"There’s Not Enough Bodies to Do the Demand:" An Exploration of Key Stakeholder Views on the Role of Health Service Capacity in Shaping Cancer Outcomes in 7 International Cancer Benchmarking Partnership Countries
Bibliographic record
Abstract
BACKGROUND: Differences in cancer survival are shaped by differences in health system capacity in workforce and infrastructure. Part of the International Cancer Benchmarking Partnership (ICBP), this study explored stakeholders' perceptions of the role of health system capacity necessary for cancer care in influencing cancer survival in 7 high-income countries. METHODS: We conducted semi-structured interviews with 79 key informants from national, regional, and local tiers of health systems, professional bodies, patient associations, and academic experts in Australia, Canada, Denmark, Ireland, New Zealand, Norway, and the United Kingdom. Data collection was guided by a conceptual model linking characteristics of health systems and cancer survival along the cancer patient journey, from recognition of symptoms at pre-diagnostic stages through to survivorship or death. Data were analysed using a thematic approach. RESULTS: We identified 3 themes as important in shaping cancer outcomes: primary care and access to diagnostic evaluation, specialist care and access to treatment, and workforce pertaining to diagnostic and treatment phases. Improved infrastructure for diagnosis and treatment had improved cancer outcomes in all jurisdictions. However, this was seen as insufficient if staffing was inadequate. Consolidation of services and greater surgical specialisation was important in some jurisdictions if accompanied by a reconfiguration of services, in particular the creation of specialist multidisciplinary teams, along with supporting capacity in the wider health system. Staff shortages were commonly cited as reasons why some jurisdictions lagged behind others. CONCLUSION: Continued improvement in cancer outcomes will require sustained investment in plans to deliver and maintain the workforce engaged in cancer care and in the infrastructure on which they depend. However, strategic plans must recognise that systems for cancer care do not work in isolation from the rest of the health system and a whole systems approach is essential if we are to improve outcomes for an ageing, increasingly multimorbid population.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.038 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.010 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| 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".