0339 The Perception of the Negative Impact of Workload and Study Load in Canadian University Students
Bibliographic record
Abstract
Abstract Introduction Studies showed that university students are at risk of presenting sleep difficulties. Various factors, including academic workload, and employment status can significantly impact their sleep. The present study aimed to investigate specific factors affecting sleep quality in Canadian university students. Methods A total of 1169 Canadian university students completed an online survey. Information about classes, hours devoted to study and work, and the negative impact of their studies and paid work on their sleep was gathered. A multiple regression and a simple linear regression analyses were done to verify the contribution of 1) study load and 2) workload (job) on the perception of a negative impact on sleep. Results In total, 64.8% of students were enrolled in at least 4 classes. They spent an average of 23.9 hours per week on their studies. A total of 47.3% reported a negative impact of 7 and more out of 10 (10 being the worst) of their studies on sleep. Analyses on study load show a significant model (F(2, 865)=33.13, p< 0.001) that explained 7% of the variance in the perception of a negative impact of studies on sleep. Results show that being enrolled in more classes (b=.29, t(865)=4.92, p<.001) and devoting more time to their studies (b=.03, t(865)=5.42, p<.001) significantly predict perception of a higher negative impact of studies on sleep. In total, 65.6% of students reported having a job, working an average of 20.1 hours per week. Results show that 56.1% reported a negative impact of 7 or more of their job on sleep. Analyses on workload show a significant model (F(1, 578)=109.91, p<.001) that explained 16% of the variance in the perception of a negative impact of their job on sleep. Results suggest that a higher number of hours worked (b=.10, t(578)=10.48, p<.001) significantly predict perception of a higher negative impact on sleep. Conclusion These results raise important issues regarding the study-work-sleep balance of Canadian university students. Many of them have a heavy study load, while many also have a job. Given the importance of sleep for mental health and cognitive performance, these results are of great concern. Support (if any)
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".