The role of leadership in driving change in cancer survival outcomes in seven high income countries
Bibliographic record
Abstract
Abstract Background There is well-established variation in cancer survival across high-income countries with seemingly-similar health systems. There is much research on reasons for these differences, but the role of leadership has been under-researched despite being one of the WHO 'building blocks' that underpin a functioning health system. Leadership is variously defined as including governance, stewardship, responsibility and accountability. As part of the International Cancer Benchmarking Partnership, this study looked at these diverse aspects of leadership to identify drivers of change and improvement across a range of high-income countries. Methods Cancer strategy documents were analysed from 22 jurisdictions: Australia (3 states), Canada (10 provinces), Denmark, Ireland, New Zealand, Norway, and UK (4 countries). Key informants in 15 of these jurisdictions, representing a range of stakeholders, were recruited through a combination of purposive and 'snowball' strategies, and invited to participate in semi-structured interviews. Documents and interview transcripts were analysed using a thematic approach. Results Different facets of leadership emerged: diffused vs centralised (including the central role of a cancer agency in some places); national, regional and local leadership structures; links between primary and secondary care. This study, however, demonstrated a central role for sustained leadership and political commitment, crucial for initiating and maintaining progress, as was a coherent vision that supported the implementation of national policies locally. Clinical leadership emerged as vital for translating policy into action. Conclusions Improving cancer outcomes is challenging and complex but is unlikely to be achieved without effective leadership and sustained political commitment that can create effective co-ordination of care. Key messages Different facets of leadership of the cancer care system emerged as important when exploring the reasons for variations in cancer outcomes in high-income countries. The persistence of these variations is unacceptable. Change will require political commitment and effective leadership, especially by clinicians.
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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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| 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".