Risk-Taking, Cognitive Distortions, and Their Influence on Proactive Coping in Higher Education
Bibliographic record
Abstract
The objective of this study was to investigate the relationships between proactive coping, risk-taking, and cognitive distortions among university students. Specifically, it aimed to determine how risk-taking behaviors and cognitive distortions predict proactive coping strategies. A cross-sectional design was employed with a sample of 265 university students. Participants completed self-report measures, including the Proactive Coping Inventory (PCI), Domain-Specific Risk-Taking (DOSPERT) Scale, and the Cognitive Distortion Scale (CDS). Data were analyzed using Pearson correlation and multiple regression analyses to explore the relationships between the variables. Assumptions for normality, linearity, and homoscedasticity were checked and confirmed prior to analysis. Descriptive statistics indicated moderate levels of proactive coping and cognitive distortions, and relatively high levels of risk-taking among participants. Pearson correlation analysis revealed that proactive coping was positively correlated with risk-taking (r = 0.56, p < .001) and negatively correlated with cognitive distortions (r = -0.42, p < .001). Multiple regression analysis showed that risk-taking (B = 0.52, p < .001) and cognitive distortions (B = -0.37, p < .001) were significant predictors of proactive coping, explaining 45% of the variance (R² = 0.45, p < .001). The findings suggest that both risk-taking and cognitive distortions play significant roles in shaping proactive coping strategies among university students. Risk-taking positively influences proactive coping, while cognitive distortions have a detrimental effect. These insights can inform the development of targeted interventions aimed at enhancing proactive coping skills by promoting adaptive risk-taking and addressing cognitive distortions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".