Sleep health among youth outside of the gender binary: Findings from a national Canadian sample
Bibliographic record
Abstract
OBJECTIVES: Sleep is important for adolescent health. The unique needs of suprabinary youth (youth with gender identities outside of the gender binary), along with the growing number of youth with these identities, underscores the need to better understand sleep health within this population. The current study's objectives were to (1) examine differences in sleep health between suprabinary and binary youth and (2) explore how social support, peer victimization, and technology use accounted for these differences. METHODS: Data were drawn from the 2017/2018 Health Behavior in School Aged Children Survey. Adolescents (individuals ages 14 to 17, n = 10,186), indicated whether they were suprabinary (n = 182) or binary (n = 10,004), and completed measures of sleep health (difficulty falling asleep, difficulty staying awake, weekday and weekend sleep length), covariates (age, family affluence, race/ethnicity, depressive symptoms), as well as variables that may account for differences between suprabinary and binary youth (family, friend, and teacher support, as well as peer victimization, and technology use before bed). RESULTS: Suprabinary youth reported worse sleep health on all outcomes, and differences persisted for both difficulty falling asleep and weekday sleep hours accounting for covariates. Significant indirect effects between suprabinary status were observed across all sleep outcomes for family support and school climate. Indirect effects for sleep quality were also observed via peer victimization. CONCLUSIONS: Findings support the relevance of looking at basic health processes like sleep to better understand how the stressors associated with suprabinary status impact health outcomes among this vulnerable population.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.008 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".