Context and contact: a comparison of patient and family engagement with early intervention services for psychosis in India and Canada
Bibliographic record
Abstract
Abstract Background It is unknown whether patient disengagement from early intervention services for psychosis is as prevalent in low- and middle-income countries (LMICs) like India, as it is in high-income countries (HICs). Addressing this gap, we studied two first-episode psychosis programs in Montreal, Canada and Chennai, India. We hypothesized lower service disengagement among patients and higher engagement among families in Chennai, and that family engagement would mediate cross-site differences in patient disengagement. Methods Sites were compared on their 2-year patient disengagement and family engagement rates conducting time-to-event analyses and independent samples t tests on monthly contact data. Along with site and family involvement, Cox proportional hazards regression included known predictors of patient disengagement (e.g. gender). Results The study included data about 333 patients (165 in Montreal, 168 in Chennai) and their family members (156 in Montreal, 168 in Chennai). More Montreal patients (19%) disengaged before 24 months than Chennai patients (1%), χ2(1, N = 333) = 28.87, p < 0.001. Chennai families had more contact with clinicians throughout treatment (Cohen's d = −1.28). Family contact significantly predicted patient disengagement in Montreal (HR = 0.87, 95% CI 0.81–0.93). Unlike in Chennai, family contact declined over time in Montreal, with clinicians perceiving such contact as not necessary (Cohen's d = 1.73). Conclusions This is the first investigation of early psychosis service engagement across a HIC and an LMIC. Patient and family engagement was strikingly higher in Chennai. Maintaining family contact may benefit patient engagement, irrespective of context. Findings also suggest that differential service utilization may underpin cross-cultural variations in psychosis outcomes.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".