Identifying trans and non-binary youth in population-based school health surveys in western Canada
Bibliographic record
Abstract
Abstract Background Most research about gender-diverse adolescents is with clinical samples, skewing population estimates of health and risk. With trans youth estimated at around 0.5% of the population and no reliable measures, school health surveys have not asked gender diversity items. In 2018, the British Columbia Adolescent Health Survey (BCAHS) in Canada trialled measures to differentiate cisgender, trans, and non-binary youth, to capture a representative picture of health for gender-diverse young people. Methods The 2018 BCAHS is a stratified random survey of 2,175 classrooms of grades 7-12 (ages 12-19) in 58/60 school districts province-wide (N = 38,015). Two measures asked about sex assigned at birth and current gender identity, combined to identify cisgender boys and girls, trans boys, trans girls, non-binary and questioning youth. We examined patterns of missingness by demographics. Results Overall, nearly 99% of youth responded to both items, with very low missing (0.5% for sex, 0.8% gender identity) with no differences by school or district, or by age. Missing responses were higher among international students and English language learners but still low (1.1%). Youth living in foster care or with a disability or chronic condition also had higher odds of skipping both items, but the highest missing was only 3.7% among youth with FASD. Overall, 48.9% identified as cis girls, 48.6% as cis boys, 0.13% as trans girls, 0.33% trans boys, 0.75% non-binary, and 1.28% as not sure (479 trans/non-binary, and 484 unsure). Preliminary analyses show positive assets also support gender diverse youth well-being. Conclusions A two-step measure of gender identity is feasible for adolescents as young as 12 years old in large scale school surveys, with very low missing responses in western Canada. Although <1% identified as trans, in large samples stable estimates for population comparisons are possible. Future studies should pilot gender identity measures in other nations/languages.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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".