Bibliographic record
Abstract
PURPOSE: A performance management system has been implemented by Cancer Care Ontario (CCO). This system allows for the monitoring and management of 11 integrated cancer programs (ICPs) across the Province of Ontario. The system comprises of four elements: reporting frequency, reporting requirements, review meetings and accountability and continuous improvement activities. CCO and the ICPs have recently completed quarterly performance review exercises for the last two quarters of the fiscal year 2004-2005. The purpose of this paper is to address some of the key lessons learned. DESIGN/METHODOLOGY/APPROACH: The paper provides an outline of the CCO performance management system. FINDINGS: These lessons included: data must be valid and reliable; performance management requires commitments from both parties in the performance review exercises; streamlining performance reporting is beneficial; technology infrastructure which allows for cohesive management of data is vital for a sustainable performance management system; performance indicators need to stand up to scrutiny by both parties; and providing comparative data across the province is valuable. Critical success factors which would help to ensure a successful performance management system include: corporate engagement from various parts of an organization in the review exercises; desire to focus on performance improvement and avoidance of blaming; and strong data management systems. PRACTICAL IMPLICATIONS: The performance management system is a practical and sustainable system that allows for performance improvement of cancer care services. It can be a vital tool to enhance accountability within the health care system. ORIGINALITY/VALUE: The paper demonstrates that the performance management system supports accountability in the cancer care system for Ontario, and reflects the principles of the provincial governments commitment to continuous improvement of healthcare.
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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.008 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".