Clinician Perspectives of COVID-19-Related Cancer Drug Funding Measures in Ontario
Bibliographic record
Abstract
The COVID-19 pandemic has a significant impact on cancer patients and the delivery of cancer care. To allow clinicians to adapt treatment plans for patients, Ontario Health (Cancer Care Ontario) issued a series of interim funding measures for the province's New Drug Funding Program (NDFP), which covers the cost of most hospital-delivered cancer drugs. To assess the utility of the measures and the need for their continuation, we conducted an online survey of Ontario oncology clinicians. The survey was open 3-25 September 2020 and generated 105 responses. Between April and June 2020, 46% of respondents changed treatment plans for more than 25% of their cancer patients due to the pandemic. Clinicians report broad use of interim funding measures. The most frequently reported strategies used were treatment breaks for stable patients (62%), extending dosing intervals (59%), and deferring routine imaging (56%). Most clinicians anticipate continuing to use these interim funding measures in the coming months. The survey showed that adapting cancer drug funding policies has supported clinical care in Ontario during the pandemic.
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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 | 0.010 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".