Patient-Centered Approaches for Designing Destigmatizing Sexual Pain-Related Web-Based Platforms: Qualitative Study
Bibliographic record
Abstract
BACKGROUND: Sexual pain is a common but neglected disorder that affects approximately 3% to 18% of women and an unmeasured number of gender-diverse people worldwide. Despite its wide prevalence, many people feel reluctant to visit conventional health care services or disclose their symptoms due to the fear of stigmatization. To alleviate this stigma, various web-based interventions have been developed to complement and, in some cases, replace conventional sexual health interventions. However, the way these web-based interventions are developed could inadvertently reproduce, perpetuate, or exacerbate stigma among end user patients. OBJECTIVE: The purpose of this study was to understand patients' perspectives on how sexual pain-related web platforms can be designed to alleviate stigma or prevent the unintended effects of stigma among patients who use web-based interventions. METHODS: Individual semistructured interviews were conducted among 16 participants with lived experiences of painful sex in a large urban city in Western Canada. Participants were recruited via social media platforms, newsletters, and a provincial health volunteer website. Using a sample sexual pain website to provide context, participants were interviewed about their experiences of stigma and how they think web platforms could be designed to address stigma. The interviews were conducted via Zoom (Zoom Technologies Inc) and analyzed using thematic analysis. RESULTS: The findings revealed 4 overarching themes that represented participants' perspectives on designing web platforms that may alleviate or prevent the unintended effects of stigma. These findings suggested the design of inclusive web platforms, having a nonprovocative and calming user interface, having features that facilitate connections among users and between users and providers, and displaying personal testimonials and experiences of sexual pain. CONCLUSIONS: This study highlighted patient-centered design approaches that could serve as a reference guide in developing web platforms that alleviate or prevent the unintended effects of stigma, particularly among nonheterosexual and gender-diverse people. While this study was conducted in the context of sexual pain, the results might also apply to web platforms on other potentially stigmatizing health-related disorders or conditions.
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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 | 0.024 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".