Changes Over Time in the Health of Caregivers of Children With Health Problems: Growth-Curve Findings From a 10-Year Canadian Population-Based Study
Bibliographic record
Abstract
OBJECTIVES: We used Canadian population-based data to examine changes in the health of caregivers of children with complex health problems compared with caregivers of healthy children over a 10-year time period. METHODS: The National Longitudinal Survey of Children and Youth collected data biennially from 9401 children and their caregivers in 6 waves from 1994-1995 to 2004-2005. We conducted growth-curve analyses of these data to model self-reported general health and depressive symptoms for 4 groups of caregivers: caregivers of healthy children, and caregivers of children with 1, 2, or at least 3 of 4 conceptually distinct indicators of child health problems. We modeled covariates for children (age, gender, only-child status) and caregivers (age, gender, education, income, marital status). RESULTS: After we controlled for covariates, caregiver health outcomes worsened incrementally with increasing complexity of child health problems. Change in self-reported general health and depressive symptoms over the 10-year period was consistent across all groups of caregivers. CONCLUSIONS: Poorer health among caregivers of children with health problems can persist for many years and is associated with complexity of child health problems. Attention to parental health should form a component of health care services for children with health problems.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".