Danish translation and linguistic validation of the GENDER-Q, a patient-reported outcome measure for use in gender-affirming care
Bibliographic record
Abstract
Abstract Background Globally, the number of individuals identifying as transgender and gender-diverse [TGD] has grown in the last decade. This study aimed to perform a Danish linguistic translation and cultural adaptation of the GENDER-Q, a new patient-reported outcome measure [PROM] designed to evaluate outcomes of gender-affirming care. Methods The field test version of the GENDER-Q (959 items) was translated into Danish using guidelines from the International Society of Pharmacoeconomics and Outcomes Research and the World Health Organization to ensure accuracy, cultural relevance, and validity. This included two forward translations, a backward translation, an expert panel meeting, and two rounds of patient cognitive interviews. Results The forward translations resulted in revisions of 142 items, which were then harmonized to form the backward translation version. A comparison of the back translation to the original questionnaire led to a total of 43 changes to items and response options. The revised version was reviewed in an expert panel meeting and minor changes were made with 28 patient participants using cognitive debriefing interviews. The translated version was proofread, resulting in the Danish translation of the GENDER-Q. Conclusions The GENDER-Q was translated and culturally adapted for use in the Danish TGD adult population. The GENDER-Q has the potential to enhance the understanding and improvement of treatment and health-related quality-of-life outcomes for adults seeking gender-affirming care. Level of Evidence: Not gradable.
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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 | 0.040 | 0.082 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".