Sleep disturbance in family caregivers of children who depend on medical technology: A systematic review
Bibliographic record
Abstract
PURPOSE: Society relies on family caregivers of children who depend on medical technology (e.g. mechanical ventilation), to provide highly skilled and vigilant care in their homes 24 hours per day. Sleep disturbance is among the most common complaints of these caregivers. The purpose of this review is to systematically examine studies reporting on sleep outcomes in family caregivers of technology dependent children. METHODS: All relevant databases were systematically searched: MEDLINE, EMBASE, PsycINFO and CINAHL. Given the heterogeneity of the studies, a qualitative analysis was completed and thus results of this review are presented as a narrative. RESULTS: Thirteen studies were retrieved that met eligibility criteria for inclusion. All of the studies reported on family caregivers of children with medical complexity living at home. Moreover, all of the studies relied entirely on self-report, not objective sleep measures. No intervention studies were found. Sleep disturbance was found to be common (51-100%) along with caregiver reports of poor sleep quality. Sleep quantity was seldom measured, but was found in the few studies that did, to be approximately 6 hours, or less than recommendations for optimal health and daytime function. Multiple caregiver, child and environmental factors were also identified that may negatively influence caregiver sleep, health and daytime function. CONCLUSION: Findings of this review suggest that family caregivers of children with medical complexity who depend on medical technology achieve poor sleep quality and quantity that may place them at risk of the negative consequences of sleep deprivation. Recommendations for practice include that health care providers routinely assess for sleep disturbance in this vulnerable population. The review also suggests that studies using objective sleep measurement are needed to more fully characterize sleep and inform the development of targeted interventions to promote sleep in family caregivers of technology dependent children.
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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 | 0.011 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.011 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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".