Mobilising knowledge between practitioners and researchers to iteratively refine a complex intervention (DAFNEplus) pre-trial: protocol for a structured, collaborative working group process
Bibliographic record
Abstract
BACKGROUND: Randomised controlled trials (RCTs) of complex interventions often begin with a pilot phase to test the proposed methods and refine the intervention before it is trialled. Although the Medical Research Council (MRC) recommends regular communication between the practitioners delivering the intervention and the researchers evaluating it during the pilot phase, there is a lack of practical guidance about how to undertake this aspect of pre-trial work. This paper describes a novel structured process for collaborative working, which we developed to iteratively refine a complex intervention prior to an RCT. We also describe an in-built qualitative study to learn lessons about how this approach could be used by future study teams. METHODS: . The intervention is being piloted in three National Health Service (NHS) diabetes centres in two waves, with refinements being incrementally implemented between each wave in response to real-time, collective learning (combining practitioner experience, process evaluation data and patient and public involvement via an advisory group). A structured 'Collaborative Working Group' (CWG) process, comprising monthly teleconferences and four strategically timed face-to-face meetings, is being used to identify and respond systematically to emerging implementation challenges and research findings. The group involves 25 members of the study team, including the multi-disciplinary practitioners delivering the intervention, the research teams conducting the process evaluation, the study manager and Chief Investigator. An in-built qualitative study comprising documentary analysis of meeting materials, discourse analysis of meeting transcripts, reflexive note taking, and thematic analysis of focus groups and interviews with CWG members is being undertaken to explore how the CWG works and how its processes and procedures might be improved. DISCUSSION: The CWG process offers a potential model for collaborative working in future pre-trial pilot phases and intervention development studies that operationalises MRC guidance to progressively develop a complex intervention and foster shared ownership through genuine collaboration. The findings from the qualitative study will provide insight into how to best support collaborative working to achieve optimal intervention design.
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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 | 0.177 | 0.182 |
| Meta-epidemiology (narrow) | 0.006 | 0.005 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.007 | 0.007 |
| Research integrity | 0.011 | 0.014 |
| Insufficient payload (model declined to judge) | 0.064 | 0.024 |
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".