Misgendering and the health and wellbeing of nonbinary people in Canada
Bibliographic record
Abstract
Background: Misgendering–using the wrong name, pronoun, or gendered language to refer to someone–is known to have negative impacts on the mental health and well-being of trans individuals generally. However, little is known about the effects of misgendering on nonbinary people specifically.Aims: As such, our research asked: 1) Among nonbinary people, what factors are associated with frequency of misgendering?; and 2) Do nonbinary people who experience misgendering less often have better health outcomes?Methods and Results: We analyzed data from Trans PULSE Canada, a community-based survey of trans and nonbinary people living in Canada, using a subset (n = 1091) who identified as nonbinary and completed questions on misgendering. Misgendering was a frequent and distressing experience for nonbinary participants, with 59% misgendered daily, 30% weekly or monthly, and only 11% yearly or less. Most (58%) reported being very or quite upset when misgendered. About one in eight (13%) corrected others most or all of the times they were misgendered. Daily misgendering was more common among nonbinary people who were younger than 25 years old (64%, p < .0001), visibly disabled (74%, p = .003), assigned female at birth (61%, p <.0001) or racialized as a person of color and assigned male at birth (65%, p < .0001) compared with their counterparts. In multivariable regression analyses, less frequent misgendering (weekly/monthly vs. daily) was associated with a lower OASIS anxiety score (β = −0.555, 95% CI = −1.062, −0.048).Discussion: Our research highlights the complexity of outness, passing, concealment, and affirmation for nonbinary people living at the intersections of marginalizations. Future research could build stronger causal analyses of the impacts of misgendering, how nonbinary people cope with misgendering, and policy and interventions to decrease misgendering.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".