Exploring the Role of Leadership in Facilitating Change to Improve Cancer Survival: An Analysis of Experiences in Seven High Income Countries in the International Cancer Benchmarking Partnership (ICBP)
Bibliographic record
Abstract
BACKGROUND: The differences in cancer survival across countries and over time are well recognised, with progress varying even among high-income countries with comparable health systems. Previous research has examined several possible explanations, but the role of leadership in systems providing cancer care has attracted little attention. As part of the International Cancer Benchmarking Partnership (ICBP), this study looked at diverse aspects of leadership to identify drivers of change and opportunities for improvement across seven high-income countries. METHODS: Key informants in 13 jurisdictions were interviewed: Australia (2 states), Canada (3 provinces), Denmark, Ireland, New Zealand, Norway and United Kingdom (4 countries). Participants represented a range of stakeholders at different tiers of the system. They were recruited through a combination of purposive and 'snowball' strategies and participated in semi-structured telephone interviews. Interview transcripts were analysed thematically drawing on the World Health Organization (WHO) health systems framework and previous work analysing national cancer control programmes (NCCPs). RESULTS: Several facets of leadership were perceived as important for improving outcomes. These included political leadership to initiate and maintain progress, intellectual leadership to support those engaged in local implementation of national policies and drive change, and a coherent vision from leaders at different levels of the system. Clinical leadership was also viewed as vital for translating policy into action. CONCLUSION: Certain aspects of cancer care leadership emerged as underpinning and sustaining improvements, such as appointing a central agency, involving clinicians at every stage, ensuring strong leadership of cancer care with a consistent political mandate. Improving cancer outcomes is challenging and complex, but it is unlikely to be achieved without effective leadership, both political and clinical.
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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 | 0.019 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.007 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".