Depressive symptoms among mothers of children with epilepsy: A review of prevalence, associated factors, and impact on children
Bibliographic record
Abstract
The impact of epilepsy is not limited to the child experiencing seizures, but affects all members of the family. As primary caregivers, mothers are particularly at risk for experiencing increased depressive symptoms and risk for clinical depression. The objective of this systematic review was to critically assess available evidence regarding the prevalence, associated factors, and impact of maternal depressive symptoms on child outcomes in epilepsy. Using a modified version of the Quality Index, studies were rigorously evaluated in terms of reporting, external validity, and internal validity. Limitations in the study designs and analytic techniques of previous research are discussed, and study methods to overcome these barriers are presented in order to advance this research area. Up to 50% of mothers of children with epilepsy are at risk for clinical depression. Correlates of maternal depressive symptoms include a number of modifiable risk factors such as role ambiguity, worry, and satisfaction with relationships. In addition, studies suggest that depressive symptoms in mothers have a negative impact on child outcomes in epilepsy including behavior problems and health-related quality of life. The overall mean score on the Quality Index was 9.7, indicating a midrange quality score, suggesting a need for more methodologically robust studies.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".