The special sauce of the Cancer Prevention and Control Research Network: 20 years of lessons learned in developing the evidence base, building community capacity, and translating research into practice
Bibliographic record
Abstract
PURPOSE: The Cancer Prevention and Control Research Network (CPCRN) is a national network focused on accelerating the translation of cancer prevention and control research evidence into practice through collaborative, multicenter projects in partnership with diverse communities. From 2003 to 2022, the CPCRN included 613 members. METHODS: We: (1) characterize the extent and nature of collaborations through a bibliometric analysis of 20 years of Network publications; and (2) describe key features and functions of the CPCRN as related to organizational structure, productivity, impact, and focus on health equity, partnership development, and capacity building through analysis of 22 in-depth interviews and review of Network documentation. RESULTS: Searching Scopus for multicenter publications among the CPCRN members from their time of Network engagement yielded 1,074 collaborative publications involving two or more members. Both the overall number and content breadth of multicenter publications increased over time as the Network matured. Since 2004, members submitted 123 multicenter grant applications, of which 72 were funded (59%), totaling more than $77 million secured. Thematic analysis of interviews revealed that the CPCRN's success-in terms of publication and grant productivity, as well as the breadth and depth of partnerships, subject matter expertise, and content area foci-is attributable to: (1) its people-the inclusion of members representing diverse content-area interests, multidisciplinary perspectives, and geographic contexts; (2) dedicated centralized structures and processes to enable and evaluate collaboration; and (3) focused attention to strategically adapting to change. CONCLUSION: CPCRN's history highlights organizational, strategic, and practical lessons learned over two decades to optimize Network collaboration for enhanced collective impact in cancer prevention and control. These insights may be useful to others seeking to leverage collaborative networks to address public health problems.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.053 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads 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".