Patient beliefs about who and what influences their hip and knee osteoarthritis symptoms and progression
Bibliographic record
Abstract
BACKGROUND: Osteoarthritis management aims to reduce pain and improve function. Many factors affect whether patients follow recommended strategies. Locus of control refers to individual beliefs around who and what influences health. Locus of control is related to the treatment strategies patients prefer. Currently, no studies explore locus of control in non-surgical management of osteoarthritis. OBJECTIVES: To explore patients' beliefs about the influences on their osteoarthritis symptoms and disease progression. METHODS: Semi-structured interviews were conducted with individuals experiencing self-reported hip and/or knee osteoarthritis who had at least one joint that had not undergone replacement surgery. We used a qualitative description approach and the Braun and Clarke method for thematic analysis. Participants' locus of control classifications-internal, chance, doctors, or other people-were based on the Multidimensional Health Locus of Control (MHLC) Scales Form C score. RESULTS: Locus of control was discussed in relation to aetiology, progression, and symptoms. Participants' opinions varied on whether their osteoarthritis progression could be influenced. 46% of participants attributed control to other people. Most participants believed that a previous injury had caused their osteoarthritis and that both themselves and others had some influence over their osteoarthritis symptoms, regardless of their locus of control classification. CONCLUSION: This research highlights the need for education about: the aetiology of osteoarthritis, the link between management and progression, and patient management of osteoarthritis. Further research is required to discern why expected patterns were not observed between participants' beliefs and locus of control classifications.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".