OP02 The role of leadership in driving change in cancer outcomes in high income countries
Bibliographic record
Abstract
<h3>Background</h3> There is well-established variation in cancer survival and, despite general improvements over time, differential progress has been made across high-income countries with seemingly similar health systems. Research has explored the source of these differences in outcomes, but the role of leadership in cancer care systems has been under-researched. Leadership is one of the WHO ‘building blocks’ that underpin a functioning health system. It 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. <h3>Methods</h3> 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 at the different tiers of the system, were recruited: hospital managers; regional and/or government officials; representatives from arms’ lengths bodies, professional bodies and patient associations; experts within the cancer field and with wider health policy expertise. Key informants were identified through a combination of purposive and ‘snowball’ strategies. They participated in semi-structured interviews held in English, using online conferencing software. Documents and interview transcripts were analysed using a thematic approach using a framework based on the WHO health systems framework and previous work analysing national cancer control programmes. <h3>Results</h3> Different facets of leadership emerged: diffused across health boards <i>vs</i> centralised (including the central role of a cancer agency in some places); the interplay between national, regional and local leadership structures; the establishment of links between primary and secondary care. The study demonstrated a central role of 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 of the cancer care system emerged as vital for translating policy into action. <h3>Conclusion</h3> Certain aspects of cancer care leadership emerged as underpinning and sustaining improvements. Improving cancer outcomes is challenging and complex, but it is unlikely to be achieved without effective leadership and sustained political commitment that can create effective co-ordination of care. These lessons can be applied to jurisdictions which are struggling to achieve the progress they might otherwise be able to, and to a variety of conditions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".