Reconstructing Cancer Services in Ontario
Bibliographic record
Abstract
This paper draws upon experience gained in the recent restructuring of cancer services in Ontario that can provide insights for broader regionalization efforts. Although Ontario is the only province in Canada not to regionalize its healthcare system, the Ontario cancer services system, like most others in Canada, is based on a regionalized system. However, the growing burden of cancer and predictable crises in cancer services in Ontario necessitated a rethinking of how the cancer system should be structured and how services should be delivered. Based on recommendations by the Cancer Services Implementation Committee in 2001, Ontario's cancer services system has recently gone through major restructuring, which has established new institutional arrangements for the Ministry of Health and Long-Term Care, Cancer Care Ontario (CCO) (the provincial cancer agency) , a new Quality Council and 11 new regionally based Integrated Cancer Programs (ICPs). This restructuring has created several levers for promoting regional change and motivating performance improvement, including (1) public reporting on performance with a new quality mandate, (2) fiscal and performance-based agreements between CCO and the ICPs, (3) leading and coordinating communities of practice and (4) direct ministerial access. While institutional relationships are still developing, these experiences may provide important insights for regionalization efforts in other jurisdictions and sectors in Canada.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.020 | 0.007 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.016 | 0.009 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".