Personal experience of living with knee osteoarthritis among older adults
Bibliographic record
Abstract
PURPOSE: Knee osteoarthritis (OA) is the most common cause of chronic disability amongst community-dwelling older adults. Yet, little is understood about the daily experience of knee OA. As clinicians we fail to understand a large group of individuals that we aim to help. We conducted an exploratory study that aimed to understand the experience of living with knee OA in older adults. METHOD: We used a descriptive phenomenology, grounded in the phenomenology in practice tradition. We conducted nine interviews with participants with physician-diagnosed knee OA, of different ages, sexes, cultural backgrounds and self-perceptions. Ninety-minute interviews with each participant were audio-taped and transcribed verbatim. We used the vanKaam method of phenomenological analysis, modified by Moustakas, as the framework for data analysis. FINDINGS: The following five themes on living with knee OA emerged: experiencing knee pain is central to daily living, experiencing mobility limitations devalues self-worth, sharing the experience, assessing our own health and managing chronic pain. CONCLUSIONS: The implications of these findings highlight the profound impact knee OA has on daily living, which have been poorly documented in the past. Clinicians should consider that the consequences of living with knee OA are significant enough to influence a person's sense of self-worth.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".