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%), χ 2 (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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".