Less Adaptive or More Maladaptive? A Meta–analytic Investigation of Procrastination and Coping
Bibliographic record
Abstract
Despite the theoretical and empirical accounts of trait procrastination as reflecting avoidance of aversive tasks as a means of mood repair, research documenting its links to coping is scarce and inconsistent. There is also little if any research to date examining whether coping strategies might explain the procrastination–stress relationship. The current research aimed to address these issues by integrating current research on procrastination and coping with our own data into a first meta–analysis of the associations of procrastination with adaptive and maladaptive coping and then testing the potential role of coping for understanding the procrastination–stress relationship. In Study 1, a literature search yielded five published papers and three theses, which were supplemented by seven unpublished data sets comprising 15 samples ( N = 4357). Meta–analyses revealed that procrastination was positively associated with maladaptive coping (average r = .31) and negatively associated with adaptive coping (average r = −.24). In Study 2, a meta–analysis of the indirect effects through coping across four samples revealed that the average indirect effects for maladaptive but not adaptive coping explained the link between procrastination and stress. These findings expand the nomological network of procrastination and highlight the role of maladaptive coping for understanding procrastinators‘ stress. Copyright © 2015 European Association of Personality Psychology
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.020 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.023 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".