Self-critical perfectionism and lower daily perceived control predict depressive and anxious symptoms over four years.
Bibliographic record
Abstract
This study of 152 community adults examined whether perfectionism interacts with daily perceived control to predict depressive and anxious symptoms over 4 years. Participants completed measures of higher-order perfectionism dimensions [self-critical (SC), personal standards (PS)] and neuroticism at time 1, daily diaries for 14 consecutive days to assess perceived control over most bothersome events at time 2 three years later, and measures of depressive and anxious symptoms at time 1, time 2, and time 3 four years after baseline. Hierarchical regression analyses of moderator effects demonstrated that individuals with higher SC perfectionism at time 1 and lower perceived control across daily stressors at time 2 had higher levels of depressive symptoms at time 3 than others, adjusting for the effects of time 1 and time 2 depressive and anxious symptoms. Higher SC perfectionism also interacted with lower perceived control to predict time 3 anxious symptoms. PS perfectionism and neuroticism did not interact with perceived control to predict time 3 depressive or anxious symptoms. These findings highlight the importance of addressing cognitive appraisals of one's control over handling daily stressors for the prevention and treatment of depressive and anxious symptoms in individuals with higher SC perfectionism. (PsycInfo Database Record (c) 2020 APA, all rights reserved).
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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) | 0.000 | 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 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".