Exploring the interactive effect of dysfunctional sleep beliefs and mental health on sleep in university students
Bibliographic record
Abstract
Introduction Poor mental health is a known risk factor for poor sleep among university students; however, less is known about the role of dysfunctional sleep beliefs and its relation to mental health and sleep. Additionally, students who identify as people of color (POC) may experience unique stressors related to discrimination and inequalities which can contribute to mental health issues and in turn, influence their sleep. The present study evaluated the impact of dysfunctional sleep beliefs and poor mental health on a student's susceptibility to worse sleep and examined differences among POC. Methods Post-secondary students completed a survey including the Insomnia Severity Index and the Dysfunctional Beliefs and Attitudes about Sleep Scale. Participants also completed questionnaires measuring symptoms of depression, anxiety, and perceived stress. Results One thousand five hundred and sixty-two students were included in the analyses, 58% of which were POC. POC students had more dysfunctional sleep beliefs (p < 0.01) and worse insomnia severity (p < 0.01) compared to white students. Overall, greater dysfunctional sleep beliefs were significantly associated with worse symptoms of depression (b = 1.521), anxiety (b = 1.170), stress (b = 1.370), and poor sleep (b =1.963; ps < 0.001). Dysfunctional sleep beliefs also moderated the relation between poor mental health and sleep, specifically depression (p = 0.035) and anxiety (p = 0.007), by exacerbating sleep outcomes. Discussion The results suggest that dysfunctional sleep beliefs may play a role in perpetuating poor mental health and sleep. Interventions to improve students' sleep and wellbeing focused on reframing dysfunctional sleep beliefs should be examined.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".