Prevalence and trajectories of depressive symptoms among mothers of children with newly diagnosed epilepsy: A longitudinal 10‐year study
Bibliographic record
Abstract
OBJECTIVE: Parental depression significantly impacts children's health and well-being. This study aimed to (1) estimate the prevalence of depressive symptoms, at six time points, among mothers over the first 10 years after their child was diagnosed with epilepsy; (2) identify trajectories of maternal depressive symptoms over time; and (3) identify baseline factors associated with each trajectory. METHODS: Data came from the Health-Related Quality of Life in Children with Epilepsy Study (HERQULES), a Canada-wide prospective cohort study. Data on child, parent, and family characteristics were collected at the time of diagnosis, and follow-ups at 0.5, 1, 2, 8, and 10 years. Maternal depressive symptoms were measured using the Center for Epidemiological Studies Depression Scale. Trajectories of depressive symptoms were evaluated using latent class growth modeling, and multinomial logistic regression was used to identify baseline factors associated with each trajectory. RESULTS: A total of 356 mothers participated in the study, of whom 57% scored in the at-risk range for major depression disorder (period-prevalence). Four unique trajectories were identified as follows: "Low-Stable" (29% of mothers), "Intermediate-Stable" (46%), "High-Stable" (20%), and "High-Decreasing" (5%). Positive family environment was consistently associated with a better trajectory of depressive symptoms over time; other significant factors included type of seizures, child cognitive comorbidity, maternal age, and maternal education. SIGNIFICANCE: A substantial proportion of mothers of children with epilepsy are at risk for depression, and this risk is stable over the long term. Family environment at the time of diagnosis has long-term and persistent effects and may be an ideal target for interventions.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".