The relationship between sleep hygiene and the prevalence of insomnia in medical students during the COVID-19 pandemic at the Faculty of Medicine, Universitas Sumatera Utara, Medan, Indonesia
Bibliographic record
Abstract
Background.Students with poor sleep quality will undoubtedly disturb their daily activities, such as being absent from lectures due to illness and falling asleep during lectures.a further impact of poor sleep quality is decreased student academic achievement.Objectives.To analyse the relationship between sleep hygiene and the prevalence of insomnia in medical students during the COVID-19 pandemic at the Faculty of Medicine, Universitas Sumatera Utara. Material and methods.The research design was analytic with a cross-sectional approach.The study population was medical students in clinical clerkships, which amounted to 152 people using consecutive sampling methods.The data was collected using a Sleep Hygiene Index (SHI) and Insomnia Severity Index (ISI) questionnaire, conducted online via Google Forms.Data processing was carried out using SPSS and the Chi-square statistical test.Results.Most students, as many as 101 people (66.4%), had moderate sleep hygiene, and most students were without insomnia (approx.61.8%).The results of the Chi-square test bivariate analysis showed a relationship between the degree of sleep hygiene and the prevalence of insomnia in the medical students of universitas Sumatera utara.Conclusions.Students are exposed to psychological impacts that can affect the quality of their sleep.Sleep hygiene and sleep cycles in students change due to changes in daily activities, such as physical activity, class schedules, assigned tasks and the use of electronic equipment.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".