Uses of strength-based interventions for people with serious mental illness: A critical review
Bibliographic record
Abstract
BACKGROUND: For the past 3 decades, mental health practitioners have increasingly adopted aspects and tools of strength-based approaches. Providing strength-based intervention and amplifying strengths relies heavily on effective interpersonal processes. AIM: This article is a critical review of research regarding the use of strength-based approaches in mental health service settings. The aim is to discuss strength-based interventions within broader research on recovery, focussing on effectiveness and advances in practice where applicable. METHOD: A systematic search for peer-reviewed intervention studies published between 2001 and December 2014 yielded 55 articles of potential relevance to the review. RESULTS: Seven studies met the inclusion criteria and were included in the analysis. The Quality Assessment Tool for Quantitative Studies was used to appraise the quality of the studies. Our review found emerging evidence that the utilisation of a strength-based approach improves outcomes including hospitalisation rates, employment/educational attainment, and intrapersonal outcomes such as self-efficacy and sense of hope. CONCLUSION: Recent studies confirm the feasibility of implementing a high-fidelity strength-based approach in clinical settings and its relevance for practitioners in health care. More high-quality studies are needed to further examine the effectiveness of strength-based approaches.
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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 | 0.009 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.008 | 0.005 |
| 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".