Associations Between Transgender Identity, Sleep, Mental Health and Suicidality Among a North American Cohort of College Students
Bibliographic record
Abstract
PURPOSE: The purpose of this study is to examine the associations between transgender identity, sleep, and mental health among a North American cohort of cisgender and transgender college students. PARTICIPANTS AND METHODS: This cross-sectional study surveyed 221,549 North American college students from the 2016-2017 American College Health Association-National College Health Assessment II. Bivariate and multivariable analysis examined associations among transgender identity and outcomes of insomnia symptoms, daytime sleepiness, sleep disorder diagnoses and treatments. Mental health outcomes included mood symptoms, suicidal behaviors, anxiety and depression diagnoses and treatments. RESULTS: Transgender identity was reported by 1.6% (n=3471) of United States (US) and 1.7% (n=717) Canadian students, respectively. Mean age was 22.5 ±6. Transgender college students have an increased prevalence of daytime sleepiness, insomnia symptoms, diagnoses and/or treatment of insomnia and other sleep disorders as compared to cisgender college students. Mental Health symptoms are more prevalent with a 2-fold increase in depression and anxiety and nearly a 4-fold increase in suicide attempts among transgender students. A higher burden of mood symptoms exists among transgender college students in the US in comparison to Canadian students. CONCLUSION: Transgender college students have an alarmingly high rate of mood, sleep disturbances and sleep diagnoses, and suicidality. Colleges and universities must provide sufficient resources to address the sleep and mental health needs of transgender students. Institutions must adopt gender affirming policies that promote an inclusive environment. Increased allocation of resources and adoption of policies that enhance the physical and mental health of transgender students could improve sleep, mood, and potentially lower the suicide risk among a population that often experiences health inequities.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.002 | 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".