Co-design of the web-based ‘My Knee’ education and self-management toolkit for people with knee osteoarthritis
Bibliographic record
Abstract
Objective Describe the co-design process and learnings related to developing the web-based Translating Research Evidence and Knowledge (TREK) ‘My Knee’ education and self-management toolkit for people with knee osteoarthritis. Co-design process Stage (i): Understand and define; systematically reviewed education interventions in published trials; appraised web-based information about knee osteoarthritis; and used concept mapping to identify education priorities of people with knee osteoarthritis and physiotherapists. Stage (ii): Prototype; created a theory-, guideline- and evidence-informed toolkit. Stage (iii): Test and iterate; completed three co-design workshops with end-users (i.e., people with knee osteoarthritis and health professionals); plus an expert review. Results The toolkit is available at myknee.trekeducation.org. Stage (i) identified the need for more accurate and co-designed resources to address broad education needs generated during concept mapping, including guidance on surgery, dispelling common misconceptions and facilitating engagement with exercise therapy and weight management. A theory- and research-informed prototype was created in Stage (ii) to address broad learning and education needs. Stage (iii) co-design workshops ( n = 15 people with osteoarthritis and n = 9 health professionals) informed further content creation and refinement, alongside improvements to optimise usability. Expert opinion review ( n = 8) further refined accuracy and usability. Conclusions The novel co-design methodology employed to create the TREK ‘My Knee’ toolkit facilitated the alignment of the content and usability to meet the broad education needs of people with knee osteoarthritis and health professionals. This toolkit aims to improve and facilitate engagement with guideline-recommended first-line care for people with knee osteoarthritis. Future work will determine its effectiveness in improving clinical outcomes in this population.
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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 | 0.057 | 0.107 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".