Development and evaluation of a behaviour change intervention to increase general practitioner REferral of people with hip and knee osteoarthritis to community-based First-linE caRe (REFER): a mixed methods study
Bibliographic record
Abstract
BACKGROUND: Globally osteoarthritis is a leading cause of pain and disability. General practitioners (GPs) have a critical role in the management of osteoarthritis in primary care, yet they also face numerous barriers to referral of people with osteoarthritis to osteoarthritis management programs that provide evidence based first line care (exercise, education, and weight management.) Thus, the aim of this project was to co-develop and evaluate the feasibility of a multi-faceted, theory-based behaviour change intervention to increase GP REferral of people with hip and knee osteoarthritis to community-based First-linE caRe (REFER). METHODS: This project involved a mixed-methods modified exploratory sequential design. Registered GPs or GP registrars with a case load including patients with hip or knee osteoarthritis were recruited in Victoria, Australia. Phase 1: REFER was initially designed by mapping GP-specific referral barriers to the behaviour change wheel. Registered GPs or GP registrars engaged in online, one-on-one semi-structured interviews to explore their learning preferences and refine REFER. Interviews were recorded, transcribed verbatim, and managed in NVIVO. Analyses involved an inductive, thematic approach. Phase 2: REFER was evaluated with a sample of GPs using the Reach, Effectiveness, Adoption, Implementation, and Maintenance Qualitative Evaluation for Systematic Translation (RE-AIM QuEST) framework. RESULTS: Phase 1: 25 GP interviews identified diverse learning preferences and barriers, including time, cost, and lack of enticing opportunities. Learning facilitators included quick and easily accessible options and earning professional development points. Almost all GPs agreed on including an online, interdisciplinary workshop with additional components (electronic medical record template, web-based toolkit, posters and flyers, booster follow-up session). Phase 2: 27 GPs participated in REFER, with 13 engaging in process evaluation interviews. REFER had high acceptability among GPs who participated and was associated with improved knowledge and confidence in OA guidelines and referral options, with a sub-set of GPs self-reporting increased referral behaviours to community-based osteoarthritis care. CONCLUSIONS: Improved GP knowledge and confidence in guidelines and referral options, alongside increased self-reported referral to first-line care indicates REFER has the potential to improve community-based osteoarthritis management. However, prior to scale-up, work is needed to improve reach and engagement with GPs, and to further refine the 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 | 0.035 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| 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".