Are they sleeping? A Canadian cross-sectional study exploring sleep quality among medical students: a study protocol
Bibliographic record
Abstract
BACKGROUND/PURPOSE: Medical students face a rigorous academic environment that often leads to compromised sleep quality, which is essential for cognitive function, mental well-being and clinical performance. Despite the importance of sleep, a significant percentage of medical students report poor sleep quality, with detrimental effects on their academic performance and overall well-being. Previous research has primarily focused on international populations, leaving a gap in understanding the sleep habits and challenges faced by Canadian medical students. METHODS AND ANALYSIS: This cross-sectional study aims to assess the sleep quality among medical students across six Canadian medical schools. The study will use validated instruments, including the Pittsburgh Sleep Quality Index and Epworth Sleepiness Scale, to measure sleep quality and daytime sleepiness. Participants will be recruited via electronic surveys distributed through email and social media. The study will collect demographic data and explore factors impacting sleep quality, such as academic workload, electronic device usage before bedtime and stress levels. Data analysis will involve stepwise multiple regressions to identify predictors of poor sleep quality. ETHICS AND DISSEMINATION: Approvals have been granted by the Research Ethics Boards at the University of Ottawa and at each participating site. Results will be reported in a peer-reviewed journal and submitted for presentation at national and international conferences.
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 | 0.021 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.013 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".