Integrating self-management and exercise for people living with arthritis
Bibliographic record
Abstract
The Program for Arthritis Control through Education and Exercise, PACE-Ex™, is an arthritis self-management program incorporating principles and practice of self-management, goal setting and warm water exercise. The purpose of this program review is to examine the impact of PACE-Ex on participants' self-efficacy for condition management, self-management behaviors, goal achievement levels and self-reported disability, pain and health status. A retrospective review was conducted on participants who completed PACE-Ex from 1998 to 2006. A total of 347 participants completed 24 PACE-Ex programs [mean age 69.9 (±12.2) years, living with arthritis mean of 14.1 (±13.2) years]. Participants showed statistically significant improvements in their self-efficacy to manage their condition (Program for Rheumatic Independent Self-Management Questionnaire) (P < 0.001) and performance of self-management behaviors (Self-Management Behavior Questionnaire) (P < 0.01). Self-reported health status, disability and pain levels improved post-program (P < 0.01) despite reporting statistically significant increase in the total swollen and tender joint counts (Health Assessment Questionnaire) (P < 0.05). Sixty-eight percent of participants achieved or exceeded their long-term goal as measured by Goal Attainment Scaling. These findings remain to be proven with a more rigorous method yet they suggest that PACE-Ex is a promising intervention that supports healthy living for individuals with arthritis.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".