Validation of the Chinese Version of the Gender Identity/Gender Dysphoria Questionnaire for Adolescents and Adults
Bibliographic record
Abstract
BACKGROUND: The number of individuals with potential gender dysphoria (GD) being referred to specialized gender identity clinics or programs is increasing internationally; these cases are initially screened using the Gender Identity/Gender Dysphoria Questionnaire for Adolescents and Adults (GIDYQ-AA). AIM: The current study aimed to assess the psychometric properties of the GIDYQ-AA in a sample of adolescents and young adults from China. METHODS: A cross-sectional study was conducted in October 2020. Sociodemographic information of the participants was first collected. Participants then completed the GIDYQ-AA, the Generalized Anxiety Disorder-7 scale, the Patient Health Questionnaire-9, and a suicidal ideation assessment. A total of 2,533 participants with a mean age of 19.30 (SD = 1.19) years were recruited. Of the participants, 841 (33.2%) were cis men, 1,589 (62.7%) were cis women, 66 (2.6%) self-identified as transgender, 17 (0.7%) self-identified as non-binary, and 20 (0.8%) self-identified as gender queer. RESULTS: The GIDYQ-AA had high internal consistency with a Cronbach's alpha = 0.89. Exploratory factor analysis showed that the GIDYQ-AA had a four-factor structure in China. The GIDYQ-AA was significantly correlated with anxiety symptoms (r = -0.32, P < .01), depressive symptoms (r = -0.33, P < .01), and suicidal ideation (r = -0.20, P < .01). CLINICAL TRANSLATION: The Chinese version of GIDYQ-AA is a useful measurement with high practical value, which could promote the assessment and research of GD across China or among Chinese migrants in other countries. STRENGTHS AND LIMITATIONS: This is the first study assessing the psychometric properties of the GIDYQ-AA in Chinese adolescents and young adults. The convergent and divergent validity of the GIDYQ-AA were not examined due to the unavailability of data. Also, the sample did not have an equal distribution of male to males and female to females. CONCLUSION: The Chinese version of GIDYQ-AA is a useful measure, which could promote both the assessment and research of GD in the Chinese population. Wang Y, Feng Y, Su D, et al. Validation of the Chinese Version of the Gender Identity/Gender Dysphoria Questionnaire for Adolescents and Adults. J Sex Med 2021;18:1632-1640.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".