“We're All Looking for Solutions”: A Qualitative Study of the Management of Knee Symptoms
Bibliographic record
Abstract
OBJECTIVE: While the prevalence of osteoarthritis (OA) increases with age, the first signs begin in the fourth or fifth decade. Little is known about how younger adults respond to OA. This study explores how people ages 35-65 years manage knee symptoms. METHODS: Six focus groups were conducted with 41 participants (mean age 50.9 years, 63% women) who self-reported a diagnosis of OA or reported knee symptoms (i.e., pain, aching, or stiffness) on most days of the past month. Purposive sampling was used, seeking variation in age and sex. The principles of constructivist grounded theory guided data collection and analysis. Data were analyzed using a constant comparative method. RESULTS: Participants engaged in a process of proactively trying to find ways to control knee symptoms and disease progression. Their approach to management was not linear, but rather a process that moved back and forth between searching for "solutions" and active management (ongoing use of strategies). During the process, participants consulted health care providers, but often perceived that medical care offered limited options and guidance. Management was constructed as a "never-ending" process that entailed effort and personal resources. CONCLUSION: Participants were proactive in seeking ways to manage knee OA symptoms. There is a mismatch between participants' proactive approach and the reactive approach of the health care system that has focused on late-stage disease. Programs and supports within the formal and informal health care system are required to enable people to successfully manage knee symptoms across their lifespan.
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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 | 0.034 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.015 | 0.018 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".