Mental health and quality of life of individuals with epilepsy during the war in Ukraine
Bibliographic record
Abstract
OBJECTIVE: To investigate the repercussions of the war in Ukraine on people with epilepsy (PWE), focusing on access to health care, seizure control, quality of life (QoL), psychological distress, anxiety, and depression; and to identify the key factors influencing these measures. METHODS: Consecutive PWE, ≥18 years of age, presenting to one of seven health centers across Ukraine were invited to complete a self-administered survey in 2023. The survey gathered information on clinical and demographic aspects, geographic displacement, and access to care and medications. It also contained five valid questionnaires exploring psychological distress (Kessler-10), QoL with the EuroQOL-5D-5L (EQ-5D-5L), depression with the Neurological Disorders in Epilepsy scale (NDDIE), anxiety with the Hospital Anxiety and Depression Scalae-Anxiety (HADS-A), and epilepsy severity with the Global Assessment of the Severity of Epilepsy scale (GASE). Multivariate linear regression models assessed the relationship between measures of mental health and QoL and their potential predictors. Ethical approval was obtained from the Institute of Neurology, Psychiatry and Narcology of NAMS of Ukraine, Ukraine. RESULTS: Among 305 participants (mean age 38 years), 40% were female and 44% had to change residence because of the war. Seizures worsened during the war in 52% of those with active epilepsy and 42% of those with well-controlled epilepsy. Difficulties accessing health care and anti-seizure medications occurred in 25% and 34% of PWE, respectively, and was worse among those who were displaced. According to the mental health instruments, 46% suffered psychological distress, 62% experienced anxiety, 50% were depressed, and 59% rated their epilepsy as somewhat severe or worse. Statistically significant predictors of psychological distress, anxiety, and depression included female gender, more severe epilepsy, increased seizures during the war, and requiring mental health support. SIGNIFICANCE: The war significantly disrupted access to health care and availability of medication in PWE, who suffer from significant anxiety, depression, and psychological distress. We identify high-risk factors that can guide resource allocation for prevention and treatment.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".