Development of a theory-informed implementation intervention to improve the triage, treatment and transfer of stroke patients in emergency departments using the Theoretical Domains Framework (TDF): the T3 Trial
Bibliographic record
Abstract
Theoretical frameworks and models based on behaviour change theories are increasingly used in the development of implementation interventions. Development of an implementation intervention is often based on the available evidence base and practical issues, i.e. feasibility and acceptability. The aim of this study was to describe the development of an implementation intervention for the T 3 Trial ( T riage, T reatment and T ransfer of patients with stroke in emergency departments (EDs)) using theory to recommend behaviour change techniques (BCTs) and drawing on the research evidence base and practical issues of feasibility and acceptability. A stepped method for developing complex interventions based on theory, evidence and practical issues was adapted using the following steps: (1) Who needs to do what, differently? (2) Using a theoretical framework, which barriers and enablers need to be addressed? (3) Which intervention components (behaviour change techniques and mode(s) of delivery) could overcome the modifiable barriers and enhance the enablers? A researcher panel was convened to review the list of BCTs recommended for use and to identify the most feasible and acceptable techniques to adopt. Seventy-six barriers were reported by hospital staff who attended the workshops (step 1: thirteen TDF domains likely to influence the implementation of the T 3 Trial clinical intervention were identified by the researchers; step 2: the researcher panellists then selected one third of the BCTs recommended for use as appropriate for the clinical context of the ED and, using the enabler workshop data, devised enabling strategies for each of the selected BCTs; and step 3: the final implementation intervention consisted of 27 BCTs). The TDF was successfully applied in all steps of developing an implementation intervention for the T 3 Trial clinical intervention. The use of researcher panel opinion was an essential part of the BCT selection process to incorporate both research evidence and expert judgment. It is recommended that this stepped approach (theory, evidence and practical issues of feasibility and acceptability) is used to develop highly reportable implementation interventions. The classifying of BCTs using recognised implementation intervention components will facilitate generalisability and sharing across different conditions and clinical settings.
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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 | 0.059 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".