Efficacy of Remotely Delivered Evidence-Based Psychosocial Treatments for Schizophrenia-Spectrum Disorders: A Series of Systematic Reviews and Meta-Analyses
Bibliographic record
Abstract
BACKGROUND: Schizophrenia is among the most persistent and debilitating mental health conditions worldwide. The American Psychological Association (APA) has identified 10 psychosocial treatments with evidence for treating schizophrenia and these treatments are typically provided in person. However, in-person services can be challenging to access for people living in remote geographic locations. Remote treatment delivery is an important option to increase access to services; however, it is unclear whether evidence-based treatments for schizophrenia are similarly effective when delivered remotely. STUDY DESIGN: The current study consists of a series of systematic reviews and meta-analyses examining the evidence-base for remote-delivery of each of the 10 APA evidence-based treatments for schizophrenia. RESULTS: Of the 10 treatments examined, only cognitive remediation (CR), cognitive-behavioral therapy (CBT), and family psychoeducation had more than 2 studies examining their efficacy for remote delivery. Remote delivery of CBT produced moderate effects on symptoms (g = 0.43) and small effects on functioning (g = 0.26). Remote delivery of CR produced small-moderate effects on neurocognition (g = 0.35) and small effects on functioning (g = 0.21). There were insufficient studies of family psychoeducation with equivalent outcome measures to assess quantitatively, however, studies of remotely delivered family psychoeducation suggested that it is feasible, acceptable, and potentially effective. CONCLUSIONS: Overall, the evidence-base for remotely delivered treatment for schizophrenia is limited. Studies to date suggest that remote adaptations may be effective; however, more rigorous trials are needed to assess efficacy and methods of remote delivery that are most effective.
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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 | 0.020 | 0.051 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.049 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 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".