The association between psychotic experiences and health-related quality of life: a cross-national analysis based on World Mental Health Surveys
Bibliographic record
Abstract
Psychotic experiences (PEs) are associated with a range of mental and physical disorders, and disability, but little is known about the association between PEs and aspects of health-related quality of life (HRQoL). We aimed to investigate the association between PEs and five HRQoL indicators with various adjustments. Using data from the WHO World Mental Health surveys (n = 33,370 adult respondents from 19 countries), we assessed for PEs and five HRQoL indicators (self-rated physical or mental health, perceived level of stigma (embarrassment and discrimination), and social network burden). Logistic regression models that adjusted for socio-demographic characteristics, 21 DSM-IV mental disorders, and 14 general medical conditions were used to investigate the associations between the variables of interest. We also investigated dose-response relationships between PE-related metrics (number of types and frequency of episodes) and the HRQoL indicators. Those with a history of PEs had increased odds of poor perceived mental (OR = 1.5, 95% CI = 1.2-1.9) and physical health (OR = 1.3, 95% CI = 1.0-1.7) after adjustment for the presence of any mental or general medical conditions. Higher levels of perceived stigma and social network burden were also associated with PEs in the adjusted models. Dose-response associations between PE type and frequency metrics and subjective physical and mental health were non-significant, except those with more PE types had increased odds of reporting higher discrimination (OR = 2.2, 95% CI = 1.3-3.5). Our findings provide novel insights into how those with PEs perceive their health status.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".