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Record W2809983266 · doi:10.1186/s40814-018-0314-4

Mobilising knowledge between practitioners and researchers to iteratively refine a complex intervention (DAFNEplus) pre-trial: protocol for a structured, collaborative working group process

2018· article· en· W2809983266 on OpenAlex
Jenna Breckenridge, Carla Gianfrancesco, Nicole de Zoysa, Julia Lawton, David Rankin, Elizabeth Coates

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenuePilot and Feasibility Studies · 2018
Typearticle
Languageen
FieldHealth Professions
TopicHealth Policy Implementation Science
Canadian institutionsCentre for Global Health Research
FundersUniversity of SheffieldKing's College LondonNational Institute for Health and Care Research
KeywordsIntervention (counseling)Randomized controlled trialMedical educationPsychological interventionProtocol (science)Qualitative researchProcess (computing)MedicinePsychologyNursingComputer scienceAlternative medicineSociology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.008
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.493
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.899
GPT teacher head0.753
Teacher spread0.146 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it