Participants’ perceived benefits from the GLA:D™ program for individuals living with hip and knee osteoarthritis: a qualitative study
Bibliographic record
Abstract
BACKGROUND: The Good Life with osteoArthritis: Denmark (GLA:D™), an evidence-based education and exercise program designed for conservative management of knee and hip osteoarthritis (OA), has been shown to benefit participants by reducing pain, improving function, and quality of life. Standardized reporting in the GLA:D databases enabled the measurement of self-reported and performance-based outcomes. There is a paucity of qualitative research on the participants' perceptions of this program, and it is important to understand whether participants' perceptions of the benefits of the program align with reported quantitative findings. METHODS: We conducted semi-structured telephone interviews with individuals who participated in the GLA:D program from January 2017 to December 2018 in Alberta, Canada. Data were analyzed using an interpretive description approach and thematic analysis to identify emergent themes and sub-themes associated with participants perceived benefits of the GLA:D program. We analyzed the data using NVivo Pro software. Member checking and bracketing were used to ensure the rigour of the analysis. RESULTS: 30 participants were interviewed (70% female, 57% rural, 73% knee OA). Most participants felt the program positively benefited them. Two themes emerged from the analysis: wellness and self-efficacy. Participants felt the program benefited their wellness, particularly with regard to pain relief, and improvements in mobility, strength, and overall well-being. Participants felt the program benefited them by promoting a sense of self-efficacy through improving the confidence to perform exercise and routine activities, as well as awareness, and motivation to manage their OA symptoms. Twenty percent of participants felt no benefits from the program due to experiencing increased pain and feeling their OA was too severe to participate. DISCUSSION: The GLA:D program was viewed as beneficial to most participants, this study also identified factors (e.g., severe OA, extreme pain) as to why some participants did not experience meaningful improvements. Early intervention with the GLA:D program prior to individuals experiencing severe OA could help increase the number of participants who experience benefits from their participation. CONCLUSION: As the GLA:D program expands across jurisdictions, providers of the program may consider recruitment earlier in disease progression and targeting those with mild and moderate OA.
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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 | 0.013 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".