‘I do hope more people can benefit from it.’: The qualitative experience of individuals living with osteoarthritis who participated in the GLA:D™ program in Alberta, Canada
Bibliographic record
Abstract
INTRODUCTION: The Good Life with osteoArthritis: Denmark (GLA:DTM) is an evidence-based program designed for individuals with symptomatic hip and knee osteoarthritis (OA). This program has reported improvement in pain, quality of life and self-efficacy, as well as delays in joint replacement surgery for adults with moderate to severe hip or knee OA. Evaluations of GLA:DTM implementation in several countries have focused on effectiveness, training, and feasibility of the program primarily from the provider perspective. Our objective was to examine how the GLA:DTM program was perceived and experienced by individuals with hip and knee OA to inform on-going program refinement and implementation. METHODS: Thirty semi-structured telephone interviews were conducted with participants who completed the GLA:DTM program in Alberta. An interpretive description approach was used to frame the study and thematic analysis was used to code the data and identify emergent themes and sub-themes associated with participants' experience and perception of the GLA:DTM program. RESULTS: Most participants had a positive experience of the GLA:DTM program and particularly enjoyed the group format, although some participants felt the group format prevented one-on-one support from providers. Three emergent themes related to acceptability were identified: accessible, adaptable, and supportive. Participants found the program to be accessible in terms of location, cost, and scheduling. They also felt the program was adaptable and allowed for individual attention and translatability into other settings. Finally, most participants found the group format to be motivating and fostered connections between participants. CONCLUSION: The GLA:DTM program was perceived as acceptable by most participants, yet the group format may not be useful for all individuals living with OA. Recommended improvements included adapting screening to identify those suited for the group format, providing program access earlier in the disease progression trajectory, modifying educational content based on participants' knowledge of OA and finally, providing refresher sessions after program completion.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.028 | 0.017 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.006 |
| 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".