0361 Prevalence and Predictive Factors of Sleeping Medication Use Among Students at a Canadian University
Bibliographic record
Abstract
University students experience high levels of stress and sleep disturbance, increasing the likelihood of sleep-promoting substance use. Long-term use of sleeping aids can lead to chronic sleep problems and dependence. It is important to understand the factors that predict such use in order to offer prevention/intervention programs for at-risk groups. 3,699 students aged 18–35 at Memorial University of Newfoundland were surveyed. Participants indicated whether they had used sleeping medications (over the counter and/or prescription) in the last month and completed the Insomnia Severity Index, Pittsburgh Sleep Quality Index, Hospital Anxiety and Depression Scale, and the Prescription Drug Attitudes Questionnaire. Univariate and multivariate logistic regression were used to examine the demographic and clinical factors associated with sleeping medication use. 73% of participants were female and 49% aged 18–21 years. 22% reported using at least one type of sleep-promoting substance. In the multivariate model, female students were more likely than males to report using sleeping medication (AOR=1.65; 95%CI, 1.18 to 2.30; p=0.003). Students aged 24–35 were more likely than those aged 18–21 (AOR=1.95; 95%CI, 1.28 to 2.96; p=0.002) and those with poor sleep quality were more likely to use sleeping medication than those with good sleep quality (AOR=2.07; 95%CI, 1.26 to 3.41; p=0.004). Not surprisingly, insomnia symptoms was a robust predictor of sleeping medication use (Mild: AOR=2.34; 95%CI, 1.68 to 3.25; p<0.001; Moderate: AOR=3.60; 95%CI, 2.37 to 5.48; p<0.001; Severe: AOR=6.93; 95%CI, 3.12 to 15.38; p<0.001). Students with the most positive attitudes towards non-medical use of prescription drugs were more likely to use sleeping medication (AOR=2.31; 95%CI, 1.63 to 3.27; p<0.001). This is the first study to examine sleeping medication use among students at a Canadian university. The results indicate that almost 25% of students are using substances to help them sleep. Students who are female, over 24 years old, have insomnia or more lenient attitudes towards substance use are all more likely to use sleeping medications. These groups are targets for sleep education and stress management interventions. Support (If Any):
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".