“Sleep? Maybe Later…” A Cross-Campus Survey of University Students and Sleep Practices
Bibliographic record
Abstract
Sleep deficiency is a significant issue across higher education campuses and has a detrimental effect on students’ academic achievement, physical and mental health, and overall wellbeing. The purpose of this study was to carry out a campus-wide survey determining students’ self-reported sleep patterns, sources of advice for sleep problems, current sleep promoting practices, and preferred mechanisms to receive new information assisting with sleep problems. An anonymous electronic survey was distributed in February 2016 to all levels of students at the University of Alberta in the Western region of Canada. Descriptive data analysis was carried out with SPSS (v23). There were 1294 students (78.0% undergraduates; 87.5% living off-campus, 77.5% female) who participated in the survey. Sleeping less than 6.5 h a night was reported by 30.5% of participants; 66.5% stated they had insufficient sleep; 80.6% had not sought help. The three most frequent behaviours to aid sleep were reading a book, listening to music, and adjusting the heat. Although sleep problems were widely reported, students seldom sought help for this. The survey revealed that students already practice several strategies (listening to music, for example) that lend themselves to serving as a foundation for a strength-based cross-campus social marketing campaign of sleep promoting strategies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".