Social jetlag negatively correlates with academic performance in undergraduates
Bibliographic record
Abstract
Discrepancies between sleep timing on workdays and weekends, also known as social jetlag (SJL), affect the majority of the population and have been found to be associated with increased health risk and health-impairing behaviors. In this study, we explored the relationship between SJL and academic performance in a sample of undergraduates of the Semmelweis University. We assessed SJL and other sleep-related parameters with the Munich ChronoType Questionnaire (MCTQ) (n = 753). Academic performance was measured by the average grade based on weekly test results as well as scores acquired on the final test (n = 247). The average mid-sleep point on free days in the Hungarian sample fits well the regression line plotted for longitudes within the Central European Time Zone and chronotypes, confirming that sunlight has a major impact on chronotype. Multivariate analysis showed negative effect of SJL on the weekly average grade (p = 0.028, n = 247) during the lecture term with its highly regular teaching schedules, while this association disappeared in the exam period (p = 0.871, n = 247) when students had no scheduled obligations (lower SJL). We also analyzed the relationship between the time of the weekly tests and academic performance and found that students with later sleep times on free days achieved worse in the morning (p = 0.017, n = 129), while the inverse tendency was observed for the afternoon test-takers (p = 0.10, n = 118). We did not find significant association between academic performance and sleep duration or sleep debt on work days. Our data suggest that circadian misalignment can have a significant negative effect on academic performance. One possible reason for this misalignment is socially enforced sleep times.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".