Similar and different? A cross-cultural comparison of the prevalence, course of and factors associated with suicidal thoughts and behaviors in first-episode psychosis in Chennai, India and Montreal, Canada
Bibliographic record
Abstract
Background:Data from high-income countries (HICs) show a high risk of suicidal thoughts and behaviors (STBs) in first-episode psychosis (FEP). It is unknown, however, whether rates and associated factors differ in low- and middle-income countries (LMICs).Aims:We therefore aimed to compare the 2-year course of STBs and associated factors in persons with FEP treated in two similarly structured early intervention services in Chennai, India and Montreal, Canada.Method:To ensure fit to the data that included persons without STBs and with varying STBs’ severity, a hurdle model was conducted by site, including known predictors of STBs. The 2-year evolution of STBs was compared by site with mixed-effects ordered logistic regression.Results:The study included 333 FEP patients (168 in Chennai, 165 in Montreal). A significant decrease in STBs was observed at both sites (OR = 0.87; 95% CI [0.84, 0.90]), with the greatest decline in the first 2 months of follow-up. Although three Chennai women died by suicide in the first 4 months (none in Montreal), Chennai patients had a lower risk of STBs over follow-up (OR = 0.44; 95% CI [0.23, 0.81]). Some factors (depression, history of suicide attempts) were consistently associated with STBs across contexts, while others (gender, history of suicidal ideation, relationship status) were associated at only one of the two sites.Conclusions:This is the first study to compare STBs in FEP between two distinct geo-sociocultural contexts (an HIC and an LMIC). At both sites, STBs reduced after treatment initiation, suggesting that early intervention reduces STBs across contexts. At both sites, for some patients, STBs persisted or first appeared during follow-up, indicating need for suicide prevention throughout follow-up. Our study demonstrates contextual variations in rates and factors associated with STBs. This has implications for tailoring suicide prevention and makes the case for more research on STBs in FEP in diverse contexts.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".