Sleep Quality and the Importance Women Place on Healthy Eating Interact to Influence Psychological Resilience
Bibliographic record
Abstract
Objectives: The impact women's daily habits may have on psychological resilience is not well understood. This cross-sectional analysis examined: (1) the impact of sleep quality on resilience, and (2) whether this association was modified by the importance women place on healthy eating. Methods: We collected data from 64 women (aged 18-67 years). The Pittsburgh Sleep Quality Index and Connor-Davidson Resilience Scale-10 assessed sleep quality and resilience, respectively, with lower scores indicating reduced resilience. One item assessed attitudes towards healthy eating. Linear regression models and 95% confidence intervals examined associations adjusted for age and income. Results: Reduced sleep quality was associated with a decreased resilience score (B=0.55, 95% CI: -1.06, -0.04, p=.04) when adjusted for age and income. After stratification, sleep quality and resilience were not associated among women who indicated healthy eating was very important. Among women who indicated healthy eating was less than very important, reduced sleep quality was associated with decreased psychological resilience (B=0.85, 95% CI: -1.55, -0.15, p=.02). Conclusions: Poor sleep quality was associated with reduced resilience among women. Placing a strong emphasis on healthy eating helped buffer the impact of poor sleep quality on women's psychological resilience.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.004 | 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".