Parental Use of Social Media and the Internet in the Context of Their Child’s Genetic Neurodevelopmental Disorder: Mixed Methods Study Nested in the GenROC Cohort Study
Bibliographic record
Abstract
Background: Social media groups (SMGs) enable individuals with rare diseases to connect with one another and access instant support and advice. Accelerated diagnoses of genetic neurodevelopmental disorders (NDDs) over the last decade have driven rapid expansion of gene-specific SMG membership. Limited literature regarding parental use of SMGs in the context of managing their child's NDD exists. Objective: The objective of our study was to determine and describe how parents use social media and the internet in the context of their child's NDD. Methods: We undertook a mixed methods study of 477 parents within a cohort of children with NDDs (Genetic Rare Disease: Observational Cohort Study; GenROC). A total of 359 parents provided quantifiable survey responses regarding their use of social media. We also interviewed 17 parents to understand how they use SMGs and their views on the data held within these groups. Results: Our survey found 359/475 (75.7%) of parents use SMGs related to their child's genetic disorder, and of these, 98.5% (354/359) are on Facebook. Most SMGs are closed, international, have more than 200 members, are specific to the NDD, and are associated with a corresponding charity or foundation. In total, 191/312 (61.2%) parents could not recall what they had consented to when joining the group with respect to the use of their posted data. Most parents trust the data (Likert scale 1-10; mean 7.03, SD 1.98) that are shared but acknowledged the anecdotal nature of them. Parents found the most valuable element of the SMG to be shared lived experience with other families. Interview data from 17 parents were coded and analyzed thematically. A total of 4 main themes were identified: (1) SMGs for support and shared lived experience; (2) possible harms from participation in SMGs; (3) SMG composition, demographics, and dynamics; and (4) usefulness and use of data shared within the groups. Conclusions: This mixed methods study shows the evolving landscape of SMG use in neurodevelopmental disorders, highlights its benefits and downsides, and is widely applicable to all parent SMGs for specific niche medical conditions. Using the strength of these groups in a more collaborative approach in the future could prove useful to clinicians, families, and researchers alike.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".