Developing an Educational Website for Women With Endometriosis-Associated Dyspareunia: Usability and Stigma Analysis
Bibliographic record
Abstract
BACKGROUND: Endometriosis is a chronic condition that affects approximately 10% of women worldwide. Despite its wide prevalence, knowledge of endometriosis symptoms, such as pelvic pain, and treatments remains relatively low. This not only leads to a trivialization of symptoms and delayed diagnosis but also fuels myths and misconceptions about pain symptoms. At the same time, the use of web-based platforms for information seeking is particularly common among people with conditions that are perceived as stigmatizing and difficult to discuss. The Sex, Pain, and Endometriosis website is an educational resource designed to provide evidence-based information on endometriosis and sexual pain to help people understand the condition, feel empowered, dispel myths, and destigmatize endometriosis-associated sexual pain. OBJECTIVE: The study objective is to evaluate the usability of the website and assess for destigmatizing properties of sexual health-related web-based resources. METHODS: We conducted a usability analysis by using a think-aloud observation, a postsystem usability questionnaire, and follow-up interviews with 12 women with endometriosis. The think-aloud data were analyzed using the framework by Kushniruk and Patel for analyzing usability video data, the questionnaire data were analyzed using descriptive statistics, and the follow-up interviews were analyzed using simple content analysis. We conducted a usability assessment by deductively analyzing the interview data via a trauma-informed care framework and a content analysis approach. RESULTS: Through usability analysis, we found the website to be simple, uncluttered, satisfying, and easy to use. However, 30 minor usability problems related to navigation; website response; the comprehension of graphics, icons, and tabs; the understanding of content; and mismatch between the website and users' expectations were reported. In our stigma analysis, we found the web content to be nonstigmatizing. The participants suggested ways in which websites could be designed to address stigma, including ensuring privacy, anonymity, inclusiveness, and factual and nonjudgmental content, as well as providing opportunities for web-based engagement. CONCLUSIONS: Overall, the participants found the website to be useful, easy to use, and satisfying. The usability problems identified were largely minor and informed the website redesign process. In the context of the limited literature on stigma and website design, this paper offers useful strategies on how sexual health-related websites can be designed to be acceptable and less stigmatizing to individuals with sensitive health issues.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".