Accelerating Knowledge to Action: The Pan-Canadian Cancer Control Strategy
Bibliographic record
Abstract
BACKGROUND: In 2006, the federal government committed funding of $250 million over 5 years for the Canadian Partnership Against Cancer Corporation to begin implementation of the Canadian Strategy for Cancer Control (CSCC). The Partnership was established as a not-for-profit corporation designed to work actively with a broad range of stakeholders and organizations that had been engaged in the development of the CSCC and with the public more broadly. A policy experiment unto itself, the Partnership was the first disease-based organization funded at the federal level outside of government. It was charged with a mandate to enable transfer of knowledge and to catalyze coordinated and accelerated action across the country to reduce the burden of cancer. IMPLEMENTATION: Implementation has involved establishing shared goals, objectives, and plans with participating partners. Knowledge management-incorporating pan-Canadian approaches to the identification of content, processes, technology, and culture change-was used to enable that work across the federated health care delivery system. Evaluation of the organization through independent review, the ability to achieve initiative-level targets by 2012, and progress measured using indicators of system performance was used to examine the effectiveness of the strategy and approach overall. DISCUSSION AND CONCLUSIONS: Evaluation findings support the conclusions that Canada has made progress in achieving immediate outcomes (achievable in <5 years) associated with advancing its cancer control goals and that there is evidence that, with sustained effort, those goals will translate into a long-term (>25 years) impact on cancer. The mechanism of funding the Partnership to develop collaboration among stakeholders in cancer control to achieve coordinated action has been possible and has been enabled through the Partnership's knowledge-to-action mandate. Opportunities are available to further engage and clarify the roles of stakeholders in action, to clearly define outcomes, and to further quantify the economic benefits that have resulted from a coordinated approach. With the ongoing funding commitment to support coordinated action within a federated environment of health care delivery, there is opportunity to reduce the impact that cancer may have in the long term in Canada.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.026 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.014 | 0.007 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 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".