Behavioral interventions—past, present, and future: Proceedings of the 5th International Behavioural Trials Network International Hybrid Meeting
Bibliographic record
Abstract
Behavioral medicine is at a crucial juncture. The Coronavirus disease 2019 (COVID-19) pandemic revealed the critical public health role of behaviors in the spread and impact of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus,1,2 and there is a growing recognition that behavioral science will be fundamental in the ongoing climate crisis.3 Furthermore, there is a sense that the methods and frameworks4,5 around how our interventions are developed and tested have matured enough to enable our field to start having widespread, long-term, positive impacts. This has been recognized internationally, through both the World Health Organization’s Behavioural Sciences for Better Health Initiative6,7 and the United Nations, where behavioral science is 1 of the 5 core cutting-edge skills identified in its quintet of change initiatives.8 However, in spite of the current wave of optimism, there are still few examples of health behavior change interventions being consistently implemented in systems, communities, or clinical practices.
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.
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 | 0.131 | 0.067 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.007 | 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".