D151. The Gender-Q: A Rigorous, Modular, Patient-reported Outcome Measure For Gender-affirming Care
Bibliographic record
Abstract
PURPOSE: To develop a comprehensive, rigorous PROM to assess outcomes of gender-affirming care in clinical practice, research, and quality initiatives (the GENDER-Q) METHODS: A mixed-methods, multiphase approach was used. In phase 1, in-depth interviews were conducted with adults seeking gender-affirming care from 4 countries (US, Canada, Denmark, Netherlands). Data were analyzed and used to develop a conceptual framework and item pool, which was used to develop preliminary scales. Iterative feedback was sought from clinicians and patients on the scales, and used to refine/develop new scales. The scales were pilot-tested using a crowd-sourcing platform. RESULTS: Data from interviews with 84 transgender and gender diverse adults (34±14years) resulted in 42 preliminary scales that were developed covering the concepts in the conceptual framework. Feedback from 50 clinicians and patient participants (n=7 to 14, depending on scale) was used to refine and develop 15 new scales, resulting in 55 scales in the field test version. Pilot-test was completed (n=602) and used to make minor changes to the GENDER-Q (e.g., drop items). CONCLUSION: The GENDER-Q was developed using extensive input from TGD individuals and clinician experts and represents the most comprehensive set of independently functioning, unidimensional scales that are available to-date. Phase 2 involves an international field-test (currently underway; n=1900), following which the scales will be made available at no charge for academic and clinical research.
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 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 | 0.013 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".