Implementation of Early Intervention in Psychosis Initiatives in Latin America and the Caribbean: A Case Study
Bibliographic record
Abstract
Abstract Psychosis is a serious mental illness, with onset in adolescence and young adulthood. Few early intervention in psychosis (EIP) programs exist in the Global South, where most of the world’s youth live. Addressing this gap requires understanding implementation contexts, pathways and challenges. This study examines EIP initiatives in Latin America and the Caribbean (LAC) and explores implementers’ perspectives on scaling them. A single-case study design was employed. Guided by the Exploration, Preparation, Implementation, and Sustainment (EPIS) framework, we conducted semi-structured interviews with EIP implementers across LAC and gathered policy documents. Data was coded and analyzed using thematic analysis. Twenty-five participants from 10 countries described 26 initiatives, including clinical and research programs, guidelines, and a technical standard. Themes were mapped onto the EPIS framework. In Exploration, participants highlighted key motivators, the influence of collaborations with foreign researchers, contextual adversity (e.g., poverty, stigma), and the role of Indigenous cosmologies and religious traditions in shaping care pathways. In Preparation, they emphasized difficulties in culturally adapting models from high-income countries (HICs), limited staff awareness, and resource shortages. In Implementation, participants described how initiatives operated in local contexts (e.g., research programs offering care to address unmet needs), how they were generally well received by patients and staff, and the shortage of psychosocial interventions. In sustainment, few initiatives persisted; participants pointed to dependence on international funding, limited policy support, capacity, and awareness. While EIP was valued, national dissemination of HIC-based programs was considered unfeasible. EIP development in LAC has occurred amid structural and resource limitations affecting many LMICs. Implementers’ proposals: task-shifting; simplified care packages; leveraging extant services; and enhancing early psychosis literacy— represent feasible strategies to support EIP across LAC. Recommendations for future research, including the involvement of service users and their families and the adaptation of implementation frameworks to context, are shared.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".