An international, Delphi consensus study to identify priorities for methodological research in behavioural trials: A study protocol
Bibliographic record
Abstract
<ns4:p> <ns4:bold>Background:</ns4:bold> Effective behaviour change interventions are needed to impact important health outcomes, including morbidity and mortality. However, the uptake and impact of behavioural interventions have been limited by methodological challenges. The International Behavioural Trials Network (IBTN) was established in 2013 to facilitate global improvement in methodological quality of behavioural trials. There has been no formal process, within the network or in the broader literature, to define the most important research priorities to achieve this aim. In this project, we will conduct an international, Delphi consensus study to identify and achieve consensus on priorities for methodological research in behavioural trials among IBTN members. </ns4:p> <ns4:p> <ns4:bold>Methods:</ns4:bold> Fifteen core members of IBTN, who are experts in the field of behavioural intervention research, will be invited to brainstorm a complete list of all items they consider priority areas for methodological research in trials of behavioural interventions. The IBTN Research Prioritisation team (the authors) will review all items generated, removing duplicates and merging similar topics, and generate a ‘long-list’ of items. This long-list will be sent to the 15 IBTN core members for approval. </ns4:p> <ns4:p>We will then administer two online Delphi surveys to all IBTN members. In the first survey, respondents will be asked to rate the importance of each item on a nine-point scale and rank their top five priorities. In the second survey, respondents will receive feedback on others’ responses and a reminder of their own responses in survey 1, and will be asked to re-rate items and re-select their ‘top five’.</ns4:p> <ns4:p> <ns4:bold>Discussion:</ns4:bold> Findings from the project will be used to develop the research agenda of the IBTN and to make recommendations for future research. </ns4:p>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.510 | 0.097 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.001 |
| Open science | 0.017 | 0.021 |
| Research integrity | 0.001 | 0.006 |
| 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; both teacher heads agree on what is shown here.
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".