OA Go Away: Development and Preliminary Validation of a Self-Management Tool to Promote Adherence to Exercise and Physical Activity for People with Osteoarthritis of the Hip or Knee
Bibliographic record
Abstract
Purpose: To determine the face and content validity, construct validity, and test–retest reliability of the OA Go Away (OGA), a personalized self-management tool to promote adherence to exercise and physical activity for people with osteoarthritis (OA) of the hip or knee. Methods: The face and content validity of OGA version 1.0 were determined via interviews with 10 people with OA of the hip or knee and 10 clinicians. A revised OGA version 2.0 was then tested for construct validity and test–retest reliability with a new sample of 50 people with OA of the hip or knee by comparing key items in the OGA journal with validated outcome measures assessing similar health outcomes and comparing scores on key items of the journal 4–7 days apart. Face and content validity were then confirmed with a new sample of 5 people with OA of the hip or knee and 5 clinicians. Results: Eighteen of 30 items from the OGA version 1.0 and 41 of 43 items from the OGA version 2.0 journal, goals and action plan, and exercise log had adequate content validity. Construct validity and test–retest reliability were acceptable for the main items of the OGA version 2.0 journal. The OGA underwent modifications based on results and participant feedback. Conclusion: The OGA is a novel self-management intervention and assessment tool for people with OA of the hip or knee that shows adequate preliminary measurement properties.
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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 | 0.014 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".