The Global Epilepsy Needs Study (GENS): A mixed-methods, multi-country exploration of the unmet psychosocial and everyday needs of people with epilepsy
Bibliographic record
Abstract
OBJECTIVE: While epilepsy research has largely focused on medical management and clinical outcomes, less attention has been given to the unmet psychosocial and everyday needs of people with epilepsy (PWE), particularly in low- and middle-income countries. The Global Epilepsy Needs Study (GENS) aims to explore these needs, which are integral to the quality of life, by capturing both shared and context-specific experiences. METHODS: The GENS employed a patient-centered approach and mixed-methods design, integrating a cross-sectional survey and semi-structured interviews in 15 countries. The survey, available in 12 languages, captured experiences across 10 life domains (n = 5296 participants). Interviews were analyzed thematically using a phenomenological approach and Colaizzi's method, exploring lived experiences in depth (n = 75 participants). To ensure meaningful involvement and diverse representation, national patient associations, healthcare professionals, researchers, and people with lived experience guided each stage of the research process, from study design to manuscript development. RESULTS: Quantitative and qualitative data were integrated using a joint display method. This analysis generated five Generalized Themes across all life domains: (1) managing uncertainty and redefining daily life; (2) living with risk, social exclusion, and misunderstanding; (3) challenges in navigating inaccessible systems; (4) consequences of inaccessible or inadequate information; and (5) complex epilepsy needs demand more than standard approaches. SIGNIFICANCE: This first-of-its-kind global study offers a comprehensive picture of the psychosocial and everyday challenges faced by PWE. It establishes a critical evidence base for epilepsy organizations, highlights the need for healthcare systems to adopt holistic, multidisciplinary approaches, and calls on policymakers to invest in systemic reforms that safeguard dignity, inclusion, and life opportunities. Future research should explore the needs of underserved groups, including caregivers, individuals with complex epilepsy, women, and those in low-income or rural settings. PLAIN LANGUAGE SUMMARY: This study examined the everyday challenges faced by people with epilepsy in different parts of the world. It showed that many people struggle with fear, stigma, poor access to services, and a lack of clear information and support. Women, people in rural areas, and those in low-income settings often face the greatest challenges. The study calls for better education, more support for caregivers, and improvements across health, work, school, and transport systems. It also shows the need for more research to understand and respond to the real-life needs of people most impacted by epilepsy.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".