Understanding the behavioural determinants of seeking and engaging in care for 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) frequently co-occurs in individuals with type 2 diabetes mellitus (T2DM). In the context of T2DM, OA is often underdiagnosed and undertreated. To elucidate strategies to improve OA care in persons with T2DM, we assessed their perceptions of the barriers and enablers to seeking and engaging in OA care. Design: We conducted semi-structured interviews with 18 individuals with T2DM and symptomatic knee OA in Ontario, Canada. Transcripts were deductively coded using the Theoretical Domains Framework (TDF), an implementation science framework that incorporates theoretical domains of behaviour determinants, which can be linked to behaviour change techniques. Within each of the relevant domains, data were thematically analyzed to generate belief statements. Results: Seven of the TDF domains prominently influenced the behaviour to seek and engage in OA care. Participants described insufficient receipt of OA knowledge to fully engage in care (knowledge), feeling incapable of participating in physical activity due to joint pain (beliefs about capabilities), uncertainty about effectiveness of therapies (optimism) and lack of guidance from health care providers and insufficient access to community programs/supports (environmental context and resources). Key enablers were strong social support (social influences), sources of accountability (behavioural regulation) and experiencing benefit from treatment (reinforcement). Participants did not see concomitant T2DM as limiting the desire to seek OA care. Conclusions: Among individuals with symptomatic knee OA and T2DM, we identified behavioural determinants of seeking and engaging in OA care. These will be mapped to behavioural change techniques to inform development of a complex intervention.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".