Sleep quality among inpatients of Spanish public hospitals
Bibliographic record
Abstract
Sleep is a complex process and is needed both in health and illness. Deprivation of sleep is known to have multiple negative physiological effects on people's bodies and minds. Despite the awareness of these harmful effects, previous studies have shown that sleep is poor among hospitalised patients. We utilized an observational design with 343 patients recruited from medical and surgical units in 12 hospitals located in nine Spanish regions. Sociodemographic and clinical characteristics of patients were collected. Sleep quality at admission and during hospitalisation was measured by the Pittsburgh Sleep Quality Index. Sleep quantity was self-reported by patients in hours and minutes. Mean PSQI score before and during hospitalisation were respectively 8.62 ± 4.49 and 11.31 ± 4.04. Also, inpatients slept about an hour less during their hospital stay. Lower educational level, sedative medication intake, and multi-morbidity was shown to be associated with poorer sleep quality during hospitalisation. A higher level of habitual physical activity has shown to correlate positively with sleep quality in hospital. Our study showed poor sleep quality and quantity of inpatients and a drastic deterioration of sleep in hospital versus at home. These results may be helpful in drawing attention to patients' sleep in hospitals and encouraging interventions to improve sleep.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".