Exploring the relationship between self-compassion and sleep
Bibliographic record
Abstract
Quality sleep can promote mental and physical health, and improve exercise quality. University students get insufficient quality and quantity of sleep. Self-compassion has been associated with increased sleep quality among university students but little is known about the processes that underlie this relationship. Self-compassion may lead to improved sleep through reducing maladaptive and promoting adaptive cognitive emotion regulation strategies. Self-compassion may also promote sleep through its positive association with a proactive health focus. The purpose of this cross-sectional study was to explore the relationship between self-compassion and sleep and to determine if this relationship is mediated by cognitive emotion regulation strategies and a proactive health focus. In this cross-sectional study, undergraduate students (N=193) completed measures of the following constructs through an online survey; self-compassion (independent variable); self-esteem (control variable); the two sleep outcomes (sleep quality; sleep hygiene); and two proposed mediators (cognitive emotion regulation; proactive health focus). Mediation analysis using Hayes PROCESS macro revealed that, after controlling for self-esteem, two cognitive emotion regulation strategies mediated the relationship between self-compassion and sleep outcomes: self-blame mediated the relationship between self-compassion and sleep quality (b = 0.55, BCa CI [0.075, 1.093]) and rumination mediated the relationship between self-compassion and sleep-hygiene (b = -0.07, BCa CI [ -0.137, -0.002]). Proactive health focus did not mediate the relationship between self-compassion and either sleep outcome. These findings contribute to the literature by highlighting that self-compassion may be associated with positive sleep outcomes through its negative association with maladaptive cognitive emotion regulation strategies.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".