Exploring the transfer effects of social skills interventions for individuals with serious mental illness: A scoping review
Bibliographic record
Abstract
Objective: Adults with serious mental illness face unique social challenges. Deficits in social cognition and social isolation can impede one’s ability to engage in meaningful relationships, which can negatively impact a person’s recovery. While the literature has established the proximal effects of social skill interventions, the extent to which the treatment effects generalize to clients’ everyday lives is less conclusive. The objective of this study was to explore the literature and identify the existing social skills interventions for adults with serious mental illness and to understand the techniques used to promote generalizability beyond the clinical context. Research Design and Methods: A scoping review methodology, using the Arksey and O’Malley framework, was applied. Medline, PsycInfo, and Embase and relevant journals were searched to retrieve articles between 1995 and 2021 that reported on social skills interventions with a follow-up period or mention of transfer of skills. Results: Interventions for social skills training were classified into six categories: social cognition training, traditional social skills training, virtual reality, integrated social skills training, CBT-based training, and contextual social skills training. Four primary themes of potential generalizability techniques used in social skills interventions were identified as: a) targeting cognition, b) facilitating and supporting social opportunities, c) engaging clients’ inner social networks, and d) ensuring goals are concrete and client-centred. Conclusions: For skills training to impact personal recovery and social functioning of adults with serious mental illness, it is necessary to incorporate generalizability into interventions to ensure skills transfer from the clinical setting to their daily lives.
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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 | 0.014 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.012 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".