Sleep Location and Its Association with Caregiver Sleep Quality During Patient Hospital Admission
Bibliographic record
Abstract
During acute hospitalization, many caregivers decide to stay at the care recipient’s bedside over the course of several days or months, coping with a stressful situation and a poor sleeping environment. Our objective was to characterize caregiver sleep–wake cycles during care recipient hospital admission and test the association between sleep location (home versus hospital) and caregiver sleep. Eighty-six informal caregivers (78.8% female; age 55.47 ± 12.43 years) were recruited. For seven consecutive days, caregivers wore actigraphy devices and filled a sleep diary indicating whether they had slept at the hospital or at home. Caregiver insomnia symptoms, anxiety, and depression along with patient dependence were also assessed. Nighttime total sleep time, wake after sleep onset, sleep efficiency, sleep latency, and fragmentation index were described. Mixed-model analyses were used to evaluate the effect of the overnight location (home versus hospital) on caregiver sleep quality. In total, 38.4% of caregivers exhibited poor objective sleep efficiencies (< 80%), and 43% of caregivers reported having moderate to severe insomnia symptoms. Caregivers mostly slept at the hospital ( n = 53), but some slept at home ( n = 14) or between both locations ( n = 19). Mixed-model analyses using actigraphy showed that caregivers had significantly better sleep quality when resting at home regarding wake after sleep onset, fragmentation index, and sleep efficiency ( p < .05). Caregivers experienced poor sleep quality during care recipients’ hospitalization, specifically when sleeping at the hospital versus sleeping at home. Healthcare workers should ensure caregivers’ well-being and strongly encourage caregivers to rest at home whenever possible.
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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 | 0.001 | 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) | 0.002 | 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".