Identifying targets for outpatient palliative care wait times in Ontario.
Bibliographic record
Abstract
258 Background: A wait time indicator is being developed for outpatient palliative care services for cancer patients in Ontario. Once developed, this indicator will become the first palliative performance measure for Regional Cancer Centres (RCCs) across the province. The wait time indicator will serve as one measure of accessibility, a key dimension of health care quality. However, in order to create this indicator, it is first necessary to identify targets against which current performance can be compared. Methods: A systematic review of the academic literature was conducted to a) identify existing palliative care wait time standards, and b) to gather evidence on how delays in care impact patient outcomes. An environmental scan was also conducted to identify wait time standards and benchmarks used in other Canadian provinces or territories. In addition, existing palliative care triaging tools and wait time standards from Ontario RCCs were collected. A consensus panel comprised of palliative clinicians, patient and family advisors, and administrators was convened to articulate a maximum acceptable wait time and a provincial target for the percentage of patients who should be seen within the maximum acceptable wait time. Results: The environmental scan and literature review found no existing standards or benchmarks for outpatient palliative care services. However, there are a number of triaging tools and wait time standards in use at Ontario RCCs for these services. Taking these tools and standards into consideration, the consensus panel identified a wait time benchmark and a provincial target for Ontario RCCs. Conclusions: This foundational work will help to highlight gaps and variation in timely access to palliative care services in Ontario. The benchmarks and targets identified through this process as well as the methods used can be useful for other jurisdictions seeking to measure and improve wait times for these services. Next steps include ensuring that data is of sufficient quality, identifying incremental improvement targets for the province based on current performance, and further refining the implementation of a palliative care wait time indicator for quality improvement.
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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 | 0.007 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 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".