The Correlation of Sleep Quality and Cognitive Function In Students Of the Faculty Medicine during the Covid-19 Pandemic
Bibliographic record
Abstract
Sleep deprivation often occurs in those who live in social situations and lifestyles that support the occurrence of these conditions, for example in people who have a large workload or academic demands, including medical students. Moreover, the outbreak of the COVID-19 pandemic since 2020 has made various universities suspend face-to-face learning which has an impact on increasing assignment assignments. This study aims to determine the relationship between sleep quality and cognitive function in the Class of 2020-2021 Students of the Faculty of Medicine of Tarumanagara University during the COVID-19 pandemic. The research method used is an analytical observational study using a cross-sectional analysis design. Data was collected online through filling out the Pittsburgh Sleep Quality Index (PSQI) and Indonesian-language Montreal Cognitive Assessment (MoCA-INA) questionnaires by respondents during the period of December 2021 to May 2022. The results of the study obtained 249 respondents, of which 159 respondents had poor sleep quality (63.9%). Poor sleep quality was more commonly found in respondents in the age group of 20-22 years (67.2%), female gender (68.2%), group with history of routine drug consumption (88.2%), and history of consuming caffeinated drinks >2x/day (76.3%). There is a significant relationship between sleep quality and cognitive function in the Class of 2020-2021 Students of the Faculty of Medicine of Tarumanagara University during the COVID-19 pandemic (RR=6.575, p<0.0001) as a conclusion. It is hoped that the results of this study can serve as educational material and reference especially for university students so that they can apply good sleep hygiene practices to optimize sleep quality and cognitive function. Keywords: COVID-19, PSQI, MoCA-INA
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".