Barriers and enablers to health care providers assessment and treatment of knee osteoarthritis in persons with type 2 diabetes mellitus: A qualitative study using the Theoretical Domains Framework
Bibliographic record
Abstract
Objectives: Symptomatic knee osteoarthritis (OA) commonly co-exists in persons with Type 2 diabetes (T2DM) and may impede diabetes self-management. Yet, OA is often underdiagnosed and undertreated due to competing health care demands. We sought to determine healthcare providers' (HCPs') perceptions of the barriers and enablers to assessing and treating knee OA in persons with T2DM. Design: We conducted 18 semi-structured telephone interviews with HCPs who manage persons with T2DM (family physicians, endocrinologists, diabetes educators). Interviews were analyzed deductively using Theoretical Domains Framework (TDF), a framework developed to comprehensively identify behavioural determinants. Within relevant domains, data were thematically analyzed to generate belief statements, and these were compared across the different HCP disciplines. Results: Six TDF domains influenced HCPs behaviour to assess and treat knee OA in persons with T2DM. For all HCPs, important barriers included not seeing assessment/treatment of joint pain as a priority for their patients (intention), and insufficient access to required resources such as physiotherapy to treat OA (environmental context and resources). Endocrinologists and diabetes educators perceived having insufficient knowledge and skills to identify and manage OA (knowledge, skills), did not consider it within their professional role to do so (professional role and identity), and perceived other physicians would not want to receive a referral for OA care (social influences). Conclusions: We identified barriers and enablers encountered by diabetes HCPs to assessing and treating knee OA in persons with T2DM involving multiple domains of the TDF. These will help inform development of a complex intervention to improve health outcomes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".