0913 EXPLORING SLEEP DISTURBANCE AMONG FAMILY CAREGIVERS OF CHILDREN WITH MEDICAL COMPLEXITY
Bibliographic record
Abstract
Family caregivers of children with complex care needs that depend on medical technology (e.g. home ventilation) are relied upon to provide skillful, vigilant homecare 24-hours/day. This responsibility has been linked to chronic sleep disturbance, placing family caregivers at risk of poor health outcomes. To inform testing of a sleep promoting intervention, the following questions have guided this research: What factors influence sleep among family caregivers? How do family caregivers appraise the utility of sleep-promoting interventions? To what degree do family caregivers perceive sleep and related health outcomes as problematic? A multi-site cross-sectional observational design using mixed data sources is underway. Participants include family caregivers with a child dependent on medical technology at night, > 3 months homecare experience and no diagnosed sleep disorders. Interviews have been completed for qualitative content analysis. Quantitative measures administered include: Q- sort using images/text depicting sleep-promoting interventions; Scale of the Problem to measure participant’s appraisal of their sleep/health on a unipolar Likert scale ranging from 0 to 4. Nine participants have completed study procedures with further sample diversification planned. Emerging qualitative themes include: 1) caregiver (vigilance/worries, mood/emotions, sleep habits, parenting preferences), 2) child (age/development, equipment use, sleep quality, care needs), 3) family (financial/household stressors, other child-care, employment demands), 4) environment (lights/noises, personal technologies, housing, sleep location); and, 5) homecare (presence of night nursing, provider competence, family-centred service, resource constraints). To date, participants rank mindfulness/yoga (7/9), brief daily-exercise (6/9) and enhanced use of respite (6/9) their ‘top choices’ among evidence-based interventions. Moreover, family caregivers appraise their sleep quality (3.2/4), daily stress (3.2/4), sleep quantity (3.0/4) and fatigue (3.0/4) to be problematic. Findings suggest multiple factors contribute to poor quality and inadequate quantity of sleep among family caregivers when a child is medically complex. Family caregivers assign value to addressing sleep problems and support testing of evidence-based sleep promoting interventions with demonstrated effectiveness in other caregiver (older-adult) populations. Funding for the study is gratefully acknowledged from the SickKids Foundation and Holland Bloorview Research Institute.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 teacher head, 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".