HUBUNGAN KUALITAS TIDUR DENGAN FUNGSI KOGNITIF PADA MAHASISWA FAKULTAS KEDOKTERAN UNIVERSITAS SUMATERA UTARA
Bibliographic record
Abstract
ABSTRACT Background: Sleep is an essential need in daily lives with abundant functions, especially neuron restoring process in neocortex after various activities. Poor sleep quality is often found, resulting from the needs of work, education, lifestyle, and sociocultural demand. It could hinder cognitive function thus affecting daily life quality. Thus, the objective of this study is to assess the risk factors of poor sleep quality and its relation to cognitive function of college students at Faculty of Medicine, Universitas Sumatera Utara. Method: This research uses an analytic method with cross-sectional design where the collection of data is simultaneously taken at one time. The data are primarily obtained from interviews utilising validated questionnaires, Pittsburgh Sleep Quality Index (PSQI) for sleep quality and Montreal Cognitive Assessment (MoCA) for cognitive function, with stratified random sampling technique. Results: The incidence of poor sleep quality of the samples is 69% with the predominance of being male, aged 17-19, having higher body mass index, and consuming caffeinated beverages. Meanwhile, the incidence of mild cognitive impairment is 42%. The Chi-Square and Spearman Rank tests show a significant relation (p=0.009) and weak correlation (r=0.262, p=0.008) between age groups and sleep quality, whereas there is no association of gender (p=0.517) and body mass index (p=0.322) with sleep quality. The Fisher’s Exact test yields no relation between consumption of caffeine (p=0.778) and sleep quality. According to the Chi-Square test, sleep quality is not associated with cognitive function as well (p=0.993). Conclusion. There is no association of sleep quality with cognitive function, however there is a significant relation and weak correlation between age groups and sleep quality.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.025 | 0.006 |
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".