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Record W4414258272 · doi:10.1101/2025.09.10.25334585

The Global Epilepsy Needs Study (GENS): A mixed-methods, multi-country exploration of the unmet psychosocial and everyday needs of people with epilepsy

2025· preprint· en· W4414258272 on OpenAlexaff
Gus A. Baker, Sandeep Kumar Bagga, Donna Walsh, Claire Nolan, Charlotte Hooker, Pepa Gonzalez Parrao, J. Helen Cross, Ma. Marta Bertone, I Peña García, Adam Jallow Janneh, Alison Kukla, Ding Ding, Latica Friedrich, Lécio Figueira Pinto, Gagandeep Singh, Chahnez Triki, Leya Raj, Allan Reese

Bibliographic record

VenuemedRxiv · 2025
Typepreprint
Languageen
FieldMedicine
TopicEpilepsy research and treatment
Canadian institutionsOntario Brain Institute
Fundersnot available
KeywordsPsychosocialMultidisciplinary approachEpilepsyEveryday lifeQuality of life (healthcare)Qualitative researchHealth care

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.021
metaresearch head score (Gemma)0.014
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.021
Threshold uncertainty score0.110

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0210.014
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0050.004
Science and technology studies0.0030.002
Scholarly communication0.0020.003
Open science0.0020.006
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.027
GPT teacher head0.358
Teacher spread0.331 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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