Impact of Caring for a Child With Cancer on Parents’ Health-Related Quality of Life
Bibliographic record
Abstract
PURPOSE: To compare the health-related quality of life (QOL) of parents of children who are undergoing treatment for cancer with that of Canadian population norms and to identify important parent and child predictors of parental QOL. PATIENTS AND METHODS: A total of 411 respondents of 513 eligible parents were recruited from five pediatric oncology centers in Canada between November 2004 and February 2007. Parents were asked to complete a questionnaire booklet that included a measure of adult QOL (SF-36), a measure of child health status (functional status IIR), and questions to assess health-promoting self-care actions (eg, sleep, diet, and exercise habits) and characteristics of the child with cancer (eg, relapse status, time since diagnosis, prognosis, treatment intensity). RESULTS: Compared with population norms, parents of children with cancer reported poorer physical and psychosocial QOL in all psychosocial domains (effect sizes range, -0.71 to -1.58) and in most physical health domains (effect sizes range, -0.08 to -0.63). Parent characteristics associated with better parental QOL included better eating, exercise and sleep habits, younger age, and higher income. Child characteristics associated with better parental QOL included better child health status (functional status IIR scores), lower treatment intensity, and longer time since diagnosis. CONCLUSION: Parents of children with cancer report poorer QOL compared with population norms. Interventions directed at parents should be included as part of the treatment plan for a child with cancer. Modifiable variables associated with poorer parental QOL, such as sleep quality and diet and exercise habits, indicate those parents most likely to experience poor QOL and may be potential areas for intervention.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".