How might healthcare systems influence speed of cancer diagnosis: A narrative review
Bibliographic record
Abstract
Striking differences exist in outcomes for cancer between developed countries with comparable healthcare systems. We compare the healthcare systems of 3 countries (Denmark, Norway, Sweden), 3 UK jurisdictions (England, Wales and Northern Ireland), 3 Canadian provinces (British Columbia, Manitoba, Ontario) and 2 Australian states (New South Wales, Victoria) using a framework which assesses the possible contribution of primary care systems to a range of health outcomes, drawing on key characteristics influencing population health. For many of the characteristics we investigated there are no significant differences between those countries with poorer cancer outcomes (England and Denmark) and the rest. In particular, regulation, financing, the existence of patient lists, the GP gatekeeping role, direct access to secondary care, the degree of comprehensiveness of primary care services, the level of cost sharing and the type of primary care providers within healthcare systems were not specifically and consistently associated with differences between countries. Factors that could have an influence on patient and professional behaviour, and consequently contribute to delays in cancer diagnosis and poorer cancer outcomes in some countries, include centralisation of services, free movement of patients between primary care providers, access to secondary care, and the existence of patient list systems. It was not possible to establish a causal correlation between healthcare system characteristics and cancer outcomes. Further studies should explore in greater depth the associations between single health system factors and cancer outcomes, recognising that in complex systems where context is all-important, it will be difficult to establish causal relationships. Better understanding of the interaction between healthcare system variables and patient and professional behaviour may generate new hypotheses for further research.
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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 | 0.006 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 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".