Addressing online-facilitated stigma: a co-design workshop among patients with lived experiences of dyspareunia
Bibliographic record
Abstract
OBJECTIVES: To engage individuals with lived experiences of dyspareunia in a co-design process to identify strategies for reducing stigma on digital health platforms. METHOD: Three virtual co-design workshops were conducted with 14 participants with lived experiences of dyspareunia. Data collection occurred in two phases. In phase 1, participants created individual prototypes of stigma-alleviating website designs. In phase 2, participants came together to collaboratively create a final design prototype using the individual designs as a guide. Participants then explained their reasons for selecting specific design elements and how these choices addressed stigma. The co-design workshops were recorded, transcribed verbatim and then analysed thematically. FINDINGS: The data revealed four overarching themes for developing destigmatising online platforms. These include providing extensive information on dyspareunia, designing for inclusivity, protecting users' identities, and offering interactive features to support information access and community connection. CONCLUSION: This study offers patient-led strategies for mitigating stigma through online platforms. The findings may inform the design of digital health resources for individuals seeking sexual health services online, particularly those from stigmatised populations who use web-based platforms to navigate or supplement their healthcare needs.
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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 | 0.027 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".