Virtual or In‐Person: Does It Matter? Comparing Pain, Function, Quality of Life, Self‐Efficacy, and Physical Function Outcomes of Virtual, Hybrid, and In‐Person Education and Exercise Program Participants
Bibliographic record
Abstract
OBJECTIVE: This study aimed to determine if program format (in-person, virtual, or hybrid) results in differences in 3-month outcomes of pain, function, quality of life, self-efficacy, and chair stands in a hip/knee osteoarthritis-management program. METHODS: A secondary analysis of the Good Life with osteoArthritis in Denmark (GLA:D) Canada database was completed. Multiple linear regression was completed for pain and function, analysis of covariance for quality of life and self-efficacy, and negative binomial regression to analyze chair stands. Outcome measures included the 12-item Knee/Hip Injury and Osteoarthritis Outcome Score (pain, quality of life, and physical function subscales), Arthritis Self-Efficacy Scale (self-efficacy), and 30-second chair stand test. Models were adjusted for different covariates. RESULTS: ). When compared with in-person formats, there was no difference in virtual or hybrid formats at 3 months for pain, quality of life, or self-efficacy. When compared with in-person formats, the virtual format resulted in lower function scores (B = -1.71; 95% confidence interval [CI] -2.78 to -0.63) and the hybrid format performed 3% fewer chair stands at 3 months (incidence rate ratio 0.97; 95% CI 0.93-0.99), which is not a clinically important change. CONCLUSION: The GLA:D Canada program appears effective in virtual, hybrid, and in-person formats. With the known barriers of strictly in-person formats, these results provide further support for the research and implementation of virtual and hybrid approaches for helping individuals manage 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 | 0.002 | 0.007 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".