Willingness to disclose sexual orientation and gender identity on federal government surveys: A community-based research study with gay, bisexual, transgender, and queer men and nonbinary and Two-Spirit people in Canada
Bibliographic record
Abstract
OBJECTIVES: Government-conducted population health surveys are important sources of data on health inequities for gay, bisexual, transgender, and queer men and nonbinary and Two-Spirit people (2S/GBTQ+). There is limited understanding of how vulnerable these surveys are to misclassification bias resulting from participants' reluctance to disclose their sexual orientation and gender identity. 2S/GBTQ+ people may be more willing to participate in community-based surveys, where they might feel safer disclosing their minority sexual orientation or gender identity than they would on a government survey. We sought to understand whether the proportion of 2S/GBTQ+ people who would disclose their sexual orientation on a government survey changed between 2012 and 2019 survey cycles, as well as the proportion of trans, nonbinary, and Two-Spirit participants who would reveal their gender identity, and the demographic factors associated with both. METHODS: We analysed data from the 2012 and 2019 cycles of Sex Now, a repeated cross-sectional Canada-wide online survey on the health and well-being of 2S/GBTQ+ people conducted by the Community-Based Research Centre. We computed frequencies and prevalence ratios of the likelihood of disclosing sexual orientation and gender identity on a Statistics Canada survey by a variety of demographic variables. RESULTS: We found that in 2019, 86.0% (95% CI [85.4, 86.7]) of all participants would reveal their sexual orientation, a significant increase from 2012 (69.5%, 95% CI [68.5, 70.4], Δ = 16.6%, 95% CI [15.4, 17.8]). However, participants who identified as bisexual, straight, or heteroflexible; who were in a relationship with a woman; or who were not "out" were less willing to reveal their sexual orientation. We found that 85% of trans men, nonbinary, and Two-Spirit participants would reveal their gender identity, which was more likely among those living with HIV or aged 19-29 years old. CONCLUSION: These findings suggest that government datasets may significantly misclassify and underestimate the population size of 2S/GBTQ+ individuals. Persistent mistrust of government institutions within this community may exacerbate underreporting and non-disclosure, underscoring the need for research into methodologies that can enhance trust and improve the accuracy of population estimates. Researchers using existing government datasets should consider using statistical methods to account for potential misclassification error.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 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".