Barriers and facilitators to orthopaedic surgeons’ uptake of decision aids for total knee arthroplasty: a qualitative study
Bibliographic record
Abstract
OBJECTIVES: The demand for total knee arthroplasty (TKA) is increasing. Differentiating who will derive a clinically meaningful improvement from TKA from others is a key challenge for orthopaedic surgeons. Decision aids can help surgeons select appropriate candidates for surgery, but their uptake has been low. The aim of this study was to explore the barriers and facilitators to decision aid uptake among orthopaedic surgeons. DESIGN: A qualitative study involving face-to-face interviews. Questions were constructed on the Theoretical Domains Framework to systematically explore barriers and facilitators. SETTING: One tertiary hospital in Australia. PARTICIPANTS: Twenty orthopaedic surgeons performing TKA. OUTCOME MEASURES: Beliefs underlying similar interview responses were identified and grouped together as themes describing relevant barriers and facilitators to uptake of decision aids. RESULTS: While prioritising their clinical acumen, surgeons believed a decision aid could enhance communication and patient informed consent. Barriers identified included the perception that one's patient outcomes were already optimal; a perceived lack of non-operative alternatives for the management of end-stage osteoarthritis, concerns about mandatory cut-offs for patient-centred care and concerns about the medicolegal implications of using a decision aid. CONCLUSIONS: Multifaceted implementation interventions are required to ensure that orthopaedic surgeons are ready, willing and able to use a TKA decision aid. Audit/feedback to address current decision-making biases such as overconfidence may enhance readiness to uptake. Policy changes and/or incentives may enhance willingness to uptake. Finally, the design/implementation of effective non-operative treatments may enhance ability to uptake by ensuring that surgeons have the resources they need to carry out decisions.
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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 | 0.023 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 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".