Development of a Patient-Reported Outcome Measure for Youth Receiving Gender-Affirming Care: The GENDER-Q Youth Module
Bibliographic record
Abstract
Purpose: This study aimed to develop and establish the content validity of a patient-reported outcome measure for youth receiving gender-affirming care (GENDER-Q Youth). Methods: This mixed-methods study involved concept elicitation interviews with youth who were seeking/receiving gender-affirming care (February 2019-October 2023). Data were used to develop a conceptual framework and set of independent functioning scales. Scales were refined through clinical and research expert input and cognitive debriefing interviews with youth (December 2023-April 2024). A pilot test was conducted to examine scale psychometric performance, overall content validity, and acceptability (July 2024). Results: The concept elicitation interview sample included 47 youth aged 12-19 years. A conceptual framework with four main domains was created and included: health-related quality of life, gender practices, voice, and experience of care. To measure aspects of the framework, 17 scales (292 items) were developed and refined with input from 33 experts and 17 youth. The pilot test sample included 406 youth aged 18-25 years. Most respondents agreed that GENDER-Q Youth was easy to understand, thorough, asked important questions in a respectful way, felt safe to complete, and made them feel that their voice would be heard. The field test version of GENDER-Q Youth includes 16 scales (248 items). Conclusion: Evidence of content validity of GENDER-Q Youth was established based on extensive input from experts and youth.
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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 | 0.015 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".