Machine-Learning Validated Short Form of the Korean Version of the Sleep-Related Behaviors Questionnaire-10 Items: SRBQ-10
Bibliographic record
Abstract
OBJECTIVE: This study aimed to develop a short form of the Korean version of the Sleep-Related Behaviors Questionnaire (SRBQ) and assess its validity and psychometric properties. METHOD: We collected 300 responses from the EMBRAIN survey system and conducted exploratory and confirmatory factor analyses to group SRBQ items based on response similarity. The most representative item from each group was selected using eXtreme Gradient Boosting (XGBoost). The psychometric properties of the final 10 items were assessed using the Rasch model of item response theory (IRT). RESULTS: Based on the selected 10 key items, we developed the SRBQ-10-a data-driven shortened version of the SRBQ, which demonstrated excellent performance (0.96) in predicting the SRBQ score, despite having only 10 items, which is one-third of the items in the original SRBQ-32. In addition to its reliability, the photometric properties of the SRBQ-10 were in the theoretically expected order, with no overlap or reversal of scale order, confirming the validity of the item scale. CONCLUSION: The SRBQ-10, a concise version of SRBQ, enables efficient screening of sleep-related behaviors in clinical settings. Our study framework combining classical test theory, XGBoost, and IRT can be applied to develop and validate shorter versions of other questionnaires.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".