Assessment of Continuous Care Based on the Roy Adaptation Model in Patients Undergoing Total Knee Replacement: A Quasi-Experimental Study
Bibliographic record
Abstract
BACKGROUND/AIMS:To assess the effectiveness of continuous care based on the Roy Adaptation Model (RAM) in patients undergoing total knee replacement (TKR) surgery. MATERIALS AND METHODS:This quasi-experimental study included 83 patients in a university hospital.The intervention group was offered continuous care based on RAM.The research data were collected using a Patient Identification Form, the Western Ontario and McMaster Universities Osteoarthritis Index, and the Hospital Anxiety and Depression Scale. RESULTS:Except for the pain score, no statistically significant difference in the pre-discharge and 3 rd month was found for the patients in the intervention and control groups.It was determined that the pain scores of patients in the intervention group in the pre-discharge period were lower than those in the control group (p=0.022)A significant difference was found between the anxiety score averages in time in the intervention group in terms of the group time interaction (p=0.009).Because of further analysis, a statistically significant difference was determined that the anxiety scores of patients in the intervention group in the 3 rd month were lower than those in the control group (p=0.032).A significant difference was found between the depression score averages in time in the intervention group in terms of the group x time interaction (p=0.037). CONCLUSION:The functional status and pain of patients improve over time.In this process, continuous care based on RAM was effective in developing effective adaptation behaviors of patients, and a positive effect on pain, anxiety, and depression was determined.
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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 | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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".