Warwick-India-Canada (WIC) global mental health group: rationale, design and protocol
Bibliographic record
Abstract
INTRODUCTION: The primary aim of the National Institute of Health Research-funded global health research group, Warwick-India-Canada (WIC), is to reduce the burden of psychotic disorders in India. India has a large pool of undetected and untreated patients with psychosis and a treatment gap exceeding 75%. Evidence-based packages of care have been piloted, but delivery of treatments still remains a challenge. Even when patients access treatment, there is minimal to no continuity of care. The overarching ambition of WIC programme is to improve patient outcomes through (1) developing culturally tailored clinical interventions, (2) early identification and timely treatment of individuals with mental illness and (3) improving access to care by exploiting the potential of digital technologies. METHODS AND ANALYSIS: This multicentre, multicomponent research programme, comprising five work packages and two cross-cutting themes, is being conducted at two sites in India: Schizophrenia Research Foundation, Chennai (South India) and All India Institute of Medical Sciences, New Delhi (North India). WIC will (1) develop and evaluate evidence-informed interventions for early and first-episode psychosis; (2) determine pathways of care for early psychosis; (3) investigate the efficacy and cost-effectiveness of community care models, including digital and mobile technologies; (4) develop strategies to reduce the burden of mental illnesses among youth; (5) assess the economic burden of psychosis on patients and their carers; and (6) determine the feasibility of an early intervention in psychosis programme in India. ETHICS AND DISSEMINATION: This study was approved by the University of Warwick's Biomedical and Scientific Research Ethics Committee (reference: REGO-2018-2208), Coventry, UK and research ethics committees of all participating organisations. Research findings will be disseminated through peer-reviewed scientific publications, presentations at learnt societies and visual media.
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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 | 0.074 | 0.054 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.072 | 0.015 |
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".