A live-online mindfulness-based intervention for children living with epilepsy and their families: protocol for a randomized controlled trial of Making Mindfulness Matter©
Bibliographic record
Abstract
BACKGROUND: Epilepsy extends far beyond seizures; up to 80% of children with epilepsy (CWE) may have comorbid cognitive or mental health problems, and up to 50% of parents of CWE are at risk for major depression. Past research has also shown that family environment has a greater influence on children's and parents' health-related quality of life (HRQOL) and mental health than epilepsy-related factors. There is a pressing need for low-cost, innovative interventions to improve HRQOL and mental health for CWE and their parents. The aim of this randomized controlled trial (RCT) is to evaluate whether an interactive online mindfulness-based intervention program, Making Mindfulness Matter (M3), can be feasibly implemented and whether it positively affects CWE's and parents' HRQOL and mental health (specifically, stress, behavioral, depressive, and anxiety symptoms). METHODS: This parallel RCT was planned to recruit 100 child-parent dyads to be randomized 1:1 to the 8-week intervention or waitlist control and followed over 20 weeks. The intervention, M3, will be delivered online and separately to parents and children (ages 4-10 years) in groups of 4-8 by non-clinician staff of a local community epilepsy agency. The intervention incorporates mindful awareness, social-emotional learning skills, and positive psychology. It is modeled after the validated school-based MindUP program and adapted for provision online and to include a parent component. DISCUSSION: This RCT will determine whether this online mindfulness-based intervention is feasible and effective for CWE and their parents. The proposed intervention may be an ideal vector to significantly improve HRQOL and mental health for CWE and their parents given its low cost and implementation by community epilepsy agencies. TRIAL REGISTRATION: ClinicalTrials.gov NCT04020484 . Registered on July 16, 2019.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".