A vision for chronic disease prevention and intervention research: report from a workshop
Bibliographic record
Abstract
The Population Studies Research Network of Cancer Care Ontario hosted a strategic planning workshop to establish an agenda for a prevention intervention research program in Ontario, including priority topics for investigation and design considerations. The two-day workshop included: presentations on background papers developed to facilitate participants' preparation for and discussions in the workshop; keynote presentations on intervention research concerning primary prevention of chronic diseases, design and study implementation considerations; a dedicated session on critical and creative thinking to stimulate participation and discussion topics; breakout groups to identify, discuss and present study ideas, designs, implementation considerations; and a consensus process to discuss and identify recommendations for research priorities and next steps. The retreat yielded the following recommendations: 1) develop an intervention research agenda that includes working with existing large-scale cohorts; 2) develop an intervention research agenda that includes novel research designs that could target individuals or groups; and 3) develop an intervention research agenda in which studies collect data on costs, define stakeholders, and ensure clear strategies for stakeholder engagement and knowledge transfer. The Population Studies Research Network will develop options from these recommendations and release a call for proposals in 2014 for intervention research pilot projects that reflect these recommendations. Pilot projects will be evaluated based on their fit with the retreat's recommendations, and their potential to scale up to full studies and application in practice.
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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 | 0.105 | 0.050 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.006 |
| Scholarly communication | 0.020 | 0.010 |
| Open science | 0.007 | 0.030 |
| Research integrity | 0.015 | 0.023 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".