ENGAGING URBAN AND RURAL SENIORS AS RESEARCH PARTNERS TO IMPROVE CHRONIC DISEASE MANAGEMENT
Bibliographic record
Abstract
Osteoarthritis is a chronic disease that affects 50% of people over the age of 65. There is no cure, but there are evidence-based strategies that can reduce the burden of this disease. The challenge lies in conveying and implementing those strategies in the target population, community-dwelling seniors in both urban and rural centers, particularly given the decreased availability of physicians, health services, and community programs in rural centers. In this qualitative study, we directly engage seniors in identifying barriers and facilitators to daily osteoarthritis management. We use hermeneutic phenomenology to explore their lived experiences. Purposeful sampling was used to recruit urban-community-dwelling seniors (N=11) and rural-community-dwelling seniors (N=9) with confirmed osteoarthritis in Ontario, Canada. Interviews were guided using three open-ended questions: 1) Where do you get information related to osteoarthritis? 2) How do you manage your osteoarthritis pain? 3) What can be done to improve osteoarthritis management for seniors? Interviews were transcribed verbatim and coded using NVivo® Pro 11 by two independent researchers. Strikingly, despite the differences between urban and rural centers, highly similar themes relating to osteoarthritis management emerged from the data from both urban- and rural-dwelling seniors. Three central themes were identified: psychological impact, personalized approach, and physician reliance. For rural-dwelling seniors, an additional theme of resource accessibility was identified. This study highlights the importance of capturing stakeholder-identified areas for improvement in chronic disease management.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.015 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".