Patients With Rare Diseases and the Power of Online Support Groups: Implications for the Medical Community
Bibliographic record
Abstract
BACKGROUND: Previous studies have shown positive tangible health benefits from using online support communities for informational support, daily living support, and emotional support. The specifics of how these communities can be improved have not been studied in detail. OBJECTIVE: This study will investigate various sources of information that patients with genetic disorders use to learn more about their condition. We will be using patients with Ehlers-Danlos Syndrome (EDS) as a typical representation of the wider group of patients with genetic disorders. This study will also investigate the benefits and disadvantages of web-based platforms and how they can be improved. METHODS: We used quantitative and qualitative analyses in this study. We undertook a web-based questionnaire survey and semistructured qualitative interviews through Zoom. Questionnaire results were analyzed using descriptive analysis. Thematic coding with constant comparison was used for interview transcript analysis. RESULTS: A total of 436 respondents completed some or all of the survey. The majority of participants are female (386/413, 93.46%), and 24% (99/413) of them are in the age range of 25-34 years. Around 81% (336/413) of the participants have some type of college degree, and 55% (227/413) of them have graduate degrees. About 49.31% (204/413) of them are not currently employed. Most patients stated that their health care providers did not give accurate and complete information to them regarding their health situation (mean 2.87, SD 1.34). Also, patients perceived their providers as not knowledgeable regarding web-based communities that discuss patients' conditions (mean 1.93, SD 1.15). Patients are confident in using health care resources available in web-based health communities (mean 3.78, SD 1.13). We interviewed 30 participants. The demographics of the interviewees were aligned with those of the survey participants. A total of 9 different themes were identified based on the Qualtrics survey and qualitative interviews. Participants shared the pros and cons of different online support groups that they were using and gave suggestions for improvement. They requested a centralized database with different categories of resources classified based on different diseases. They also emphasized the importance of search features and the ability to find relevant information with a hashtag. Furthermore, they elaborated on the privacy and security concerns they have regarding web-based support group platforms. CONCLUSIONS: Patients with rare diseases are finding information not available from their health care providers in community support groups. The medical community and web developers have a great opportunity to help these people by engaging with their web-based communities.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".