Research, Practice, and Policy Partnerships in Pan-Canadian Coalitions for Cancer and Chronic Disease Prevention
Bibliographic record
Abstract
OBJECTIVE: To describe the development stages of the Coalitions Linking Action and Science for Prevention (CLASP) initiative of the Canadian Partnership Against Cancer to support research, practice, and policy coalitions focused on cancer and chronic disease prevention in Canada. DESIGN: Coalitions Linking Action and Science for Prevention was implemented in 3 stages. This article describes Stage 1 that consisted of an online concept-mapping consultation process, 3 topic specific networking and consultation workshops, and 3 context-specific networking, coalition development, and planning meetings. These were all completed using a participatory engagement approach to encourage knowledge exchange across jurisdictions and sectors in Canada. SETTING: Toronto, Ontario; Calgary, Alberta; Montreal, Québec; and Ottawa, Ontario. PARTICIPANTS: More than 500 researchers, practitioners, and policy specialists were invited to take part in the first stage activities. MAIN OUTCOME MEASURES: (1) Participant-identified high-priority opportunities for strategic collaboration; (2) Cross-jurisdictional and cross-sector representation; and (3) Participant feedback on the CLASP processes and activities. RESULTS: Participants from Stage 1 activities were distributed across all provinces/territories; 3 jurisdictional levels; and research, practice, and policy sectors. Ninety priority opportunities for strategic collaboration were identified across all 3 workshops. Participants provided detailed feedback about transparency of the RFP (Request for Proposals) application process, support needed to level the playing field for potential applicants, and valuable suggestions for the adjudication process. CONCLUSIONS: Coalitions Linking Action and Science for Prevention engaged hundreds of research, practice, and policy experts across Canada focusing social-behavioral, clinical, and environmental and occupational opportunities for cancer and chronic disease prevention. Given the extent of expert and jurisdictional engagement, the substantial Partnership investment in a participatory engagement approach to RFP development and potential applicant response suggests that efforts to link cancer and chronic disease prevention efforts across jurisdictions and through research, practice, and policy collaboration may require this type of a priori investment in networking, communication, coordination, and collaboration.
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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 | 0.039 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".