The Times They Are A-Changing: What Worked and What We Learned in Deploying Ontario's Wait Time Information System
Bibliographic record
Abstract
How many days would you be comfortable waiting if you needed cancer surgery? What would you do if someone, not as medically urgent, was able to receive an MRI or CT scan before you? Would you want to know if you could wait less time for treatment at another location or with another clinician? These are some of the dilemmas facing patients and our health system when dealing with the issue of wait times. To address these pressing concerns, in the fall of 2004, Ontario launched its Wait Time Strategy. Two years later, Collins-Nakai et al. (2006) reported that Ontario had moved "from being a laggard to a leader" with respect to wait times. This article summarizes Ontario's work to date to improve access to care, including reviewing the need, action taken and the emerging results. Much can be learned and leveraged from the experiences described in this article and throughout this issue. They can serve as an important starting point for further discussion, improvement and action, for initiatives big and small, by all types of organizations and jurisdictions.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".