Health‐related Quality of Life in Children with Epilepsy: Development and Validation of Self‐report and Parent Proxy Measures
Bibliographic record
Abstract
PURPOSE: To answer a need to include and measure accurately the impact and burden of epilepsy as outcomes of interventions with affected children, we developed and validated self-report and parent-proxy respondent health-related quality of life (HRQL) instruments for preadolescent children with epilepsy. METHODS: We combined qualitative and quantitative research methods. Items were extracted from focus group discussions involving children with epilepsy and their parents. We created scales formatted with alternative paired options of forced responses and used factor analysis to generate relevant subscales and reduce the number of items. We checked internal consistency, assessed test-retest reliability 10-14 days apart, and documented construct validity. RESULTS: A sample of 381 children with epilepsy, age 6-15 years, and their parents independently completed a 67-item questionnaire, from which we chose five items for each subscale. The measures share four subscales, but each measure has an additional distinct subscale. The children and parents could discern differences and report differentially between the various aspects of the HRQL. Internal consistency measured with Cronbach's alpha was acceptable for all subscales; construct validity has been demonstrated from the testing of several hypotheses. Test-retest reliability examined with the intraclass correlation coefficient was satisfactory for the parents and for children age 8 years and older. The correlations between the mothers' and children's responses was poor to moderate. CONCLUSIONS: The data demonstrate sound psychometric properties for both related measures, which are easy to administer for children with epilepsy who are 8 years and older and their parents. The subscales encompass HRQL dimensions judged most important by children with epilepsy for the self-report measure and by parents for the proxy response measure. The parent-proxy measure should be useful as a complement to the child self-report measure in evaluating the validity of parental assessment of the child's health status; in longitudinal outcome research; and in HRQL assessment of children who are unable to respond independently.
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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 | 0.021 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".