Assessment of a “Transgender Identity Stigma” scale among trans women in India: Findings from exploratory and confirmatory factor analyses
Bibliographic record
Abstract
Background: Sexual minority stigma has been shown to influence mental health and sexual risk, but limited research is available on measuring transgender-identity stigma among trans women in India. We adapted an Exposure to Transphobia scale to the Indian context and tested a 14-item Transgender Identity Stigma Questionnaire (TGISQ) among trans women in India. We aimed to assess and validate the factor structure of the TGISQ and to assess its reliability.Methods: Data were from a cross-sectional survey among 300 trans women (including hijras/thirunangais) from six urban/semi-urban sites in India. The TGISQ consisted of self-reported ratings on 14 items. We initially conducted exploratory factor analysis, using principal axis factoring (PAF) and promax rotation, and assessed reliability (internal consistency) using Cronbach's alpha; we then conducted confirmatory factor analysis to assess construct validity (factorial validity). Construct validity of the final 13-item Transgender Identity Stigma Scale (TGISS) was also examined by computing Pearson's correlations between TGISS and relevant theoretical constructs (e.g., depression, social support).Results: PAF identified two factors: enacted stigma (5 items) and felt normative stigma (8 items). The final 13-item TGISS had high reliability and acceptable construct validity.Conclusions: The TGISS was found to have adequate psychometric properties, making it the first valid and reliable scale to measure stigma and discrimination faced by trans women in India. Future studies can further refine TGISS, which might help in comparing the differences in stigma experiences among diverse subgroups of trans women, and in monitoring and evaluating the success of stigma reduction programs.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".