Assessing Stigma Towards Chronic Insomnia: The Role of Health Status and Life Quality
Bibliographic record
Abstract
Abstract Background The objective of this study was to explore the stigma and related influencing factors in patients with chronic insomnia disorder (CID). Methods A total of 70 CID patients and 70 healthy controls (CON) were enrolled in the study. The Pittsburgh Sleep Quality Index (PSQI) and the 17-Item Hamilton Depression Rating Scale (HAMD-17) were used to assess sleep quality and depressive symptoms, respectively. The Chinese-Beijing version of the Montreal Cognitive Assessment scale (MoCA-C) was used to assess cognitive function. Stigma and life quality were measured using the Chronic Stigma Scale and the 36-Item Short-Form Health Survey (SF-36). Results The ratio of individuals with stigma was significantly different between CID and CON groups (C2 = 35.6, p < 0.001). Compared with the CON group, the CID group had higher scores for total stigma (U = 662.0, p < 0.001), internalized stigma (U = 593.0, p < 0.001), enacted stigma (U = 1568.0, p < 0.001), PSQI (U = 2485.0, p < 0.001) and HAMD-17 (U = 69.5, p < 0.001) as well as lower scores for MoCA-C (U = 3997.5, p < 0.001) and SF-36 for the items of physical role (U = 1560.5, p < 0.001), body pain (U = 1633.5, p < 0.001), general health (U = 1194.0, p < 0.001), vitality (U = 1169.5, p < 0.001), social function (U = 1703.0, p = 0.001), emotional role (U = 1451.5, p < 0.001), mental health (U = 1147.0, p < 0.001) and health transition (U = 1341.0, p < 0.001). Partial correlation analysis showed that different items of the Chronic Stigma Scale were positively correlated with illness duration, PSQI and HAMD-17 scores, while negatively correlated with one or more items of the SF-36. Multivariate regression analysis showed that illness duration and the mental health domain of the SF-36 were independent risk factors for one or more items of stigma in CID patients. Conclusion Patients with CID have an increased risk of stigma. Moreover, illness duration and mental health may be primary factors related to stigma.
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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 | 0.004 |
| 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 | 0.000 |
| 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".