Client and clinician perspectives about a virtual education and exercise chronic disease management programme for people with hip and knee osteoarthritis
Bibliographic record
Abstract
Abstract Introduction Osteoarthritis (OA) is one of the most prevalent chronic conditions in Canada. Despite the established benefits of non‐pharmacological management (education, exercise) for people with OA, many do not receive treatment, resulting in pain, decreased physical function, and poorer quality of life. Virtual programme options grew significantly during the recent pandemic and may provide longer‐term opportunities for increased uptake by reaching individuals otherwise unable to participate. This study explored the experiences and perspectives of clients participating in and clinicians providing the Good Life with osteoArthritis: Denmark (GLA:D TM) Canada remote programme. Methods This qualitative descriptive study recruited 10 clients with hip and/or knee OA and 11 clinicians across Canada using purposive sampling. An online pre‐interview survey was completed, and individual interviews were conducted, audio‐recorded, transcribed verbatim and analysed independently by two researchers using inductive thematic analysis. Coding and analyses were initially conducted separately by group and then compared and combined. Results Four overarching themes (and 11 subthemes) were identified: (1) Expected and unexpected benefits of virtual programs; (2) Drawbacks to virtual programs; (3) Programme delivery in a virtual world; (4) Shifting and non‐shifting perspectives. Although initially sceptical, after completion of the programme, clients were in favour of virtual delivery with many benefits described. Clinicians' perspectives varied about feedback aimed to correct client movement patterns. Conclusions Clients and clinicians identified important experiential and procedural elements for virtual chronic disease management programs that include education and exercise. Additional work is needed to understand if the GLA:D TM remote outcomes are equivalent to the in‐person programme.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".