Evidence-based practice ‘on-the-go’: using ViaTherapy as a tool to enhance clinical decision making in upper limb rehabilitation after stroke, a quality improvement initiative
Bibliographic record
Abstract
Recovery of upper limb function after stroke is currently sub-optimal, despite good quality evidence showing that interventions enabling repetitive practice of task-specific activity are effective in improving function. Therapists need to access and engage with such evidence to optimise outcomes with people with stroke, but this is challenging in fast-paced stroke rehabilitation services. This quality improvement project aimed to investigate acceptability and service impact of a new, international tool for accessing evidence on upper limb rehabilitation after stroke-'ViaTherapy'-in a team of community rehabilitation therapists. Semi-structured interviews were undertaken at baseline to determine confidence in, and barriers to, evidence-based practice (EBP) to support clinical decision making. Reported barriers included time, lack of access to evidence and a research-practice disconnect. The clinicians then integrated use of 'ViaTherapy' into their practice for 4 weeks. Follow-up interviews explored the accessibility of the tool in community rehabilitation practice, and its impact on clinician confidence, treatment planning and provision. Clinicians found the tool, used predominantly in mobile device app format, to be concise and simple to use, providing evidence 'on-the-go'. Confidence in accessing and using EBP grew by 22% from baseline. Clinicans reported changes in intensity of delivery of interventions, as rapid access to recommended doses via the tool was available. Following this work, the participating health and social care service provider changed provision of therapists' technology to enable use of apps. Barriers to use of EBP in stroke rehabilitation persist; the baseline situation here supported the need for more accessible means of integrating best evidence into clinical processes. This quality improvement project successfully integrated ViaTherapy into clinical practice, and found that the tool has potential to underpin positive changes in upper limb therapy service delivery after stroke, by increasing accessibility to, use of and confidence in EBP. Definitive evaluation is now indicated.
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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 | 0.146 | 0.295 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".