Analysis of a Parent-Initiated Social Media Campaign for Hirschsprung’s Disease
Bibliographic record
Abstract
BACKGROUND: Social media can be particularly useful for patients or families affected by rare conditions by allowing individuals to form online communities across the world. OBJECTIVE: Our aim in this study was to conduct a descriptive and quantitative analysis of the use of a social media community for Hirschsprung's Disease (HD). METHODS: In July 2011, a mother of a child with HD launched the "Shit Happens" campaign. The campaign uses social media (blogs, Twitter, and Facebook) to engage other families affected by HD. Internet analytics including Google Analytics and Facebook Insights were used to evaluate the reach and responsiveness of this campaign. RESULTS: On the day the HD campaign was launched, 387 people viewed the blog "Roo's Journey". Blog views have now exceeded 5400 views from 37 countries. The Facebook page extends to 46 countries, has an average post reach of 298 users, 1414 "likes", and an overall reach of 131,032 users. The campaign has 135 Twitter followers and 344 tweets at the time of writing. The most common question posted on the Facebook page is related to treatment for extreme diaper rash. Responsiveness assessment demonstrated that within 2 hours of posting, a question could receive 143 views and 20 responses, increasing to 30 responses after 5 hours. CONCLUSIONS: Social media networks are well suited to discussion, support, and advocacy for health-related conditions and can be especially important in connecting families affected by rare conditions. The HD campaign demonstrates the reach and responsiveness of a community that primarily relies on social media to connect families affected by HD. Although responsive, this community is currently lacking consistent access to evidence-based guidance for their common concerns. We will explore innovative consumer-researcher partnerships to offer a solution in future research.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".