Patient-reported outcome measures in early psychosis: Evaluating the psychometric properties of the single-item Self-Reported Health and Self-Reported Mental Health measures in Chennai, India and Montreal, Canada
Bibliographic record
Abstract
Aim: Patient-reported outcome measures (PROMs) provide valuable information and promote shared decision-making but are infrequently used in psychosis. Self-Rated Health (SRH) and Self-Rated Mental Health (SRMH) are single-item PROMs in which respondents rate their health and mental health from ‘poor’ to ‘excellent’. We examined the psychometric properties of the SRH and SRMH in early intervention for psychosis contexts in Chennai, India and Montreal, Canada. Methods: Assessments were completed in Tamil/English in Chennai and French/English in Montreal. Test-retest reliability included data from 59 patients in Chennai and Montreal. Criterion validity was examined against clinician-rated measures of depression, anxiety, positive and negative symptoms, and a quality-of-life PROM for 261 patients in Chennai and Montreal. Results: SRH and SRMH had good to excellent test-retest reliability (ICC>0.63) at both sites and in English and Tamil (but not French). Results for criterion validity were mixed. Whereas in Montreal, low SRH was associated with not being in positive symptom remission, and poorer functioning and quality of life, SRH was associated only with functioning in Chennai. No associations were found for SRMH in Montreal. In Chennai, however, low SRMH was associated with not being in positive symptom remission and poorer functioning. Conclusions: Our work advances knowledge of more feasibly integrating single-item PROMs into clinical settings. Importantly, it highlights how PROMs may perform differently across languages and contexts. More critical work is needed to understand if discrepancies between PROMs and CROMs are indicative of poor validity of PROMs or “valid” differences between patient and clinician perceptions.
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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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".