The impact of sleep, mental health, and gender on academic performance in Canadian university students
Bibliographic record
Abstract
Purpose To understand the independent and combined effects of sleep and mental health on academic performance, while also exploring gender differences. Methods A cross-sectional survey was distributed to undergraduate students at two Canadian universities in March 2022. Sleep quality and quantity was assessed using the Pittsburgh Sleep Quality Index. Mental health variables included stress, depression, and anxiety. Academic performance was self-reported as students’ cumulative percent average. Multiple linear regressions were used to investigate how (1) sleep, (2) mental health, (3) sleep and mental health together related to academic performance. These analyses were then repeated, stratified by gender. Results A total of 1,258 undergraduate students participated. While mental health and sleep duration predicted academic performance among the whole sample, there were important gender differences. In gender-stratified data, sleep quality and quantity predicted academic performance in men but not mental health in the combined model. For women, stress, depression, and anxiety predicted academic performance but not sleep quality. Sleep duration squared, but not sleep duration simply, was associated with academic performance in women. Conclusion Sleep and mental health are essential for academic performance in undergraduate students. Further, gender may play a critical role. Universities should consider gender-specific supports to improve the wellbeing of their students.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".