Sleep Disruption and Its Impact on Academic Performance in Medical Students: A Systematic Review
Bibliographic record
Abstract
Medical students are prone to sleep deprivation and poor sleep quality. Insomnia and stress are commonly the cause of lack of sleep among students. This systematic review aimed to assess sleep quality that affects the academic performance among medical students by determining the components that help in identifying sleep quality in medical students, to identify how sleep quality affects the academic performance of medical students and determines the factors that cause sleep deprivation in medical students. We conducted a comprehensive screening through various database sources to extract data about the prevalence of poor sleep quality among medical students and its impact on students' academic performance. Findings of the studies showed that the academic performance was significantly affected by sleep deprivation among the medical students and there was a significant correlation between sleep quality, academic performance and the factors affecting sleep quality. These findings calling for immediate attention in order to improve the students sleep quality as poor sleep quality is significantly correlated to their low academic performance. One study showed no significant difference between sleep and academic performance of students but sleep quality is significantly associated with elevated levels of stress. However, longitudinal study is needed to conclude for more certainty.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".