Insights on mental health when living with rheumatoid arthritis: A descriptive qualitative study of threads on the Reddit website
Bibliographic record
Abstract
Abstract Background: Reddit is a highly visited social news and discussion website where individuals anonymously ask questions, post opinions and share experiences, which provide a valuable pool of publicly available data. Our objective was to systematically search and analyze threads on the social news website, Reddit, to understand experiences of individuals with rheumatoid arthritis (RA) regarding their mental health. Methods: We conducted a patient-oriented descriptive qualitative study. We identified threads from two subreddits, “r/Thritis” and “r/Rheumatoid”, using keywords such as “mood”, “mental health”, “stressed”, “depressed”, “anxious” over a 1-year period between June 2018 and June 2019. For included threads, we extracted the title, original post, and corresponding comments and responses. We applied thematic analysis using an inductive approach. Results: Of 81 threads identified, we included 27. We identified four themes: 1) Navigating the management of RA explores how the physical impacts of the disease, lack of health resources/support and the complexity of medications affect mental health; 2) Experiencing impact on relationships and social isolation includes experiencing misconceptions of RA, feeling misunderstood and feeling guilt; 3) Experiencing loss , touches on the helplessness brought by challenges with performing self-defining activities such as self-care, work, and childbearing/parenting; and finally, 4) Experiencing emotional struggles captures how tension between fighting through and despair has led some to suicide ideation and thoughts of death. Conclusions: Online forums and communities such as Reddit have created opportunities for individuals with RA to share experiences on mental health matters, which they may not necessarily be able to share with others.
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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 | 0.013 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.009 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".