Evaluation of the Interaction Model of Client Health Behavior‐based multifaceted intervention on patient activation and osteoarthritis symptoms
Bibliographic record
Abstract
PURPOSE: This study evaluated a multifaceted intervention for osteoarthritis symptoms. METHODS: A cluster randomized controlled trial was conducted with a convenience sample of 90 patients aged between 45 and 82 years, who were recruited from three Community Health Posts (CHPs). The CHPs were randomized into two experimental groups (E1 and E2) and one control group (C). The intervention included health education and counseling combined with exercise classes based on the Interaction Model of Client Health Behavior. Trained community health nurse practitioners led the intervention. Stretching exercise (E1) and walking (E2) combined with muscle strengthening exercise were provided. The instruments included the Patient Activation Measure (PAM), Korean version of the Western Ontario and McMaster Universities Osteoarthritis Index (K-WOMAC), Stanford Health Assessment Questionnaire Disability Scale (HAQDS), and Center for Epidemiologic Studies Depression Scale (CES-D). The HAQDS was measured using an ordinal scale, and Likert scales were used for the other instruments. Chi-square test and analysis of covariance were used; P values <.05 were considered significant. The study was conducted in 2014. RESULTS: The groups were similar at the baseline (P > .05) except for joint pain and patient activation (P < .05). At post-test, the changes in the mean scores were significant for joint pain and stiffness, and physical functioning. E2 with walking exercise showed better improvements in joint pain and physical functioning than the other groups (P < .01). CONCLUSIONS: Eight sessions of multifaceted intervention improved the osteoarthritis symptoms. Follow-up studies will be needed to determine the intermediate and long-term effects of the multifaceted intervention.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".