Development of a Modified Korean Version of the Epworth Sleepiness Scale Reflecting Korean Sociocultural Lifestyle
Bibliographic record
Abstract
OBJECTIVE: The Epworth Sleepiness Scale is a measure used for the diagnosis of sleep disorders including obstructive sleep apnea (OSA) syndrome, insomnia, and narcolepsy. Although a Korean version has been developed (the KESS), Korean lifestyle such as the floor culture and low driving rates has not been considered. We aim to develop and validate a modified KESS (mKESS) that reflects the Korean lifestyle. METHODS: The sample consisted of 795 healthy participants and 323 OSA patients. The mKESS was developed by modifying several questions to concern the floor culture (questions 1, 2, 6, and 7) and low driving rates (question 8). Feasibility of the modification was tested by comparing the KESS and mKESS using paired samples t-test and by examining internal consistency reliability. Then, mKESS scores of the OSA patients and general participants were compared to test its validity. RESULTS: Questions 1, 2, 7, and 8 were significantly different when comparing the performances of the general population on both scales. Especially, the mean scores on question 8 were significantly different in the non-driver group, but not in the driver group. Cronbach's alpha of the mKESS was relatively higher than that of the KESS. Total mKESS scores of the OSA patients were significantly higher than that of the general population. CONCLUSION: The mKESS is more universally applicable for the clinical evaluation of people that live in Korea. Results support that the mKESS can be administered to measure the average daytime sleep propensity of the Korean population as an alternative to the KESS.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".