Effects of Integrating Pain Coping Strategies into Occupational Therapy After Total Knee Arthroplasty: A Parallel Mixed-Method Study
Bibliographic record
Abstract
Background/Objectives: This study aimed to evaluate whether integrating coping strategies into occupational therapy (OT) enhances functional recovery and psychological adaptation after total knee arthroplasty (TKA). Methods: Twenty-eight patients who underwent TKA were equally assigned to an intervention and control group. Both groups received standard goal-oriented OT, while only the intervention group underwent structured training in pain coping strategies using the “Coping List”. Treatment effects were assessed using Canadian Occupational Performance Measure (COPM), pain, anxiety, depression, and pain-related disability scores. Results: A total of 210 coping strategies were identified, with the intervention group adopting an average of 15.1 additional strategies per patient. Strategies were categorized into six domains: physical, psychological and cognitive, social support, relaxation, daily activities, and medication management. The intervention group showed significantly greater improvements in COPM performance scores (7.6 ± 1.7 vs. 5.5 ± 2.6; p = 0.048) and COPM satisfaction scores (7.9 ± 2.0 vs. 5.6 ± 2.8; p = 0.049) compared to the control group. Conclusions: The findings suggest that individualized coping strategies integrated with physical rehabilitation can help patients achieve postoperative goals, enhance recovery, and improve overall well-being. Incorporating such strategies into OT appears to be effective in early-postoperative rehabilitation. Clinical trial number: This study’s clinical trial registration information is available online at UMIN (UMIN000050536).
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".