Development of a set of strategy-based system-level cancer care performance indicators in Ontario, Canada
Bibliographic record
Abstract
OBJECTIVES: To develop a set of scientifically sound and managerially useful system-level cancer care performance indicators for public reporting in Ontario, Canada. IMPLEMENTATION: Using a modified Delphi panel method, comprising a systematic literature review and multiple rounds of structured feedback from 34 experts, the Cancer Quality Council of Ontario developed a set of quality indicators spanning cancer prevention through to end-of-life care. To be useful to decision-makers and providers, indicator selection criteria included a clear focus on the cancer system, relevance to a diversity of cancer providers, a strong link to the mission and strategic objectives of the cancer system, clear directionality of indicator results, presence of targets and/or benchmarks, feasibility of populating the indicator, and credibility of the measure as an indicator of quality. To ensure that the selected indicators would measure progress over time against specific and widely accepted goals, we created a strategy map based on the five strategic objectives of the Ontario cancer system: (i) to improve the measurement and reporting of cancer quality, (ii) to increase the use of evidence and innovation in decision-making, (iii) to improve access to cancer services and reduce waiting times, (iv) to increase efficiency across the system, (v) to reduce the burden of cancer. An analysis of the mean indicator ratings by experts, and the strategy mapping exercise resulted in the identification of 36 indicators deemed suitable for routine performance measurement of the Ontario cancer system. LESSONS LEARNED: The resulting instrument incorporates a credible evidence basis for performance measurement aligned to the five strategic goals for the Ontario cancer system. It represents the integrating of a management culture, focused on the implementation of a new strategic direction for the cancer system, with the underlying evidence-based culture of clinicians.
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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 | 0.036 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.010 | 0.020 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".