Emotional well‐being in children with epilepsy: Family factors as mediators and moderators
Bibliographic record
Abstract
OBJECTIVE: Our objective was to examine the relationships of factors associated with children's emotional well-being 2 years after diagnosis, and to examine if these relationships are mediated or moderated by family factors. METHODS: Data came from a multicenter prospective cohort study of children with newly diagnosed epilepsy from across Canada (Health-Related Quality of Life in Children with Epilepsy Study; HERQULES, n = 373). Emotional well-being was assessed using the Quality of Life in Childhood Epilepsy Questionnaire (QOLCE-55). The relationships between clinical factors, family factors, and emotional well-being were assessed using multiple regression analyses. RESULTS: Family functioning, family stress, and repertoire of resources that the families had to adapt to stressful events were significantly associated with poor emotional well-being 2 years after diagnosis (p < 0.05) in the multivariable analysis. The effect of parental depressive symptoms was partially mediated by family functioning and family stress (p < 0.01 and p = 0.02, respectively). Family resources acted as a moderator in the relationship between severity of epilepsy and emotional well-being (p < 0.05). SIGNIFICANCE: Based on our findings, efforts to strengthen the family environment may warrant attention. We suggest that clinicians take a family centered care approach by including families in treatment planning. Family centered care has been shown to improve family well-being and coping and in turn may reduce the impact of clinical factors on emotional well-being to improve long-term health-related quality of life.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".