Mediating Pain: Navigating Endometriosis on Social Media
Bibliographic record
Abstract
With the rise of social media, many people with endometriosis have turned to platforms such as Facebook and Instagram in the face of lacking care. This qualitative study focuses on why and how people with endometriosis use these platforms. Despite the risks of misinformation and conflict on social media, the results of this research show that many people with endometriosis find these spaces beneficial, particularly for information sharing, social support, representation, and advocacy practices around endometriosis. Using data collected from surveys and interviews, this study reveals that people with endometriosis often use social media to understand, experiment with, and navigate their symptoms and that these efforts deserve recognition by endometriosis researchers and practitioners. This article proposes that, in order to improve future patient-practitioner and patient-researcher relationships for endometriosis, we must understand, not dismiss, the social media practices of those with endometriosis. By understanding how and why patients turn to social media, clinicians and researchers can build toward more patient-oriented futures.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".