Procrastination and Health in Nurses: Investigating the Roles of Stress, Health Behaviours and Social Support
Bibliographic record
Abstract
Objectives: Evidence linking chronic procrastination to a range of poor health outcomes and trajectories continues to build. Yet, much of this research has been conducted in academic contexts or in non-student samples. Despite theory indicating that high-stress contexts increase vulnerability for procrastination, the pathways linking chronic procrastination to health outcomes proposed by the procrastination–health model have not been examined in a high stress environment. Accordingly, we tested the contribution of procrastination to health in nurses and whether social support was a protective factor. Design: Pre-registered cross-sectional study using a random sample of nurses recruited from the membership of a regional nursing association, supplemented by nurses and nurse trainees recruited from online nursing associations, conferences and forums. Methods: Nurses and nurse trainees (N = 597) completed measures of chronic procrastination, stress, health behaviours, social support and self-rated health. Results: Chronic procrastination was associated with perceived stress, health behaviours, self-rated health and social support in the expected directions. Consistent with the procrastination–health model, structural equation modelling revealed significant indirect effects linking chronic procrastination to poor self-rated health through higher stress and fewer health behaviours. Contrary to our hypotheses, social support did not moderate these pathways. Conclusions: This study is the first to demonstrate the relevance of procrastination for health in high-stress, non-academic contexts and to find support for both the stress and behavioural pathways linking procrastination to poor health outcomes. Findings further highlight the importance of addressing chronic procrastination as a vulnerability factor for poor health in nurses.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".