Effective interventions to support recovery of people with psychosis and their families across socio-ecological levels in low-income and middle-income countries: a systematic review
Bibliographic record
Abstract
Summary Background The aim of this systematic review was to synthesise evidence on the effectiveness and cost-effectiveness of interventions to support the recovery of people living with psychosis and their families in low-income and middle-income countries (LMICs). Methods We searched nine databases for articles published from January 2001 to January 2024 without language restrictions. Studies were eligible if they enrolled people living with psychosis or family members, and tested a psychoeducational, psychological, social, economic or service intervention or delivery or implementation strategy aimed at improving outcomes of people with psychosis. Eligible studies were required to compare outcomes with an alternative condition, using any prospective evaluation study design in a LMIC setting. We extracted summary data from published papers and appraised risk of bias using the Effective Public Health Practice Project tool. We prioritised the reporting of recovery-orientated outcomes including social inclusion, personal recovery, reduced stigma and discrimination and human rights protections. We conceptualised the person living with psychosis in their context (individual, family, organisation and community) based on the socio-ecological model of disability and highlighted studies intervening and measuring outcomes across multiple socio-ecological levels. Protocol registration: PROSPERO (CRD42022330298). Findings A total of 310 individual studies including data from 34,435 participants in 37 countries were included. Aggregate data from a further five meta-analyses, comprising data from 130 individual studies were also included. The majority of studies (77%) were conducted in upper middle-income countries. There was a dominance of studies evaluating impacts of interventions on individual-level mental health and functioning and a paucity of studies measuring the recovery-orientated outcomes prioritised by people living with psychosis. There were modest effects for comprehensive interventions involving family, psychosocial rehabilitation and care close to home provided by trained specialists however their scalability in resource-limited settings is unclear. Over half the studies were considered to have a high risk of bias. Interpretation There is a need for studies that evaluate scalable interventions supporting recovery with comprehensive and contextualised outcome measures and for greater investment in strengthening capacity to conduct rigorous psychosis research across LMICs. Funding None. Research in context Evidence before this study Recent World Health Organization (WHO) guidance on human rights-based, recovery-orientated community mental health care featured markedly few case studies of good practice for people with psychosis in low-income and middle-income countries (LMICs). Systematic reviews of interventions for psychosis in LMICs have been narrow in focus and reporting outcomes, and limited to English language publications. Added value of this study This systematic review is the most comprehensive synthesis to date of psychosis interventions in LMICs. Inclusion is not restricted by publication language. We highlight studies reporting recovery-oriented outcomes prioritised by people living with psychosis and impacts of interventions across levels of the socio-ecological model of disability. While being particularly relevant to LMICs, our findings also contribute a useful perspective for high income settings. Implications of all the available evidence Most interventions were targeted at the individual and focused on mental health and functioning outcomes, with few evaluations of impact on social inclusion and other valued outcomes. There is some evidence in support of specialist-delivered comprehensive interventions involving family, psychosocial rehabilitation and care close to home, but effect sizes were small-to-modest, and many intervention types and delivery agents have not been adequately tested, especially in LICs and rural settings. There is a clear need to develop comprehensive and contextualised measures for recovery-orientated outcomes and to invest in strengthening capacity to conduct rigorous research on interventions for psychosis in LMICs.
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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 | 0.017 | 0.066 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.009 |
| Bibliometrics | 0.012 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 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".