Exploring what influences physiotherapists’ capability, opportunity and motivation to integrate new evidence into routine clinical care using the Balance Intensity Scale
Bibliographic record
Abstract
Purpose To explore influences on the capability, opportunity and motivation of physiotherapists integrating new evidence into routine care.Materials and Methods Mixed-methods study utilising the Theoretical Domains Framework and Capability-Opportunity-Motivation-Behaviour model. Metropolitan inpatient rehabilitation physiotherapists participated by integrating the Balance Intensity Scale into routine care for 6 weeks. Evidence integration was supported by a tailored theory-informed approach. Participants completed pre- and post-evidence integration surveys and a post-evidence integration focus group.Results Pre- and post-surveys were completed by 24 and 12 participants, respectively. One focus group (n = 7) was conducted. Framework analysis identified themes in Capability (n = 4), Opportunity (n = 4) and Motivation (n = 5) domains influencing behaviour when implementing new evidence. The evidence integration process enhanced participants’ Knowledge (p = 0.04), Skills (p = 0.003) and Belief in capabilities (p = 0.03) when prescribing and measuring balance exercises.Conclusions This study identified perceived barriers and enablers to evidence integration of a new outcome measure into routine care. It highlights strategies that may support physiotherapy teams in incorporating new evidence into routine care. These strategies include education on the evidence being implemented, physical resources, change champions to facilitate social support, management endorsement, and recognition of the time and effort required for evidence integration in the short term.
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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 | 0.011 | 0.052 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".