Bibliographic record
Abstract
Population-level intervention is required to prevent cancer and other chronic diseases. It also promotes health for those living with established risk factors and illness. In this article, the authors describe a vision and approach for continuously improving population-level programs and policies within and beyond the health sector. The vision and approach are anchored in contemporary thinking about what is required to link evidence and action in the field of population and public health. The authors believe that, as a cancer prevention and control community, organizations and practitioners must be able to use the best available evidence to inform action and continually generate evidence that improves prevention policies and programs on an ongoing basis. These imperatives require leaders in policy, practice, and research fields to work together to jointly plan, conduct, and act on relevant evidence. The Propel Center and colleagues are implementing this approach in Youth Excel-a pan-Canadian initiative that brings together national and provincial organizations from health and education sectors and capitalizes on a history of collaboration. The objective of Youth Excel is to build sustainable capacity for knowledge development and exchange that can guide and redirect prevention efforts in a rapidly evolving social environment. This goal is to contribute to creating health-promoting environments and to accelerate progress in preventing cancer and other diseases among youth and young adults and in the wider population. Although prevention is the aim, health-promoting environments also can support health gains for individuals of all ages and with established illness. In addition, the approach Youth Excel is taking to link evidence and action may be applicable to early intervention and treatment components of cancer control.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.200 | 0.062 |
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".