Children receiving treatment for cancer and their caregivers: A mixed methods study of their sleep characteristics
Bibliographic record
Abstract
BACKGROUND: Sleep has a significant impact on the daily functioning of children and their parents. The purpose of this study was to describe and gain an understanding of the sleep characteristics of children receiving treatment for cancer and their caregivers using a mixed methods concurrent triangulation design. PROCEDURE: Data were collected from questionnaires completed by 35 caregivers of children receiving treatment for cancer and compared to similar data from 64 caregivers of healthy children. RESULTS: There was considerable variability in the sleep characteristics of the children receiving treatment for cancer as reported by their caregivers. However, as a group, the magnitude of their sleep problems, particularly among the adolescents, was significantly greater than that of the comparison group and had the potential to impact negatively on their participation in everyday life. They had poorer sleep efficiency. Many impairments, particularly pain, nightmares, and symptoms associated with steroid administration, impacted their sleep. Their caregivers also experienced an increased prevalence of sleep issues, which impacted their daytime functioning. Suggestions to prevent and treat the sleep issues of children receiving treatment for cancer focused on practicing good sleep habits, ensuring a safe, secure, and comfortable sleep environment, and using non-pharmaceutical and pharmaceutical interventions to address impairments interfering with sleep. Caregivers noted that it was important to take care of themselves by getting sufficient sleep and accepting help from others. CONCLUSIONS: Sleep issues are prevalent in families of children receiving treatment for cancer and should be assessed routinely and addressed.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".