Involving clinicians in sports medicine and physiotherapy research: ‘design thinking’ to help bridge gaps between practice and evidence
Bibliographic record
Abstract
Bridging the gap between current practice and evidence is not easy in sports medicine. Research findings must be relevant to real-world clinical practice and effectively reach practitioners, who then must understand, interpret and apply them to their patients. Any change in practice requires clinically meaningful research to spark interest among clinicians. The ‘Ikea effect’—where novice builders value their own creations as highly as the work of experts1—provides some insight into a strategy which will help drive clinical research uptake. Specifically, engaging clinicians in the scientific process is key to ensuring effective translation of research to patient care. Sports medicine research needs a ‘design thinking’ approach that prioritises the needs of the end user.2 Design thinking would have clinicians guiding researchers in designing research questions based on experience and interaction with patients to solve practice-generated problems.3 As an example, patients are unlikely to engage in a treatment plan if their beliefs and expectations are not considered in the decision-making process4; increasingly funding organisations require input from patient partners in grant applications. Similarly, clinicians are unlikely to implement, or even read, research that they find irrelevant to their practice, yet it is rare for funding bodies to require clinician partners in grant applications. However, research teams should strongly value input from clinician partners from research conceptualisation …
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.023 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".