Effects of Incontro, Alleanza, Responsabilita, Autonomia Intervention Model Combined with Orem Self-Care Model and the Use of Smart Wearable Devices on Perceived Stress and Self-Efficacy in Patients after Total Hip Arthroplasty
Post-publication record
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Bibliographic record
Abstract
Objective. To explore the effects of Incontro, Alleanza, Responsabilita, Autonomia (IARA) combined with Orem self-care model and the use of smart wearable devices on perceived stress and self-efficacy in patients after total hip arthroplasty (THA). Methods. A total of 60 patients after THA in our hospital were enrolled. Patients were randomly divided into control group (IARA intervention model combined with Orem self-care model) and study group (intelligent wearable device combined conference—IARA and Orem self-care model). Harris hip function score, Western Ontario and McMaster Universities Arthritis Index (WOMAC) score, functional independence measure (FIM) score, social support level, perceived stress, and self-efficacy were compared between the two groups. Results. Harris hip function score, WOMAC score, FIM score, and the level of social support of the study group were higher compared with the control group after operation ( P < 0.05). Additionally, the perceptual pressure in the study group was lower compared with the control group after intervention ( P < 0.05). The self-efficacy of the two groups was compared, and the self-efficacy of the study group was higher than that of the control group at 4, 6, 8, and 12 weeks after the intervention, and the difference was statistically significant ( P < 0.05). Conclusion. Patients after THA utilize an intelligent wearable device combined with IARA model and Orem self-care model, which can effectively reduce awareness pressure, improve self-efficacy, and facilitate the improvement of the hip fracture.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.002 | 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".