Self‐esteem mediates mental health outcomes in young people with epilepsy
Bibliographic record
Abstract
Summary Objective To evaluate the extent to which self‐esteem mediates the impacts of epilepsy‐specific and environmental factors on mental health outcomes in young people with epilepsy. Methods A prospective cohort of 480 young people with epilepsy and their families participated in five visits over 28 months. We collected data on clinical seizure burden, cognitive comorbidity, peer and parental support, self‐esteem, and self‐reported mental health symptoms. We used structural equation modeling to specify and test relationships among these constructs simultaneously. Direct, indirect, and total effects were estimated with confidence intervals constructed through bias‐corrected bootstrapping. Results Self‐esteem mediated the effects of clinical seizure burden ( = 0.23, 95% confidence interval [0.05, 0.42]) and peer support ( = −0.15, 95% CI [−0.28, −0.03]) on mental health. There were no mediating effects of parental support ( = −0.07, 95% CI [−0.14, 0.00]) or cognitive comorbidity ( = −0.01, 95% CI [−0.02, 0.01]) on mental health. Significance We found evidence that self‐esteem mediates the impact that both clinical seizure burden and peer support have on mental health outcomes, indicating that assessment of and interventions targeting self‐esteem may be appropriate for young people with epilepsy. Supporting self‐esteem could mitigate negative influences on mental health, whether from resistant epilepsy or low peer support.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".