Stretching the Scope of Behavioral Interventions: Proceedings of the 4th International Behavioural Trials Network Hybrid Meeting
Bibliographic record
Abstract
Over the last 3 years, the Coronavirus disease 2019 (COVID-19) pandemic has shined a major spotlight on the role of health behaviors in the management of the spread of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus [1, 2]. In spite of the desperate need for innovative and adaptive behavioral interventions to better engage individuals in actions such as getting vaccinated, wearing a mask, physically distancing, etc [2, 3], only 0.007% of worldwide COVID-19 trials were dedicated to behavioral research [4]. Beyond the pandemic, we know that health risk behaviors, such as medication non-adherence, physical inactivity, consumption of a poor quality diet, and smoking, underpin virtually all non-communicable chronic diseases (NCDs) [5, 6]. Though there have been a number of behavioral intervention success stories over the last several decades, there is still limited uptake of health behavior interventions in the community and clinical practice. The mission of the International Behavioural Trials Network (IBTN [7], www.IBTNetwork.org) is to foster global improvement in the quality of behavioral interventions and in trial implementation. This is done through the sharing of existing recommendations, tools, and methodologies on behavioral trials and intervention development. The members of the IBTN met for their 4th international conference, which was held using a hybrid format between May 19 and 21, 2022 in Montreal, Canada. The meeting was attended by more than 230 researchers, clinicians, public health and implementation specialists, trainees, and other end-users from 28 countries, spanning 6 continents, and featured 9 plenary presentations, 3 achievement awards presentations, 6 early career investigator presentations, 7 workshops, given by an outstanding faculty (https://www.ibtnetwork.org/conference/2022-conference-program/), and 42 abstracts (see supplement). Here we summarize the proceedings of the plenary sessions and discuss key challenges that were raised for the field as it moves forward.
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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 | 0.230 | 0.165 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.009 | 0.013 |
| Insufficient payload (model declined to judge) | 0.027 | 0.005 |
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".