Sex, Pain & Endometriosis : Co-Designing An Online Resource Through integrated Knowledge Translation
Bibliographic record
Abstract
Background: Endometriosis is a gynaecological disease that affects 1 in 10 women in Canada and is characterized by the presence of endometrial cells outside the uterus leading to painful periods, chronic pelvic pain, and sexual pain. Painful sex occurs in 50% of women with endometriosis, negatively influencing their sexual functioning and interpersonal relationships. Despite the prevalence and impact of painful sex, there is limited accessible, evidence-based information to help people understand their symptoms, seek appropriate health care, and make treatment decisions. Guided by patient-oriented research, the aim of this project was to develop an online platform for people with endometriosis-related sexual pain. Methods: Firstly, we conducted stakeholder focus groups with patients, clinicians, researchers and endometriosis organizations to determine the appropriate audience, content, mood and feel of the online platform. Secondly, we evaluated existing online resources for readability, suitability, and quality using validated eHealth tools. Finally, we developed content for the online platform informed by user-centred iterative design. Results: Hope, de-stigmatization, empowerment, connectedness, depth of information and credibility were identified by our stakeholder groups as important for an online platform. Our review suggest that existing online resources use medical/technical language, offer limited content, include long blocks of text and are not visually appealing. As a result, all content for our site including types of sexual pain, physical and psychological mechanisms, management and treatment options were conveyed through short plain language messages, visual images and design. Implications: The end product was an appealing, informative public website for a diverse audience. Further development and qualitative interviews will explore the user experience to determine additional information, support needs and self-management strategies leading to better health outcomes for people with endometriosis and painful sex.KT Approach: The cornerstone of this project was Integrated knowledge translation (iKT), which allowed for stakeholder/researcher co-design to ensure the product was fit for knowledge users. Acknowledgements: We would like to thank the Canadian Institute for Health Research for funding the project, Dr. Catherine Allaire, Dr. Christina Williams, Kate Wahl, Natasha Orr, Leah Tannock for reviewing content and Web Design company Tactica Interactive.
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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 | 0.017 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".