A Transgender Health Information Resource: Participatory Design Study
Bibliographic record
Abstract
BACKGROUND: Despite the abundance of health information on the internet for people who identify as transgender and gender diverse (TGD), much of the content used is found on social media channels, requiring individuals to vet the information for relevance and quality. OBJECTIVE: We developed a prototype transgender health information resource (TGHIR) delivered via a mobile app to provide credible health and wellness information for people who are TGD. METHODS: We partnered with the TGD community and used a participatory design approach that included focus groups and co-design sessions to identify users' needs and priorities. We used the Agile software development methodology to build the prototype. A medical librarian and physicians with expertise in transgender health curated a set of 97 information resources that constituted the foundational content of the prototype. To evaluate the prototype TGHIR app, we assessed the app with test users, using a single item from the System Usability Scale to assess feature usability, cognitive walk-throughs, and the user version of the Mobile Application Rating Scale to evaluate the app's objective and subjective quality. RESULTS: A total of 13 people who identified as TGD or TGD allies rated their satisfaction with 9 of 10 (90%) app features as good to excellent, and 1 (10%) of the features-the ability to filter to narrow TGHIR resources-was rated as okay. The overall quality score on the user version of the Mobile Application Rating Scale was 4.25 out of 5 after 4 weeks of use, indicating a good-quality mobile app. The information subscore received the highest rating, at 4.75 out of 5. CONCLUSIONS: Community partnership and participatory design were effective in the development of the TGHIR app, resulting in an information resource app with satisfactory features and overall high-quality ratings. Test users felt that the TGHIR app would be helpful for people who are TGD and their care partners.
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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 | 0.043 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".