Exploring Relationships of Sleep Duration with Eating and Physical Activity Behaviors among Canadian University Students
Bibliographic record
Abstract
Background: Students pursuing postsecondary education are a population described as vulnerable for sleep problems, poor dietary habits, weight gain, and reduced physical activity. The primary goal of this study was to examine relationships of sleep behaviors with eating and physical activity behaviors in a sample of undergraduate health sciences students. Methods: Using a cross-sectional design, undergraduate health sciences students in a small Canadian university were recruited to complete an on-line questionnaire about their sleep, eating, and physical activity behaviors using valid and reliable instruments. Key sociodemographic characteristics and self-reported height and weight data were also captured. Results: The participants (n = 245) were on average 23 years of age, female (86%), and the majority were full-time students (92%). The mean BMI was within a healthy range (mean 24.58 SD 5.55) with the majority reporting low physical activity levels (65%). Despite self-reports of very or fairly good (65%) sleep quality in the past month, the mean global sleep scores (scores > 5, mean 7.4, SD 3.3) indicated poor overall sleep quality. Poorer sleep quality was associated with higher BMIs (r = 0.265, p < 0.001). Conclusions: The findings highlight the need to expand the scope of on-campus wellness programs to promote healthy sleep habits in a vulnerable university 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".