Improving Social Media-Based Support Groups for the Rare Disease Community: Interview Study With Patients and Parents of Children with Rare and Undiagnosed Diseases
Bibliographic record
Abstract
BACKGROUND: The rarity that is inherent in rare disease (RD) often means that patients and parents of children with RDs feel uniquely isolated and therefore are unprepared or unsupported in their care. To overcome this isolation, many within the RD community turn to the internet, and social media groups in particular, to gather useful information about their RDs. While previous research has shown that social media support groups are helpful for those affected by RDs, it is unclear what these groups are particularly useful or helpful for patients and parents of children with RDs. OBJECTIVE: This study aimed to identify what specific features of disease-related support groups (DRSGs) the RD community finds particularly useful or supportive and provide a set of recommendations to improve social media-based RD support groups based on this information. METHODS: Semistructured qualitative interviews were performed with patients and parents of patients with RDs. Interview participants had to be at least 18 years of age at the time of the interview, be seen by a genetics specialist at a partner health care institution and be proficient in the English language. Social media use was not a prerequisite for participation, so interview participants ranged from extensive users of social media to those who chose to remain off all social media. All interviews were conducted by phone, recorded, and then transcribed. Interview transcripts were then coded using the 6 steps outlined by Braun and Clarke. Three researchers (TAD, SLV, and CMEH) performed initial coding. After this, the study team conducted a review of themes and all members of the team agreed upon a final analysis and presentation of data. RESULTS: We conducted 31 interviews (mean age 40, SD 10.04 years; n=27, 87% were women; n=30, 97% were non-Hispanic White). Thematic analysis revealed that social media DRSG users identified the informational usefulness of these groups as being related to the gathering and sharing of specific information about an RD, clarification about the importance and meaning of certain symptoms, and obtaining insight into an RD's progression and prognosis. Participants also identified that DRSGs were useful sources of practical information, such as tips and tricks about managing RD-related issues and concerns. In addition, participants found DRSGs to be a useful space for sharing their disease-related stories but also highlighted a feeling of exhaustion from overexposure and overuse of DRSGs. CONCLUSIONS: This study identifies the usefulness of DRSGs for the RD community and provides a set of recommendations to improve future instances of DRSGs. These recommendations can be used to create DRSGs that are less prone to splintering into other DRSGs, thus minimizing the risk of having important RD-related information unhelpfully dispersed amongst a multitude of support groups.
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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 | 0.009 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| 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 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".