Translating Cancer Control Research Into Primary Care Practice: A Conceptual Framework
Bibliographic record
Abstract
Effective dissemination, implementation, and adoption of research-tested lifestyle risk factor interventions within primary care are critical to reduce cancer morbidity and mortality. The objective of this study is to identify short- and long-term action steps within primary care research and practice to bridge the discovery-to-delivery gap in cancer prevention and control. Experts in primary care research and practice from the United States and Canada participated in this qualitative project. Concept mapping was used to synthesize expert input on actions to improve research-practice integration in cancer prevention and control. Results were used to facilitate an action-planning meeting among primary care researchers and practitioners. Five areas were identified as critical to improving the integration of research and practice in cancer prevention and control: (1) stakeholder collaborations, (2) organizational culture and structure, (3) learning infrastructure, (4) incentives and funding, and (5) data and accountability systems. Addressing the discovery-to-delivery gap in primary care requires collaboration among researchers and practitioners throughout the knowledge production cycle. The model developed in this project can be used to stimulate actions at the individual, organizational, and systems level to reduce the burden of cancer related to lifestyle risk factors.
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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 | 0.099 | 0.069 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.013 | 0.010 |
| Science and technology studies | 0.008 | 0.052 |
| Scholarly communication | 0.022 | 0.017 |
| Open science | 0.007 | 0.013 |
| Research integrity | 0.010 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".