Sleepiness, sleep deprivation, quality of life, mental symptoms and perception of academic environment in medical students
Bibliographic record
Abstract
BACKGROUND: It has been previously shown that a high percentage of medical students have sleep problems that interfere with academic performance and mental health. METHODS: To study the impact of sleep quality, daytime somnolence, and sleep deprivation on medical students, we analyzed data from a multicenter study with medical students in Brazil (22 medical schools, 1350 randomized medical students). We applied questionnaires of daytime sleepiness, quality of sleep, quality of life, anxiety and depression symptoms and perception of educational environment. RESULTS: 37.8% of medical students presented mild values of daytime sleepiness (Epworth Sleepiness Scale - ESS) and 8.7% presented moderate/severe values. The percentage of female medical students that presented ESS values high or very high was significantly greater than male medical students (p < 0.05). Students with lower ESS scores presented significantly greater scores of quality of life and perception of educational environment and lower scores of depression and anxiety symptoms, and these relationships showed a dose-effect pattern. Medical students reporting more sleep deprivation showed significantly greater odds ratios of presenting anxiety and depression symptoms and lower odds of good quality of life or perception of educational environment. CONCLUSIONS: There is a significant association between sleep deprivation and daytime sleepiness with the perception of quality of life and educational environment in medical 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 | 0.003 |
| 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 | 0.000 | 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".