Duration of Untreated Psychosis in Chinese and Mauritian: Impact of Clinical Characteristics and Patients’ and Families’ Perspectives on Psychosis
Bibliographic record
Abstract
BACKGROUND: Duration of untreated psychosis (DUP) is a potentially modifiable prognostic factor of course and prognosis of psychiatric disorders. Few studies have demonstrated that different cultural backgrounds or perspectives on psychosis may be important factors to the DUP. This study attempted to explore whether the DUP was different in Chinese and Mauritians and to clarify potential influencing factors to a long DUP (>3 months). METHODS: 200 patients from China and 100 patients from Mauritius were enrolled in the study. Their respective family members were also recruited. Demographic and clinical characteristics were collected, and the Internalized Stigma of Mental Illness (ISMI) scale was adapted to measure the stigma in all subjects. Binary logistic regression analysis was used to find the potential influencing factors to the long DUP. RESULTS: 35.3% of the enrolled patients had a long DUP. No significant difference was found in frequency of long DUP between the two countries. Chinese patients had relatively less perceptions of stigma. Furthermore, Chinese patients with a long DUP had more perception of breakup due to mental illness (OR = 2.22, p = 0.04) and more families' perception of the patient being disinherited due to mental illness (OR = 6.47, p = 0.01). Mauritian patients with a long DUP were less likely to have high monthly income (OR = 0.12, p<0.01), while they had less patients' awareness of mental illness (OR = 0.31, p<0.05) and less families' awareness of mental illness (OR = 0.14, p<0.01). CONCLUSION: The results of this study underlined the importance of DUP in economic conditions, racial and sociocultural factors, and public awareness on psychosis in developing countries.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".