Doing What We Know, Knowing What to Do: Californians Linking Action with Science for Prevention of Breast Cancer (CLASP-BC)
Bibliographic record
Abstract
Given the lack of progress in breast cancer prevention, the California Breast Cancer Research Program (CBCRP) plans to apply current scientific knowledge about breast cancer to primary prevention at the population level. This paper describes the first phase of Californians Linking Action with Science for Prevention of Breast Cancer (CLASP-BC). The foci of Phase 1 are building coalitions and coalition capacity building through community engagement in community-based participatory research (CBPR) and dissemination and implementation (D&I) research training. Based on the successful implementation and evaluation of Phase 1, the foci of Phase 2 (presented separately in this special issue) will be to translate the California Breast Cancer Prevention Plan overarching goal and specific intervention goals for 23 breast cancer risk and protective factors strategies into evidence-informed interventions (EIIs) that are disseminated and implemented across California. CLASP-BC is designed to identify, disseminate and implement high-impact, population-based prevention approaches by funding large scale EIIs, through multi-jurisdictional actions, with the intent to decrease the risk of breast cancer and other chronic diseases (sharing common risk factors), particularly among racial/ethnic minorities and medically underserved populations in California.
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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 | 0.036 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.011 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.005 | 0.016 |
| 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; 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".