The impact of maternal depressive symptoms on health‐related quality of life in children with epilepsy: A prospective study of family environment as mediators and moderators
Bibliographic record
Abstract
PURPOSE: To examine the impact of maternal depressive symptoms (DS) on health-related quality of life (HRQL) in children with new-onset epilepsy and to identify family factors that moderate and mediate this relationship during the first 24 months after epilepsy diagnosis. METHODS: A sample of 339 mother-child dyads recruited from pediatric neurologists across Canada in the Health-related Quality of Life in Children with Epilepsy Study. Mothers' and neurologists' reports were collected at four times during the 24-month follow-up. Mothers' DS were measured using the Center for Epidemiological Studies Depression Scale (CES-D) and children's HRQL using the Quality of Life in Childhood Epilepsy (QOLCE). Data were modeled using individual growth curve modeling. KEY FINDINGS: Maternal DS were observed to have a negative impact on QOLCE scores at 24 months (β = -0.47, p < 0.0001) and the rate of change in QOLCE scores during follow-up (β = -0.04, p = 0.0250). This relationship was moderated by family resources (β = 0.25, p = 0.0243), and the magnitude of moderation varied over time (β = 0.09, p = 0.0212). Family functioning and demands partially mediated the impact of maternal DS on child HRQL (β = -0.07, p = 0.0007; β = -0.12, p = 0.0006). SIGNIFICANCE: Maternal DS negatively impact child HRQL in new-onset epilepsy during the first 24 months after diagnosis. This relationship is moderated by family resources and mediated by family functioning and demands. By adopting family centered approaches, health care professionals may be able to intervene at the maternal or family level to promote more positive outcomes in children.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".