Developing a trauma-informed social media campaign to disseminate endometriosis specific qualitative arts-based research findings: A tutorial (Preprint)
Bibliographic record
Abstract
UNSTRUCTURED Trauma-informed approaches promote the creation of systems that prioritize safety and empowerment to improve patient well-being. These approaches are especially important in sexual and reproductive healthcare, where patients are often asked to disclose sensitive and personal information. This disclosure is particularly relevant in the context of endometriosis, a condition that affects 10% of reproductive-aged women and causes debilitating pelvic pain. Our team led a trauma-informed social media campaign to raise awareness and improve understanding of endometriosis by sharing research findings from a photovoice study focusing on Asian women’s experiences of endometriosis during the COVID-19 pandemic in Canada (EndoPhoto Study). In this manuscript, we describe how we adapted and applied trauma-informed approaches to the development and implementation of the social media campaign by following five principles: support and collaboration; trustworthiness and transparency; safety; empowerment and voice; and cultural and gender sensitivity. We co-designed this campaign with patient partners with lived experience of endometriosis to facilitate collaboration and mutuality. Additionally, we shared details about the funders of this study to increase trust and transparency, moderated comments and de-identified images to promote participant safety, chose safer platforms to enhance empowerment and voice, and avoided stereotypes and shared authentic experiences of Asian people with endometriosis to support cultural and gender sensitivity. The campaign launched on Instagram and Pinterest in March 2025 to coincide with Endometriosis Awareness Month. The social media campaign received 8,540,528 total impressions over the course of the month and had a 6.23% and 1.4% engagement rates on Instagram and Pinterest, respectively.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.037 | 0.010 |
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".