Exploring the link between cancer policies and cancer survival: a comparison of seven countries
Bibliographic record
Abstract
Abstract Background Disparity in cancer survival across countries has been linked to variation in cancer policy delivery but there is lack of empirical evidence for this association. We traced the evolution of cancer policies in 20 jurisdictions in Australia, Canada, Denmark, Ireland, Norway, New Zealand and the UK since 1995 and present the findings of an exploratory analysis linking cancer policy consistency to cancer survival. Methods We systematically searched and analysed national/regional cancer plans and strategies, mapping timelines of cancer policy evolution. For 10 jurisdictions, evidence was synthesised into five categories: oversight function; cancer plan; implementation plan; budget for plan implementation; and evaluation. We assigned scores evaluating whether a category was present or absent, and weighted scores for consistency. Summed scores were correlated with trends in survival from seven cancers between 1995-2014. Results All ten jurisdictions had implemented a high-level structure overseeing, steering or delivering cancer control policies (1995 - 2014); all had also published at least one major cancer plan. There was great variation in oversight mechanisms, ranging from institutionalising cancer control (New South Wales, Ontario) to cancer steering groups or taskforces (Denmark, Northern Ireland, Wales). Frequency and consistency of cancer plans also varied, from a succession of plans that build on each other (Denmark, New South Wales, Ontario) to the publication of isolated plans (New Zealand, Northern Ireland). We found a positive, albeit weak, correlation of cancer policy consistency and improvements in survival over time for six of the seven cancers. Conclusions Jurisdictions that have implemented consistent cancer control policies over time tended to be more successful in improving survival for a wide range of cancers. Our findings can help guide policymakers seeking approaches and frameworks to improve cancer services and, ultimately, cancer outcomes. Key messages • Sustained and consistent strategic cancer planning and investment are crucial for ensuring better patient outcomes, and this requires strong and sustained commitment at all levels. • The findings can help guide policymakers seeking approaches and frameworks to improve cancer services and, ultimately, cancer outcomes.
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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 | 0.006 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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; 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".