Sleep disturbance in family caregivers of children who depend on medical technology
Bibliographic record
Abstract
Objectives Family caregivers of children who depend on medical technology (CMT) provide highly skilled care up to 24 hours per day. Sleep disruption places family caregivers at risk for poor health and related outcomes that threaten their long-term caregiving capacity. Few studies exist that have measured sleep in family caregivers, and most have relied entirely on subjective measures. Methods In a prospective cohort study, family caregivers of CMT (n=42) and caregivers of healthy children (n=43) were recruited. Actigraphy data and a concurrent sleep diary were collected for 6 days/7 nights. Measures of sleep quality, depression, sleepiness, fatigue and quality of life were also administered. Results Family caregivers of CMT averaged fewer hours of sleep per night (mean (SD)) (6.56 ± 1.4 vs 7.21 ± 0.6, p=0.02) of poorer quality (7.75 ± 2.9 vs 5.45 ± 2.8, p<0.01) than the control group. Three times as many family caregivers of CMT scored in the range for significant depressive symptomatology (12(33%) vs 4(10%), p=0.01) and experienced excessive daytime sleepiness (16(38%) vs 5(12%), p<0.01). Fatigue was also more problematic among family caregivers of CMT (22.12 ± 9.1 vs 17.44 ± 9.0, p=0.02). Conclusions Family caregivers of CMT are at risk of acute and chronic sleep deprivation, psychological distress and impaired daytime function that may threaten their capacity for sustained caregiving. Family caregivers of CMT may be important targets for screening for sleep disorders and the development of novel sleep-promoting interventions.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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".