Views of People With Epilepsy About Web-Based Self-Presentation: A Qualitative Study
Bibliographic record
Abstract
BACKGROUND: Web-based media, particularly social networking sites (SNSs), are a source of support for people with long-term conditions, like epilepsy. Living with epilepsy can reduce opportunities for accessing information and social support owing to transportation difficulties and stigma leading to self-isolation. However, some people with epilepsy (PWE) overcome these barriers using SNSs and other Web-based media. At present, little is known about Web-based identity and self-presentation of PWE; this study aims to address this gap. OBJECTIVE: This study aims to describe how the use of digital technologies, such as SNSs, impacts sense of identity in PWE. METHODS: We used qualitative research methods to examine Web-based media use and self-presentation in a group of 14 PWE (age range: 33-73 years; 7 men and 7 women). The median diagnosis duration was 25 years. Semistructured interviews ranged from 40 to 120 minutes, held at participants' homes or in a public place of their choice, in the United Kingdom. QSR Nvivo 11 software was used to perform an inductive thematic analysis. RESULTS: In this study, 9 participants used Web-based media to "silently" learn from other PWE by reading user posts on SNSs and epilepsy-related forums. When asked about self-presentation, 7 participants described feeling cautious about disclosing their epilepsy to others online. Six participants presented themselves in the same manner irrespective of the situation and described their identity as being presented in the same way both online and offline. CONCLUSIONS: PWE can deploy SNSs and Web-based media to manage aspects of their condition by learning from others and obtaining social support that may otherwise be difficult to access. Some PWE share openly, whereas others silently observe, without posting. Both benefit from the shared experiences of others. Privacy concerns and stigma can act as a barrier to sharing using Web-based media and SNSs. For some, Web-based media offers a chance to experiment with identity and change self-presentation, leading to gradually "coming out" and feeling more comfortable discussing epilepsy with others.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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 teacher head, 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".