Early Intervention in Psychosis and Management of First Episode Psychosis in Low- and Lower-Middle-Income Countries: A Systematic Review
Bibliographic record
Abstract
BACKGROUND AND HYPOTHESIS: People with first-episode psychosis (FEP) in low- and lower-middle-income countries (LMIC) experience delays in receiving treatment, resulting in poorer outcomes and higher mortality. There is robust evidence for effective and cost-effective early intervention in psychosis (EIP) services for FEP, but the evidence for EIP in LMIC has not been reviewed. We aim to review the evidence on early intervention for the management of FEP in LMIC. STUDY DESIGN: We searched 4 electronic databases (Medline, Embase, PsycINFO, and CINAHL) to identify studies describing EIP services and interventions to treat FEP in LMIC published from 1980 onward. The bibliography of relevant articles was hand-searched. Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed. STUDY RESULTS: The search strategy produced 5074 records; we included 18 studies with 2294 participants from 6 LMIC countries. Thirteen studies (1553 participants) described different approaches for EIP. Pharmacological intervention studies (n = 4; 433 participants) found a high prevalence of metabolic syndrome among FEP receiving antipsychotics (P ≤ .005). One study found a better quality of life in patients using injectables compared to oral antipsychotics (P = .023). Among the non-pharmacological interventions (n = 3; 308 participants), SMS reminders improved treatment engagement (OR = 1.80, CI = 1.02-3.19). The methodological quality of studies evidence was relatively low. CONCLUSIONS: The limited evidence showed that EIP can be provided in LMIC with adaptations for cultural factors and limited resources. Adaptations included collaboration with traditional healers, involving nonspecialist healthcare professionals, using mobile technology, considering the optimum use of long-acting antipsychotics, and monitoring antipsychotic side effects.
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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 | 0.006 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".