Mechanisms of action of an implementation intervention in stroke rehabilitation: a qualitative interview study
Bibliographic record
Abstract
BACKGROUND: Despite best evidence demonstrating the effectiveness of increased intensity of exercise after stroke, current levels of therapy continue to be below those required to optimise motor recovery. We developed and tested an implementation intervention that aims to increase arm exercise in stroke rehabilitation. The aim of this study was to illustrate the use of a behaviour change framework, the Behaviour Change Wheel, to identify the mechanisms of action that explain how the intervention produced change. METHODS: We implemented the intervention at three stroke rehabilitation units in the United Kingdom. A purposive sample of therapy team members were recruited to participate in semi-structured interviews to explore their perceptions of how the intervention produced change at their work place. Audio recordings were transcribed and imported into NVivo 10 for content analysis. Two coders separately analysed the transcripts and coded emergent mechanisms. Mechanisms were categorised using the Theoretical Domains Framework (TDF) (an extension of the Capability, Opportunity, Motivation and Behaviour model (COM-B) at the hub of the Behaviour Change Wheel). RESULTS: We identified five main mechanisms of action: 'social/professional role and identity', 'intentions', 'reinforcement', 'behavioural regulation' and 'beliefs about consequences'. At the outset, participants viewed the research team as an external influence for whom they endeavoured to complete the study activities. The study design, with a focus on implementation in real world settings, influenced participants' intentions to implement the intervention components. Monthly meetings between the research and therapy teams were central to the intervention and acted as prompt or reminder to sustain implementation. The phased approach to introducing and implementing intervention components influenced participants' beliefs about the feasibility of implementation. CONCLUSIONS: The Behaviour Change Wheel, and in particular the Theoretical Domains Framework, were used to investigate mechanisms of action of an implementation intervention. This approach allowed for consideration of a range of possible mechanisms, and allowed us to categorise these mechanisms using an established behaviour change framework. Identification of the mechanisms of action, following testing of the intervention in a number of settings, has resulted in a refined and more robust intervention programme theory for future testing.
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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 | 0.047 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.013 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| 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".