Bibliographic record
Abstract
Research suggests increased risk for adverse psychosocial outcomes and poor health-related quality of life and emotional well-being (EWB) in children with epilepsy compared to their healthy peers. The factors associated with poor EWB and the course of EWB in these children remains unclear. The objectives of this study were to: investigate the relationship between epilepsy-related and family factors and children’s EWB two years after the diagnosis of epilepsy; identify the average group trajectory of EWB in children with newly-diagnosed epilepsy over the first two years; and investigate whether we can identify subgroups of children with epilepsy that can be better represented with yet unidentified unique trajectories to describe their course of EWB, rather than using a single homogeneous group trajectory to represent all children.\nData came from a multi-centre 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). EWB was measured using the Quality of Life in Childhood Epilepsy Questionnaire. Multiple regression assessed the relationship between epilepsy-related factors and EWB and tested possible mediation or moderation effects of family factors. Latent growth modeling and multinomial logistic regression was used to identify trajectories of EWB, the factors associated with each trajectory, and predictors of group membership to a particular trajectory.\nBehavioural problems, family functioning, family demands, and family resources were associated with poor EWB two-years post-diagnosis. Parental depressive symptoms were partially mediated by family functioning and by family demands. Family resources played a dual mediator/moderator role, moderating the relationship between severity of epilepsy and EWB.\nTwo linear trajectories were identified, with the same set of factors associated with baseline EWB for both trajectories, but factors differed in their association with EWB across time for the two trajectories. The level of severity of epilepsy and family resources predicted a child’s membership to a particular trajectory.\nPoor EWB in children with epilepsy is associated with several epilepsy-related and family factors. After a diagnosis of epilepsy, family factors appear to be the most important influences on changes in EWB over time so efforts to strengthen the family environment may warrant attention.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".