Patient- and family-reported experiences of their treating teams in early psychosis services in Chennai, India and Montreal, Canada
Bibliographic record
Abstract
BACKGROUND: Cross-cultural psychosis research has mostly focused on outcomes, rather than patient and family experiences. Therefore, our aim was to examine differences in patients' and families' experiences of their treating teams in early intervention services for psychosis in Chennai, India [low- and middle-income country] and Montreal, Canada [high-income country]. METHODS: Patients (165 in Chennai, 128 in Montreal) and their families (135 in Chennai, 110 in Montreal) completed Show me you care, a patient- and family-reported experience measure, after Months 3, 12, and 24 in treatment. The measure assesses the extent to which patients and families view treating teams as being supportive. A linear mixed model with longitudinal data from patient and family dyads was used to test the effect of site (Chennai, Montreal), stakeholder (patient, family), and time on Show me you care scores. This was followed by separate linear mixed effect models for patients and families with age and gender, as well as symptom severity and functioning as time-varying covariates. RESULTS: As hypothesized, Chennai patients and families reported more supportive behaviours from their treating teams (β=4.04; β= 9, respectively) than did Montreal patients (Intercept =49.6) and families (Intercept=42.45). Higher symptom severity over follow-up was associated with patients reporting lower supportive behaviours from treating teams. Higher levels of positive symptoms (but lower levels of negative symptoms) over follow-up were associated with families reporting lower supportive behaviours from treating teams. There was no effect of time, age, gender and functioning. CONCLUSIONS: The levels to which treating teams are perceived as supportive may reflect culturally shaped attitudes (e.g., warmer attitudes towards healthcare providers in India vis-à-vis Canada) and actual differences in how supportive treating teams are, which too may be culturally shaped. Being expected to be more involved in treatment, Chennai families may receive more attention and support, which may further reinforce their involvement. Across contexts, those who improve over follow-up may see their treating teams more positively.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".