Pelvic pain & endometriosis: the development of a patient-centred e-health resource for those affected by endometriosis-associated dyspareunia
Bibliographic record
Abstract
BACKGROUND: We recognized a paucity of accessible, evidence-based, empowering patient-centred resources for those with endometriosis-associated dyspareunia. Affecting more than 50% of people with endometriosis, dyspareunia can significantly impact relationships, chronic pain and the ability to have a family. We aimed to develop a patient-centred educational website for those affected by endometriosis-associated dyspareunia. METHODS: To develop a functional and meaningful website for endometriosis-associated dyspareunia, we utilized a Knowledge to Action framework, supplemented with a patient-centred research design and technology-enabled knowledge translation. Our patient partners influenced the direction and scope of the project, provided critical feedback throughout the development process, and approved website revisions prior to launch. The website was developed in five phases; (1) needs assessment interviews and focus groups with key stakeholders, (2) landscape analysis of pre-existing websites, (3) development, (4) usability testing and qualitative interviews, and (5) revisions and launch. RESULTS: Phase 1 and 2 emphasized a need for comprehensive yet plain language explanations of pain mechanisms and strategies for pain management. Rigorous consultation with key stakeholders informed the creation of the preliminary website in phase 3. Usability testing in phase 4 identified five main categories of usability problems, most of which were considered minor. Phase 4 qualitative interviews identified users' overall impressions of the preliminary website, including that the website could help people understand their pain and describe their pain to partners and healthcare providers, as well as feel empowered to seek healthcare and validated in their experiences. User suggestions, combined with usability testing, informed revisions in phase 5. CONCLUSION: We developed an educational website for endometriosis-associated painful sex where people can find evidence-based etiologies for pain, pain management options, and actionable resources. Based on the data collected through qualitative interviews with patients, this website can potentially empower people to seek health care. The strength of the website development approach used was the inclusion of qualitative user insights in addition to the commonly completed user tests. The patient interviews provided insights into the potential impact of the website and, thus, ensured that we not only created a functional website that meets end users' needs, but a website that is also meaningful to those affected by this condition.
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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 | 0.004 | 0.088 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".