A Good Night’s Sleep in Malta in 2023: A Cross-sectional Study Exploring Sleep Quality and its Determinants via Social Media
Bibliographic record
Abstract
Background: Sleep quality is affected by a plethora of different factors, although its relationship with chronic diseases is still unclear. This study explored perceived sleep quality and its associated determinants among the adult population of Malta. Study Design: A cross-sectional study. Methods: An anonymous online survey was distributed through social media targeting adults residing in Malta. Data pertaining to socio-demographic, medical history, lifestyle, well-being, sleep, and daytime sleepiness were gathered, and descriptive, univariant, and multiple binary logistic regression modelling analyses were performed. Results: A total of 855 adults responded, out of whom 35.09% (95% confidence interval [CI]: 31.90, 38.41) reported sleep difficulties, especially females (81.33%; 95% CI: 76.36, 85.49), while 65.33% (95% CI: 59.61, 70.65) reported suffering from chronic disease(s). Sleep problems were positively associated with multimorbidity (odds ratio [OR]: 2.17; 95% CI: 1.38, 3.40; P=0.001), sleeping<6 hours (OR: 3.79; 95% CI: 1.54, 9.30; P=0.040), and the presence of moderate anxiety symptoms (OR: 1.99; 95% CI: 1.10, 3.59; P=0.020). They were also related to the presence of mild (OR: 2.25; 95% CI: 1.46, 3.45; P=0.001), moderate (OR: 2.40; 95% CI: 1.24, 4.64; P=0.010), and moderately severe (OR: 15.35; 95% CI: 4.54, 31.86; P=0.001) depressive symptoms after adjusting for confounders. Conclusion: Chronic conditions, including anxiety and depression, along with short sleep duration, appear to contribute to poor sleep quality in Malta. A multifaceted approach is required to deal with the issue holistically and safeguard the health of current and future generations.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".