0797 THE CORRELATION BETWEEN NAPS AND OTHER SLEEP INDICES IN COLLEGE STUDENTS
Bibliographic record
Abstract
College is a common time to nap frequently given self-chosen class schedules and the absence of parental supervision. Napping has been shown to correlate with students’ high achievement yet, there is little research on how naps affect other sleep indices. Knowledge of how naps affect total sleep time, sleep efficiency, sleep onset latency, and other important sleep indices, can offer insight into when, and for whom, naps can be beneficial. Fifty-four college students (age: M=19.58, SD=1.49; gender: 82.7% female) used actigraph watches (Actigraph Corp.) for an average of 7 days, recording their wake- and sleep-related movement. They completed sleep diaries each morning regarding their sleep the night before. Sleep diaries were used to inform actigraphy data calculated by Actilife software, specifically to obtain sleep-onset latency. Three participants provided unusable actigraphy data and were excluded from analyses. Number of sleep periods over the course of one week (range: 6–15) was used as the measure of naps and correlated with actigraphy measures. More sleep periods correlated with less total sleep time (r = -.451, p = .001), less time awake after sleep onset (r = -.353, p = .011), fewer awakenings (r = -.498, p < .001), and increased sleep efficiency (r = .287, p = .041). There was also a trending correlation with shorter sleep onset latency (r = -.244, p = .084). The findings suggest that those students who are napping more are not getting enough sleep during the night and are incurring greater sleep debt, and hence greater sleep drive. This would explain an increased number of naps during the week, as well as greater efficiency of sleep during the hours they do sleep at night. None.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".