Cognitive distortions, humor styles, and depression
Bibliographic record
Abstract
Cognitive distortions are negative biases in thinking that are theorized to represent vulnerability factors for depression and dysphoria. Despite the emphasis placed on cognitive distortions in the context of cognitive behavioural theory and practice, a paucity of research has examined the mechanisms through which they impact depressive symptomatology. Both adaptive and maladaptive styles of humor represent coping strategies that may mediate the relation between cognitive distortions and depressive symptoms. The current study examined the correlations between the frequency and impact of cognitive distortions across both social and achievement-related contexts and types of humor. Cognitive distortions were associated with reduced use of adaptive Affiliative and Self-Enhancing humor styles and increased use of maladaptive Aggressive and Self-Defeating humor. Reduced use of Self-Enhancing humor mediated the relationship between most types of cognitive distortions and depressed mood, indicating that distorted negative thinking may interfere with an individual's ability to adopt a humorous and cheerful outlook on life (i.e., use Self-Enhancing humor) as a way of regulating emotions and coping with stress, thereby resulting in elevated depressive symptoms. Similarly, Self-Defeating humor mediated the association of the social impact of cognitive distortions with depression, such that this humor style may be used as a coping strategy for dealing with distorted thinking that ultimately backfires and results in increased dysphoria.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".