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Record W2992843917 · doi:10.11575/prism/37269

Adult patient perspectives of the unknowns of living with epilepsy - results from a focus group study.

2019· article· en· W2992843917 on OpenAlexaboutno aff
Jeanie Y. Y. Lee, Jennifer Gelfand, Sundus Khan, Rachel Crooks, Colin B. Josephson, Samuel Wiebe, Scott B. Patten, Lawrence Korngut, Eric E. Smith, Pamela Roach

Bibliographic record

VenuePRISM (University of Calgary) · 2019
Typearticle
Languageen
FieldHealth Professions
TopicAdolescent and Pediatric Healthcare
Canadian institutionsnot available
Fundersnot available
KeywordsEpilepsyFocus (optics)Focus groupPsychologyMedicinePsychiatrySociology

Abstract

fetched live from OpenAlex

Background/Objectives: Epilepsy is one of the most common and debilitating neurological conditions that affects nearly 50 million people worldwide, yet there remains a stigma around this condition, which can impact the information-seeking behaviours of patients. As the Brain and Mental Health Research Clinics develop a website about registry-based research, including patient-facing areas, it is important to understand how patients look for information, and the types of information they are seeking out. The objective of this study was to encourage conversation and understand the patient perspectives of existing knowledge gaps between epilepsy patients and the resources they use to obtain information. Methods: A total of thirteen patients (mean (SD) age = 46.4 (16.1) years) from the Calgary Comprehensive Epilepsy Program Registry and four caregivers participated in one of the three focus groups completed in order to meet our aims. There were eight female and five male patients. A semi-structured guide was used to understand the patients’ experiences, top concerns, informational resources currently used, and resources or knowledge that patients felt are lacking. The focus groups were audio-recorded and transcribed verbatim. Thematic content analysis was conducted by two researchers who independently open-coded the transcripts using NVivo 11. The final analysis was done by team discussion and ongoing analysis of the codes to create themes and sub-themes. Results: The major themes that emerged from the data included: 1) daily management; 2) resources; and 3) medications and treatment. For daily management, the participants reported concerns about the effects of epilepsy on day-to-day activities such as driving, working, and the barriers they face in society due to their perceived lack of awareness and education about seizure management in the general public. The participants felt negatively impacted by the stigma and compared their experience with epilepsy with other disorders such as cancer or diabetes which they feel are much more accepted in society. The geographical location of the patient also plays a role in the support they receive for epilepsy management, with participants citing challenges and feelings of isolation in rural areas. To acquire more information about epilepsy, participants reported that they primarily asked their physicians or searched online. However, despite the conveniences of the internet, some individuals felt the volume and variation of quality of online information was overwhelming. Instead, they would prefer to go to trusted resources that are provided by healthcare professionals or websites affiliated with hospitals or universities. Updated information on medication, side effects, and research are examples of resources the patients would like to see provided on such websites. Conclusion: Overall, it is clear from our focus groups that resources and support for self-management and day-to-day living for individuals with epilepsy is paramount to reduce knowledge gaps. Not only is it important to provide daily management and medication information to patients through trusted organizational resources, but it is equally important to increase public awareness about epilepsy and seizure disorders to reduce the stigma attached to these conditions.

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)0.012
metaresearch head score (Gemma)0.017
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 score0.012
Threshold uncertainty score0.066

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.017
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0060.002
Scholarly communication0.0020.003
Open science0.0010.005
Research integrity0.0020.003
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 head0.015
GPT teacher head0.259
Teacher spread0.244 · 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
Published2019
Admission routes1
Has abstractyes

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