The lived experience of social anxiety disorder: A conceptual model based on published literature and social media listening
Bibliographic record
Abstract
Abstract Social anxiety disorder (SAD) affects up to 1 in 8 individuals over their lifetime and is characterized by an intense fear of social situations where there may be exposure to unfamiliar people or possible scrutiny. The analysis of social media data rather than traditional methods (interviews or focus groups) can provide a unique opportunity to understand the lived experience of individuals with SAD, for whom interacting with strangers is challenging. This retrospective observational study reviewed published literature from PubMed and data from Reddit using social media listening (SML). A stepwise analysis in line with US Food and Drug Administration Patient-Focused Drug Development guidelines was performed to develop a conceptual model for SAD. Natural language processing techniques and machine learning approaches were employed to extract symptoms and impacts described by individuals with SAD. Eleven publications from the literature and 535,544 posts from 118,040 Reddit users were included. Clinical and patient experts then refined the conceptual model covering three key symptom domains (physical, negative automatic thoughts, and emotions) and two impact domains (social functioning and occupational/educational functioning). This study provides insights into the lived experience of individuals with SAD and confirms the value of SML when traditional methods are inappropriate.
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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 | 0.011 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.013 | 0.006 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.005 |
| 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".