Procrastination and anxiety: Exploring the contributions of multiple anxiety-related disorders
Bibliographic record
Abstract
Background Procrastination is the unnecessary delay of a task that subsequently creates anxiety (Rothblum, Solomon, & Maurakami, 1986). Research suggests that procrastination is linked with poorer mental health, but questions remain regarding its association with anxiety disorders. Studies exploring obsessive-compulsive disorder (OCD) and procrastination have found high levels of procrastination in OCD (Ferrari & McCown, 1994), but have also found no association between obsessive thoughts and procrastination (Kağan, Çakır, İlhan, & Kandemir, 2010). Scher and Osterman (2002) found that procrastination correlated with physiological anxiety and social anxiety, but not worry. No previous research has examined the connection between procrastination and health anxiety. Participants and procedure A non-clinical university sample (N = 300) completed online self-report questionnaires in order to examine the relationships between procrastination and symptoms of OCD, generalised anxiety disorder, social anxiety disorder, health anxiety, and panic disorder. Results Symptoms of panic disorder, social anxiety disorder, and health anxiety correlated with levels of procrastination. However, using a multiple regression analysis, only panic disorder symptoms uniquely predicted procrastination. Conclusions It is proposed that people with panic disorder may procrastinate to avoid anxiety inducing situations, or that individuals who frequently procrastinate may become sensitive to the anxiety caused by procrastination, thereby potentially triggering panic disorder. The full implications of these findings are further discussed.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".