An international, Delphi consensus study to identify priorities for methodological research in behavioral trials in health research
Bibliographic record
Abstract
BACKGROUND: Non-communicable chronic diseases are linked to behavioral risk factors (including smoking, poor diet and physical inactivity), so effective behavior change interventions are needed to improve population health. However, uptake and impact of these interventions is limited by methodological challenges. We aimed to identify and achieve consensus on priorities for methodological research in behavioral trials in health research among an international behavioral science community. METHODS: An international, Delphi consensus study was conducted. Fifteen core members of the International Behavioral Trials Network (IBTN) were invited to generate methodological items that they consider important. From these, the research team agreed a "long-list" of unique items. Two online surveys were administered to IBTN members (N = 306). Respondents rated the importance of items on a 9-point scale, and ranked their "top-five" priorities. In the second survey, respondents received feedback on others' responses, before rerating items and re-selecting their top five. RESULTS: Nine experts generated 144 items, which were condensed to a long-list of 33 items. The four most highly endorsed items, in both surveys 1 (n = 77) and 2 (n = 57), came from two thematic categories:"Intervention development" ("Specifying intervention components" and "Tailoring interventions to specific populations and contexts") and "Implementation" ("How to disseminate behavioral trial research findings to increase implementation" and "Methods for ensuring that behavioral interventions are implementable into practice and policy"). "Development of novel research designs to test behavioral interventions" also emerged as a highly ranked research priority. CONCLUSIONS: From a wide array of identified methodological issues, intervention development, implementation and novel research designs are key themes to drive the future behavioral trials' research agenda. Funding bodies should prioritize these issues in resource allocation.
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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 | 0.599 | 0.542 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.010 | 0.009 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.005 | 0.023 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 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; the direct Gemma label and the distilled Codex classifier 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".