Operation Change: A New Paradigm Addressing Behavior Change and Musculoskeletal Health Disparities
Bibliographic record
Abstract
BACKGROUND: In this study, we examined the implementation and efficacy of Operation Change, a community-based, culturally sensitive program to stimulate behavioral changes in activity level and improve musculoskeletal health in African-American (AA) and Hispanic/Latina (H/L) women with obesity and early-stage osteoarthritis. METHODS: Sixty-two women (32 AA and 30 H/L), 40-75 years old, with nontraumatic knee pain and body mass index values > 30, participated in a 12-week program of presentations, motivational interviewing, goal setting, and physical activities. Assessments (at 0, 6, and 12 weeks) included a demographic questionnaire, physical assessment, timed 50-ft walking test, Western Ontario and McMaster Universities Arthritis Index (WOMAC), Short Form-36 Health Survey (SF-36), 8-Item Physical Health Questionnaire (PHQ-8), and motivational interview assessment. RESULTS: Walking time improved significantly for H/L women (P < 0.0001) but not AA women (P = 0.0759). Both groups had significant mean weight loss (P < 0.05) with high variability among individuals. WOMAC scores for both groups indicated decreased pain (P < 0.0001) and stiffness (P < 0.0001) and improved physical functioning (P < 0.0001) by 12 weeks. SF-36 results were comparable to those of the WOMAC. PHQ-8 results improved significantly for H/L women (P < 0.0001) but not AA women (P = 0.077). Participants scored the motivational interviewing component of the program favorably. CONCLUSIONS: Participation in Operation Change increased physical activity, resulting in improvements in pain and function scores. This supports a new paradigm for behavioral modification that helps AA and H/L women take an active role in living with osteoarthritis.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".