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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.036 | 0.110 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads agree on what is shown here.
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".