Comparing Treatment Delays and Pathways to Early Intervention Services for Psychosis in Urban Settings in India and Canada
Bibliographic record
Abstract
Abstract IntroductionAlthough extensively studied in high-income countries (HICs) and less so in low- and middle-income countries (LMICs), pathways to care and treatment delays in early psychosis have not been compared across contexts. We compared pathways to early intervention for psychosis in an HIC (Montreal, Canada) and an LMIC (Chennai, India). We hypothesised that the duration of untreated psychosis (DUP) would be longer in Chennai.MethodsThe number of contacts preceding early intervention, referral sources, first contacts, and DUP and its referral and help-seeking components of first-episode psychosis patients at both sites were similarly measured and compared using chi-square analyses and t-tests/one-way ANOVAs.ResultsOverall and help-seeking DUPs of Chennai (N = 168) and Montreal (N = 165) participants were not significantly different. However, Chennai patients had shorter referral DUPs [mean = 12.0 ± 34.1 weeks vs. Montreal mean = 13.2 ± 28.7 weeks; t(302.57) = 4.40; p < .001] as the early intervention service was the first contact for 44% of them (vs. 5% in Montreal). Faith healers comprised 25% of first contacts in Chennai. Those seeing faith healers had significantly shorter help-seeking but longer referral DUPs. As predicted, most (93%) Montreal referrals came from medical sources. Those seeing psychologists/counsellors/social workers as their first contact had longer DUPs.ConclusionDifferences in cultural views about mental illnesses and organizational structures shape pathways to care and their associations with treatment delays across contexts. Both formal and informal sources need to be targeted to reduce delays. Early intervention services being the first portal where help is sought can reduce DUP especially if accessed early on in the illness course.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.000 | 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".