Improving diagnosis and treatment of knee osteoarthritis in persons with type 2 diabetes: development of a complex intervention
Bibliographic record
Abstract
BACKGROUND: Symptomatic knee osteoarthritis (OA) commonly co-occurs in people with type 2 diabetes (T2DM) and increases the risk for diabetes complications, yet uptake of evidence-based treatment is low. We combined theory, stakeholder involvement and existing evidence to develop a multifaceted intervention to improve OA care in persons with T2DM. This was done in partnership with Arthritis Society Canada to leverage the existing infrastructure and provincial funding for community arthritis care. METHODS: Each step was informed by a User Advisory Panel of stakeholder representatives, including persons with lived experience. First, we identified the target groups and behaviours through consulting stakeholders and current literature. Second, we interviewed persons living with T2DM and knee OA (n = 18), health professionals (HPs) who treat people with T2DM (n = 18) and arthritis therapists (ATs, n = 18) to identify the determinants of seeking and engaging in OA care (patients), assessing and treating OA (HPs) and considering T2DM in OA treatment (ATs), using the Theoretical Domains Framework (TDF). We mapped the content to behavioural change techniques (BCTs) to identify the potential intervention components. Third, we conducted stakeholder meetings to ascertain the acceptability and feasibility of intervention components, including content and modes of delivery. Fourth, we selected intervention components informed by prior steps and constructed a programme theory to inform the implementation of the intervention and its evaluation. RESULTS: We identified the barriers and enablers to target behaviours across a number of TDF domains. All stakeholders identified insufficient access to resources to support OA care in people with T2DM. Core intervention components, incorporating a range of BCTs at the patient, HP and AT level, sought to identify persons with knee OA within T2DM care and refer to Arthritis Society Canada for delivery of evidence-based longitudinal OA management. Diverse stakeholder input throughout development allowed the co-creation of an intervention that appears feasible and acceptable to target users. CONCLUSIONS: We integrated theory, evidence and stakeholder involvement to develop a multifaceted intervention to increase the identification of knee OA in persons with T2DM within diabetes care and improve the uptake and engagement in evidence-based OA management. Our partnership with Arthritis Society Canada supports future spread, scalability and sustainability. We will formally assess the intervention feasibility in a randomized pilot trial.
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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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".