Patient perspectives on improving osteoarthritis management in urban and rural communities
Bibliographic record
Abstract
INTRODUCTION: Although there is no cure for osteoarthritis (OA), there are lifestyle modifications that can mitigate symptoms such as pain, and improve management of the disease. This information is not always translated to community-dwelling seniors. Individuals in rural areas often face additional challenges due to geographic isolation and decreased access to community services. METHODS: We used qualitative research methodology (hermeneutic phenomenology) to better understand the lived experiences of urban and rural community-dwelling seniors diagnosed with OA. We explored their sources of information about OA, how they manage their OA pain, and how OA management could be improved in the community. Purposeful sampling was used to recruit 20 information-rich participants (11 urban, 9 rural) in Ontario, Canada. All participants were aged >65 and diagnosed with OA. Semi-structured interviews were conducted, audio recorded, and transcribed verbatim. NVivo 11 Pro qualitative software was used to code transcripts. RESULTS: Thematic analysis revealed 9 key themes where 8 were common to urban and rural participants, and 1 was unique to rural participants. Most significant among the common themes was the description of the social network as a source of OA information, the trial-and-error approach used for OA management, and the individual contextualization of OA management. Our results suggest that there are several common experiences among urban- and rural-dwelling seniors living with OA, including the desire for support over time, but also a unique experience to rural-dwelling seniors, namely lack of access to local care. CONCLUSION: These findings can be used to improve translation of OA information in both urban and rural communities in Canada, highlighting that common strategies may be effective in different contexts for this disease.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".