Intolerance of uncertainty, perfectionism, and coping as predictors of depression diagnosis and severity
Bibliographic record
Abstract
Major Depressive Disorder (MDD) is the leading cause of disability worldwide, affecting 3.8% of the global population. Despite its prevalence, less than half of those diagnosed with MDD receive treatment and remission rates remain low. Given these poor outcomes for individuals with depression and the findings of our previous study examining the role of certain psychological phenomena on the incidence of obsessive-compulsive disorder (OCD), the present study aims to examine whether intolerance of uncertainty, perfectionism, and coping strategies can together predict the diagnosis and severity of MDD. Participants were outpatients (N = 549) referred to a tertiary care clinic in Toronto, Canada between 2011 and 2014. After undergoing a diagnostic assessment, participants were administered a series of self-report questionnaires that measured intolerance of uncertainty, perfectionism, and coping. Results demonstrate that task-oriented coping and emotion-oriented coping significantly predicted depression diagnosis, while avoidant coping, perfectionism, and intolerance of uncertainty did not. As for depression severity, significant predictors included perfectionism, task-oriented coping, emotion-oriented coping, and avoidant coping. Further research is needed to identify interactions between the subscales of these constructs to determine how they work in tandem to influence MDD. Our findings indicate a need for more personalized interventions in the treatment of this disorder.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".