Contract Management of Ontario's Cancer Surgery Wait Times Strategy
Bibliographic record
Abstract
The province of Ontario, as a result of the First Ministers' Meeting, was committed to addressing surgery wait times in Ontario. The Ministry of Health and Long-Term Care's response to this commitment was the Wait Times Strategy (WTS) initiative, which addressed access issues with the aim of positively impacting wait times in cancer surgery. Cancer Care Ontario (CCO) was tasked with managing the cancer surgery WTS. CCO engaged in accountability agreements with Ontario hospitals to provide incremental cancer surgery volumes, in return for one-time funding. Through the use of accountability agreements, CCO was able to tie service volume delivery, quality care initiatives and reporting requirements to funding. Other elements of the cancer surgery WTS implementation included the development of wait times definitions, guidelines and targets; the use of a performance management system; facilitation by existing regional cancer leads and continued development of regional cancer programs. Eight key lessons were learned: (1) baseline volume guarantees are critical to ensuring that wait times are positively impacted; (2) there is a need to create a balance between accountability and systems management; (3) clinical quality initiatives can be tied to funding initiatives; (4) allocations of services should be informed by many factors; (5) regional leadership is key to ensuring that local needs are met; (6) data are invaluable in improving performance; (7) there is regional disparity in service delivery, capacity and resources across the province; and (8) program sustainability is an underlying goal of the WTS for cancer surgery. The implication is that accountability agreements can be leveraged to create sustainable health management systems.
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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 | 0.019 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.010 | 0.003 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.020 | 0.003 |
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".