0801 IDENTIFYING CURRENT PRACTICES AND OPPORTUNITIES FOR STRENGTH-BASED INTERVENTIONS TO IMPROVE UNIVERSITY STUDENTS’ SLEEP HABITS ANDOUTCOMES: A CAMPUS-WIDE SURVEY
Bibliographic record
Abstract
For many students sleep deficiency is a routinely accepted part of university. This is concerning; we know that sleep deficiency exerts a significant toll on students’ physical and emotional health, and decreases their capacity for critical academic skills including learning, memory and problem solving. Research clearly demonstrates the relationship between sleep deficiency in college and university students and risk-taking behaviours such as drink driving, mental health conditions including suicidality, depression and anxiety, decreased self-efficacy, substance misuse, binge drinking and excessive caffeine consumption, smoking and high rates of social media use. Many university students bring poor sleep habits and pre-existing sleep disorders into their pursuit of higher education. Compounding these pre-existing factors are new social and financial pressures, increased anxiety, and sleep-disruptive living environments. Students sacrifice sleep in pursuit of higher grades and academic achievement regardless of evidence that self-reported sleep quality and frequency significantly predict grades. We carried out a campus-wide anonymous internet 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. 1,294 students (78.0% undergraduates; 87.5% living off-campus, 77.5% female) from the University of Alberta, Canada participated. 30.5% reported sleeping less than 6.5 hours a night; 66.5% stated they had insufficient sleep; 80.6% reported they had not sought help. Those that did seek help turned to family/friends and physicians for the most part. The three most frequent behaviours students used to aid sleep were reading a book, listening to music, and adjusting the heat. Participants’ preferences for receiving more information were predominately short-online video clips with an option to contact someone for one-to-one more personalized advice. Although widely reported, students seldom sought help for sleep problems. Students already practice several sleep strategies (reading books for example) that, employing a strengths-based behavioural change intervention, could potentially be modified providing evidence-based sleep promoting strategies. Department of Occupational Therapy, Faculty of Rehabilitation Medicine, University of Alberta
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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 | 0.004 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".