Is procrastination related to sleep quality? Testing an application of the procrastination–health model
Bibliographic record
Abstract
Despite a growing body of research on the consequences of procrastination for health and well-being, there is little research focused on testing or explaining the potential links between procrastination and sleep quality. Using the procrastination–health model as our guiding conceptual lens, we addressed this gap by examining how and why trait procrastination may be linked to various dimensions of sleep quality across two student samples. In Study 1, procrastination was associated with feeling unrested, but not with sleep disturbance frequency, in a sample of Greek undergraduate students (N = 141). In Study 2, bootstrapping analysis of the indirect effects of procrastination on an index of sleep quality through perceived stress in a sample of Canadian students (N = 339) was significant, supporting an extended procrastination–health model view of how chronic self-regulation failure may compromise sleep quality. Given the potential for dynamic and reciprocal relations among procrastination, stress, and sleep quality, suggested by the current and other research, the ways in which procrastination may contribute to and be influenced by poor sleep quality warrant further investigation.
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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 | 0.013 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".