Patients' Perceptions of Navigating “The System” for Arthritis Management: Are They Able to Follow Our Recommendations?
Bibliographic record
Abstract
PURPOSE: To understand whether a visit to a Hip/Knee Arthritis Assessment Centre (AC), where non-surgical candidates with arthritis are directed toward community resources and provided with a conservative treatment "prescription," contributes to patients' self-management and ability to access community resources. METHODS: A purposive sample of non-surgical patients was contacted 3-10 months after their AC visit. Three focus groups (n=20) and 20 semi-structured telephone interviews were conducted. Transcripts were systematically coded and analyzed using a qualitative descriptive research methodology. RESULTS: While participants generally reported that the AC visit improved self-management, analysis identified an emergent theme about the inadequacy of conservative management in general, subdivided into two sub-themes related to (1) limited access to high-quality, non-surgical treatment, such as physiotherapy and (2) health care providers' attitudes and approaches, which do not embrace chronic disease prevention and management. CONCLUSIONS: An AC visit contributes to arthritis self-management; however, the current health care system does not adequately support conservative treatment of chronic conditions. Treatment guidelines need to be tailored to the local health care context in which they are applied.
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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 | 0.005 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".