A French Translation of the Transgender Congruence Scale: Validation and Associations with Distress, Well-Being, and Perceived Transition Status
Bibliographic record
Abstract
Purpose: Disparities in psychosocial functioning between transgender and cisgender populations highlight the importance of validating measures assessing mechanisms of resilience for transgender and nonbinary people. Gender congruence is an important mechanism of resilience, as it focuses on the individual's own gender objectives. Moreover, research increasingly links gender congruence to psychosocial functioning and well-being. The goals of the current study were to validate a French-language version of the Transgender Congruence Scale and examine how this scale was associated with life satisfaction, psychological distress, and perceived transition status. Methods: Individuals ( N =179) with a variety of transgender and nonbinary gender identities living in Quebec, Canada, were recruited online. They completed the Transgender Congruence Scale, as well as measures of transition status, psychological distress, and life satisfaction. Results: A two-factor model (assessing appearance congruence and identity congruence, respectively) was supported in the current sample. The French translation of the Transgender Congruence Scale showed good reliability and validity, similar to those found with the original English language version of the measure. Higher scores on both subscales, as well as the total scale, were associated with better psychosocial functioning and self-defined transition status. Discussion: Findings linked both appearance and identity congruence to psychosocial outcomes and supported the validity of this French version of the Transgender Congruence Scale. With potential clinical or research applications, the Transgender Congruence Scale is a brief and psychometrically sound measure of an important resilience construct for gender minority individuals that can now be used with Francophone populations.
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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 | 0.007 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".