Impact of Psychological Resilience on the Fear of Pain and Activity Recovery in Postsurgical Patients: Observational Cohort Study
Bibliographic record
Abstract
BACKGROUND: Fear of pain (FOP) and fear avoidance belief (FAB), play a crucial role in the occurrence and development of chronic pain. However, the dynamics of these factors in post-surgical pain appear to differ, with FOP often learned from others rather than directly caused by painful experiences. Psychological resilience refers to an individual's capacity to adapt effectively to adversity, challenges, and threats, and may play a significant role in overcoming FOP and avoidance behavior. OBJECTIVE: The aim of this study was to investigate the role of psychological resilience in overcoming FOP and avoidance behavior among surgical patients undergoing lung surgery. METHODS: Participants were recruited at Wuhan union hospital. Psychological resilience was measured using the Connor-Davidson Resilience Scale. FOP was assessed using the simplified Chinese version of the Fear of Pain-9 items. FAB was measured using the Physical Activity subscale of the Fear-Avoidance Beliefs Questionnaire. Activity recovery was assessed through questions related to social activities and household responsibilities. Adaptive Lasso regression analysis under nested cross-validation was used to identify key factors affecting postoperative FOP and activity recovery. RESULTS: A total of 144 participants were included in the final analysis. The results showed that preoperative FOP (coefficient =8.620) and FAB (coefficient =8.560) were mainly positively correlated with postoperative FOP, while psychological resilience (coefficient =-5.822) and age (coefficient =-2.853) was negatively correlated with it. The model explains an average 73.1% of the variance. In predicting activity recovery, psychological resilience was the most important factor, and the model obtained an average accuracy of 0.820 ± 0.024 and an average AUC of 0.926 ± 0.044. CONCLUSIONS: Psychological resilience is negatively associated with postoperative FOP and positively with activity recovery in lung surgery patients. Patients with higher resilience are more likely to cope effectively with post-surgical pain and recover activities sooner. These findings highlight the importance of assessing and potentially enhancing psychological resilience in the perioperative period to improve postoperative outcomes. CLINICALTRIAL: Registration Number: ChiCTR2200056651.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".