The Interplay of Neuroticism and Self-Efficacy in Pain Catastrophizing: A Quantitative Analysis
Bibliographic record
Abstract
This study aimed to examine the predictive roles of neuroticism and self-efficacy on pain catastrophizing in an adult population. A cross-sectional design was utilized, involving 290 participants who completed the Pain Catastrophizing Scale, the NEO Five-Factor Inventory for Neuroticism, and the General Self-Efficacy Scale. Data were analyzed using multiple linear regression in SPSS-27. Results indicated that neuroticism positively predicted pain catastrophizing, while self-efficacy showed a negative predictive relationship. The model accounted for approximately 26% of the variance in pain catastrophizing scores. The findings highlight the significant influence of neuroticism and self-efficacy on pain catastrophizing, suggesting that interventions aimed at reducing neuroticism and enhancing self-efficacy may be effective in mitigating pain catastrophizing in individuals.
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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 | 0.012 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".