Formulating Knee Osteoarthritis Management Plans Taking Type 2 Diabetes Into Account: Qualitative Study of Arthritis Therapists Using Theoretical Domains Framework
Bibliographic record
Abstract
OBJECTIVE: Delivering person-centered care in individuals with knee osteoarthritis (OA) necessitates consideration of other chronic conditions that frequently co-occur. We sought to understand the extent to which arthritis therapists consider type 2 diabetes mellitus (T2DM) when treating persons with knee OA and concomitant T2DM, and barriers to doing so. METHODS: We conducted 18 semistructured telephone interviews with arthritis therapists working within a provincially funded arthritis care program (Arthritis Society Canada) in Ontario, Canada. We first analyzed interviews deductively using the Theoretical Domains Framework (TDF) to comprehensively identify barriers and enablers to health behaviors. Then, within TDF domains, we inductively developed themes. RESULTS: We identified 5 TDF domains as prominently influencing the behavior of arthritis therapists considering concomitant T2DM when developing a knee OA management plan. These were as follows: therapists' perceived lack of specific knowledge around comorbidities including diabetes; the lack of breadth in skills in behavioral change techniques to help patients set and reach their goals, particularly when it came to physical activity; variable intention to factor a patient's comorbidity profile to influence their treatment recommendations; the perception of their professional role and identity as joint focused; and the environmental context with lack of formalized follow-up structure of the current Arthritis Society Canada program that limited sufficient patient monitoring and follow-up. CONCLUSION: Within the context of a Canadian arthritis program, we identified several barriers to arthritis therapists considering T2DM in their management plan for persons with knee OA and T2DM. These results can help inform strategies to improve person-centered OA care and overall health outcomes.
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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 | 0.014 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.007 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".