Clinical and policy contexts for cancer care: Evidence from Denmark, Ireland and Ontario (Canada)
Bibliographic record
Abstract
Abstract Background We know there are large differences in cancer survival between countries. We know much less about why they exist and persist. In the International Cancer Benchmarking Partnership, we are seeking to understand the influences on patient journey in different countries. In this study we explore how health system factors impact on cancer care in Denmark, Ireland, and Ontario (Canada) to identify common themes, national specificities, and messages for other health care providers. Methods We systematically analysed (i) cancer policy and strategy documents from the three jurisdictions, published between 1995-2014 (n = 20) and (ii) interviews with key informants representing government, health services providers, professional bodies and patient organisations (n = 25). We thematically analysed both datasets using NVivo. Results Five themes emerged from the document review and were confirmed by interview: governance, quality assurance, service delivery, infrastructure and workforce. All three jurisdictions introduced a designated organisation to lead, monitor and, in Ontario, fund cancer services. Reducing wait times was prioritized, with the expansion of diagnostic capacity from the 2000s, for example. Concentrating services into fewer specialist centres was widely viewed as crucial for improving survival for some cancers. Yet policy intent was not always successfully realised on the ground, with lack of sustained investment, organisational barriers or logistical challenges impeding implementation. Jurisdictions face particular challenges maintaining and upgrading infrastructure and equipment, and recruiting and retaining critical staff, specifically in radiology and primary care. Conclusions Cancer care is complex and understanding the interrelationships between factors acting at different levels of the health system is important to improve outcomes. Continued investment in infrastructure and people will be essential. Key messages Countries face common challenges in creating health systems that optimise cancer outcomes. Sustained investment in equipment and human resources will be critical to optimise cancer care and survival.
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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 | 0.016 | 0.070 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.010 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".