Factors associated with poor sleep quality in midlife Singaporean women: The Integrated Women’s Health program (IWHP)
Bibliographic record
Abstract
Objective: To examine factors associated with poor sleep quality in community-dwelling midlife women. Methods: Healthy women (aged 45-69 years) of Chinese, Malay and Indian ethnicities attending well-women clinics at the National University Hospital, Singapore, completed the Pittsburgh Sleep Quality Index (PSQI). A PQSI score >5 denoted poor sleep quality. The women filled out validated questionnaires covering menopausal and genito-urinary symptoms, and mental health. Physical performance was measured. Bone mineral density and visceral adiposity were assessed by dual energy X-ray absorptiometry. Binary logistic regression analyses assessed independent factors for poor sleep. Results: Poor sleep quality was reported in 38.2% of women (n = 1094, mean age: 56.4 ± 6.2 years). Indian women had higher sleep disturbance scores than Chinese women (mean ± SD: 1.33 ± 0.58 vs 1.17 ± 0.49). Malays experienced more daytime dysfunction (0.54 ± 0.60 vs 0.33 ± 0.55) and had a higher overall PSQI score (6.00 ± 3.31 vs 5.02 ± 2.97) than the Chinese. A low education level (aOR: 1.76, 95% CI: 1.01-3.05), feelings of irritability (2.67, 1.56-4.60) and vaginal dryness (1.62, 1.03-2.54) were associated with poor sleep quality in the adjusted multivariable model. Women with moderate to severe disability were ∼3 times (2.99, 1.20-7.44) more likely to experience less than ideal sleep quality, while urinary incontinence (1.53, 1.08-2.17) and breast cancer history (2.77, 1.36-5.64) were also associates of poor sleep quality. Conclusion: Self-reports of education level, irritability, vaginal dryness, disability, urinary incontinence, and breast cancer history were independently related to poor sleep. Ethnic differences suggest the need for targeted interventions among the ethnic groups.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".