Thoughts and Experiences of Behçet Disease From Participants on a Reddit Subforum: Qualitative Online Community Analysis
Bibliographic record
Abstract
BACKGROUND: Behçet disease (BD) is a type of vasculitis with relapsing episodes and multisystemic clinical features, associated with significant morbidity and impact on patients' lives. People affected by BD often participate in discussions of their illness experiences. In-person support groups have limited physical accessibility and a relative lack of anonymity; however, online communities have become increasingly popular. OBJECTIVE: This study investigates the perspectives and experiences of people affected by BD by examining the content shared and discussed on a subforum of the website Reddit-a popular online space for anonymous discussions. METHODS: All discussion threads posted between March 9, 2021, and March 12, 2022, including posts and comments, were examined from the subforum "r/Behcets," an anonymous online community of 1100 members as of March 2022. A Grounded Theory analysis was completed to identify themes and subthemes, and notable quotes were extracted from the threads. Parameters extracted from each post included the number of comments, net upvotes, category, and subcategories. Two research team members read the posts separately to identify initial codes and themes to ensure data saturation was achieved. RESULTS: Six recurring themes were identified: (1) finding connectedness and perspectives through shared experiences, (2) struggles of the diagnostic odyssey, (3) sharing or inquiring about symptoms, (4) expressing strong emotions relating to the experience of BD, (5) the impact of BD on quality of life and personal relationships, as well as (6) COVID-19 and the COVID-19 vaccination in relation to BD. Subthemes within each theme were also identified and explored. CONCLUSIONS: This novel study provides a qualitative exploration of the perspectives and experiences of people affected by BD, shared in the anonymous and accessible online community of Reddit. The study found that people impacted by an illness seek to connect and receive validation through shared conditions and experiences. By examining the content shared in r/Behcets, this study highlights the needs of people affected by BD, identifying gaps and areas for improvement in the in-person support they receive.
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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 | 0.010 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.011 | 0.007 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".