Behavioral Medicine Research Council (BMRC) Statement Papers: A New Approach to Consensus Building in Behavioral Medicine Science
Bibliographic record
Abstract
In his departing remarks, outgoing NIH Director Dr. Francis Collins lamented that he should have engaged more with behavioral science to develop COVID-19 mitigation strategies [1]. Behavioral medicine can contribute unique value to public health efforts to achieve optimal health for all. Yet, we continue to see gaps in the dissemination of behavioral medicine research and gaps in the effective translation of science into public health practice and policy. The Behavioral Medicine Research Council (BMRC) addresses these gaps by building a strong collaborative foundation for behavioral medicine. The BMRC is an autonomous, joint committee made up of representatives from the four leading U.S.-based behavioral medicine organizations: the Academy of Behavioral Medicine Research (ABMR); the American Psychosomatic Society (APS); the Society of Behavioral Medicine (SBM); and the Society for Health Psychology (SfHP) [2]. The goal of the BMRC is to identify strategic behavioral medicine research goals and promote systematic, interdisciplinary approaches to achieve them. The efforts of the BMRC include but are not limited to identifying specific research targets, prescribing best practices, cultivating a field-level culture, and building coalitions and joint advocacy to improve the influence of behavioral science in the broader research community, and the public, and policymakers. The BMRC comprises two distinguished senior scientists to represent each of the member societies for 3-year terms. In addition, the editors-in-chief of the three major journals—Annals of Behavioral Medicine, Health Psychology, and Psychosomatic Medicine—also engage in coordinating and facilitating dissemination. This coalition represents a new effort to harness the field’s collective strength and lead toward a more remarkable impact on population health assertively.
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 | 0.757 | 0.852 |
| Meta-epidemiology (narrow) | 0.004 | 0.006 |
| Meta-epidemiology (broad) | 0.013 | 0.012 |
| Bibliometrics | 0.037 | 0.033 |
| Science and technology studies | 0.024 | 0.040 |
| Scholarly communication | 0.061 | 0.042 |
| Open science | 0.023 | 0.045 |
| Research integrity | 0.049 | 0.055 |
| Insufficient payload (model declined to judge) | 0.036 | 0.018 |
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".