The <scp>WPA</scp> Expert International Advisory Panel for Early Intervention in Psychosis in Low‐ and <scp>Middle‐Income</scp> Countries: an update on recent relevant activities
Bibliographic record
Abstract
In 2019, the WPA set up an Expert International Advisory Panel for Early Intervention in Psychosis (EIP) in Low- and Middle-Income Countries (LMICs), as part of a presidential initiative1 linked to the WPA Action Plan 2020-20232-4. Here we present an update on recent activities related to that initiative. The WPA has promoted several symposia and keynote/plenary lectures at international conferences on EIP models in LMICs, their clinical effectiveness, cultural contextualization, and implementation challenges. These conferences included the 21st World Congress of Psychiatry (virtual, October 2021); the WPA/UK National Institute for Health and Care Research (NIHR) Webinar on EIP in LMICs (December 2021); the WPA Thematic Conference "Public Health and Associated Opportunities" (Lahore, Pakistan, March 2022); the 22nd World Congress of Psychiatry (Bangkok, August 2022); and the WPA Thematic Conference "Early Intervention across the Lifespan" (Athens, June 2022). Some recent examples (illustrative, not an exhaustive list) of EIP programmes in LMICs include the Schizophrenia Research Foundation (SCARF)'s dedicated EIP service in Chennai, India5, developed in collaboration with the Prevention and Early Intervention Program for Psychosis in Montreal6; the University of Chile High-risk Intervention Program for Ultra-High-Risk Youth7; and a pilot EIP service in Malawi8. Understanding that inadequate mental health workforce, fragmented health care systems and scarcity of research and implementation capacity are significant barriers to introducing such programmes in LMICs, the Warwick-India-Canada (WIC) network was formed with a shared strategic vision to reduce the burden of psychotic disorders in resource-poor settings9. This network brought together knowledge and expertise of four internationally recognized institutions: the University of Warwick, UK; the McGill University, Canada; the All India Institute of Medical Sciences (AIIMS), New Delhi, India; and the SCARF, Chennai, India. The largest cohort of first-episode psychosis cases in LMIC settings was recruited and followed through the WIC programme at SCARF and AIIMS. A comprehensive package of biopsychosocial care, ready to use in any LMIC setting, has been developed. The integration of faith/traditional/indigenous healing with mental health services in LMICs appears a promising way for community detection of untreated psychosis, but there are significant challenges in such collaborations. Trusting relationships are difficult to build, ongoing training and supervision beyond the project timelines are hard to deliver, and sustainability is more easily promised than achieved. The COllaborative Shared care to IMprove Psychosis Outcome (COSIMPO) trial10 assessed the effectiveness of a collaborative shared care (CSC) for psychosis delivered by traditional healers and primary health care providers, compared to enhanced care-as-usual, in Ghana and Nigeria. Participants randomized to the CSC model had significantly lower symptom scores at 6-month follow-up. CSC led to greater reductions in overall care costs. Such models offer the prospect of scaling up across LMICs. A new programme of such collaborations is under way in Nigeria and Bangladesh. Digital technology can play a vital role in overcoming resource and infrastructure limitations in LMICs11. The WIC early psychosis study9 co-designed the Saksham app for people with schizophrenia and their caregivers. The app is ready for public roll out in India. Telepsychiatry offers another innovative approach to reaching individuals in rural regions who may otherwise not have access to treatment. Several models of telepsychiatry have been launched in India: the SCARF STEP tele-psychiatry model12; the psychiatristonweb application13; the Ganiyari model; and the National Institute of Mental Health and Neurosciences (NIMHANS) hub-and-spoke model14. Emerging evidence suggests that these models improve medication and appointment adherence, and lead to reductions in relapses and fewer hospitalizations.
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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 | 0.028 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.024 | 0.012 |
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".