Gender and Sleep Health in High School Students: A Cross-Cultural Study
Bibliographic record
Abstract
BACKGROUND & OBJECTIVE: Many recent studies have shown that sleep health is important for overall health and performance. However, adolescents often report poor sleep health, such as short duration and low quality sleep. In some cases, sleep characteristics are independent of gender and culture, but not in others. In this study, we tested for effects of gender, culture, and their interaction on measures of sleep health (adequacy and quality) for adolescents in an American population and a Chinese population. METHODS: A common survey instrument was administered to high school students in New Jersey, USA and Wen Zhou, PRC. Students were asked to answer questions about their sleep duration, perception of sleep adequacy, daytime sleepiness, and napping for typical school days and weekends. Our final sample included 2,986 female students (2,059 American and 837 Chinese) and 2,544 male students (1,764 American and 780 Chinese). RESULTS: Differences in sleep duration were minor or absent, but differences in sleep health were substantial. Females were more likely than males to report inadequate sleep and daytime sleepiness, for both school days and weekends, and higher hypersomnolence scores. Chinese students were more likely than American students to report inadequate sleep and daytime sleepiness, for both school days and weekends, and higher hypersomnolence scores, with the exception that perception of adequate sleep did not differ between Chinese and American males on weekends. Especially dramatic was the difference in hypersomnolence, in which 74% of Chinese students reported inadequate sleep, sleepiness, and a nap for a typical school day, compared to only 29% of American students. CONCLUSIONS: The results suggest the presence of gender and cultural differences in sleep quality that yield divergent outcomes for similar sleep durations.
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".