Self-Compassion and Bedtime Procrastination: an Emotion Regulation Perspective
Bibliographic record
Abstract
The current study extended previous research on self-compassion and health behaviours by examining the associations of self-compassion to bedtime procrastination, an important sleep-related behaviour. We hypothesized that lower negative affect and adaptive emotion regulation would explain the proposed links between self-compassion and less bedtime procrastination. Two cross-sectional online studies were conducted. Study 1 included 134 healthy individuals from the community (mean age 30.22, 77.4% female). Study 2 included 646 individuals from the community (mean age 30.74, 68.9% female) who were screened for the absence of clinical insomnia. Participants in both studies completed measures of self-compassion, positive and negative affect and bedtime procrastination. Participants in study 2 also completed a measure of cognitive reappraisal. Multiple mediation analysis in study 1 revealed the expected indirect effects of self-compassion on less bedtime procrastination through lower negative affect [ b = − .09, 95% CI = (− .20, − .02), but not higher positive affect. Path analysis in study 2 replicated these findings and further demonstrated that cognitive reappraisal explained the lower negative affect linked to self-compassion [ b = − .011; 95% CI = (− .025; − .003)]. The direct effect of self-compassion on less bedtime procrastination remained significant. Our novel findings provide preliminary evidence that self-compassionate people are less likely to engage in bedtime procrastination, due in part to their use of healthy emotion regulation strategies that downregulate negative mood.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".