Supporting the implementation of Osteoarthritis Management Programs in low-and middle-income settings: Understanding clinician training outcomes alongside perceived program barriers and facilitators in Malawi
Bibliographic record
Abstract
ABSTRACT Objective To examine Malawian physiotherapists’ knowledge and beliefs about Osteoarthritis (OA) and their perceived capabilities to deliver an Osteoarthritis Management Program (OMP) to people with knee and hip OA; and to identify OMP barriers and facilitators in Malawi. Design Two-phased mixed methods formative evaluation Methods Phase 1: Malawian physiotherapists participated in the GLA:D ® Australia training course and answered quantitative pre-and-post course questions that were descriptively summarized, and analysed using McNemar’s test, where appropriate. Phase 2: semi-structured focus groups generated qualitative data that were thematically analysed and mapped to the Consolidated Framework for Implementation Research. Mixed methods data were integrated through triangulation. Results Eleven Malawian physiotherapists [9 (82%) female, 10 (91%) with 5–10 years clinical experience] participated. Post training course, participants’ knowledge of OA management increased regarding the benefits of therapeutic exercise (p=0.002), importance of weight management (p=0.004), and acceptable symptoms profile (p=0.008). Participants’ confidence and beliefs in managing knee and hip OA also increased. Implementation barriers included program costs, current medical management of OA with painkillers, and infrastructure challenges. Implementation facilitators included the content and organisation of GLA:D ® , the ability to adapt the program, and OA awareness and education among other health professionals. Conclusion Knowledge, confidence, and beliefs in managing knee and hip OA improved post GLA:D ® training in Malawian physiotherapists. Increasing education of physiotherapists, other health professionals and the general public about evidence-based OA management and making contextual adaptions to the GLA:D ® training and program structure may facilitate future implementation of OMP, such as GLA:D ® , in low-and-middle income countries.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 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".