CIRCADIAN PREFERENCE AND COLLEGE STUDENTS’ BELIEFS ABOUT SLEEP EDUCATION
Bibliographic record
Abstract
The current study used social cognitive theory as a conceptual framework to investigate whether college students' beliefs about their sleep were compatible with sleep education, and whether incompatibility was greater for evening than morning or intermediate types. Students at a Canadian college (n = 499) completed an investigator-designed measure of outcome expectancies about how their sleep is affected by recommended sleep practices, self-efficacy beliefs about the ease of implementing the recommendations, a question about sleep status (i.e., good sleeper/poor sleeper), and the Composite Scale of Morningness (CSM). Contrary to predictions, outcome expectations of evening types did not differ from those of morning or intermediate types for 24 of the 26 items. Chi square tests indicated that most students' beliefs about the effects of sleep scheduling, caffeine consumption, sleep environment, and bedtime arousal were compatible with sleep education, whereas those about exercising, doing stimulating or important work close to bedtime, or using their beds for studying or watching TV were incompatible with sleep education. Consistent with predictions, ANOVA results indicated that global self-efficacy scores of evening types were lower, as were their item scores pertaining to sleep scheduling (i.e., napping, bedtimes, rise times, and staying in bed too long) and cognitive arousal in bed (i.e., thinking, worrying, or problem solving in bed or going to bed stressed, angry, nervous, or upset) than were those of intermediate or morning types. Results of an ANCOVA showed that evening preference was associated with poorer self-efficacy when differences in sleep status were controlled. Finally, Pearson correlations and stepwise multiple regression showed evening preference and describing oneself as a poor sleeper both contributed to low self-efficacy. These findings are relevant to the refinement of sleep education. Content included in sleep education needs to consider what students already know about sleep so that education is relevant, credible, and not redundant. Sleep education also needs to address lower self-efficacy of evening types for implementing sleep recommendations. Conclusions reported in this study should be considered tentative because they were based on a single Canadian sample using a novel measure. The generalizability of the results remains to be determined.
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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 | 0.000 | 0.000 |
| 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) | 0.003 | 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".