Bibliographic record
Abstract
The Ontario Ministry of Health and Long-Term Care (MOHLTC) launched the Wait Time Strategy in 2004 to improve access to healthcare by reducing the wait times for procedures and treatments. A fundamental component of the strategy was the development of the Wait Time Information System (WTIS). On behalf of the MOHLTC, Cancer Care Ontario (CCO) delivered the first electronic application used by hospitals province-wide to collect essential wait time data. Until then, clinicians had been maintaining wait lists within their own offices (usually on paper), but had no effective way to manage waits that were getting too long. Patients also wanted faster treatment, but had no concrete information to hold the health system accountable for inappropriate waits or to help in managing their own care. Lastly, hospitals and health system planners knew that a more comprehensive view of wait times could help them make objective decisions around how to allocate resources. The WTIS was introduced to solve this information challenge. Having better information, however, is only one side of the equation. Arguably, it's how you use the data that will provide the benefit. The Wait Time Strategy (the strategy) used a "pay for performance" approach requiring hospital leaders to be accountable for using the data captured through the WTIS to achieve defined wait time targets in return for funding for more procedures and programs. Hospital accountability for improving performance was further driven through the reporting of wait times on a public website (www.ontariowaittimes.com). Here we examine the steps that CCO took to support the collection of necessary data and turn it into meaningful information to drive improvements. This experience is now being used to shape performance management activities for the broader access to care agenda across the province.
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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 | 0.078 | 0.280 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.016 | 0.015 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.029 | 0.049 |
| Open science | 0.006 | 0.022 |
| Research integrity | 0.007 | 0.015 |
| Insufficient payload (model declined to judge) | 0.028 | 0.020 |
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".