeutic alliance and motivation for treatment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.025 | 0.047 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.045 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".