Early intervention in psychosis programs in Africa, Asia and Latin America; challenges and recommendations
Bibliographic record
Abstract
Background: While early intervention in psychosis (EIP) programs have been increasingly implemented across the globe, many initiatives from Africa, Asia and Latin America are not widely known. The aims of the current review are (a) to describe population-based and small-scale, single-site EIP programs in Africa, Asia and Latin America, (b) to examine the variability between programs located in low-and-middle income (LMIC) and high-income countries in similar regions and (c) to outline some of the challenges and provide recommendations to overcome existing obstacles. Methods: EIP programs in Africa, Asia and Latin America were identified through experts from the different target regions. We performed a systematic search in Medline, Embase, APA PsycInfo, Web of Science and Scopus up to February 6, 2024. Results: Most EIP programs in these continents are small-scale, single-site programs that serve a limited section of the population. Population-based programs with widespread coverage and programs integrated into primary health care are rare. In Africa, EIP programs are virtually absent. Mainland China is one of the only LMICs that has begun to take steps toward developing a population-based EIP program. High-income Asian countries (e.g. Hong Kong and Singapore) have well-developed, comprehensive programs for individuals with early psychosis, while others with similar economies (e.g. South Korea and Japan) do not. In Latin America, Chile is the only country in the process of providing population-based EIP care. Conclusions: Financial resources and integration in mental health care, as well as the availability of epidemiological data on psychosis, impact the implementation of EIP programs. Given the major treatment gap of early psychosis in Africa, Latin America and large parts of Asia, publicly funded, locally-led and accessible community-based EIP care provision is urgently needed.
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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 | 0.026 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".