Impact of the COVID-19 Pandemic on Medical Oncology Workload: A Provincial Review
Bibliographic record
Abstract
(1) Background: Cancer is the leading cause of death in Canada, with significant resource limitation impacting the delivery of cancer care nationwide. The onset of the COVID-19 pandemic forced additional resource restriction and diversion, further impacting care delivery. Our intention is to analyze the impact COVID-19 on a provincial medical oncology workload and bring attention to the limitations of the current workload metric for oncologists. (2) Methods: All medical oncology patient encounters were extracted and compared, collected by year and encounter type, from April 2014 through March 2022. (3) Results: There was an increase in all patient encounters by an average of 9.5% per year, including during the strictest COVID-19 restrictions. There was an increase in virtual care encounters from 37.9% to 52.1%. (4) Conclusions: Medical Oncology workloads have increased over time and estimates suggest growing demand. Little data exist to inform workforce requirements and actual workload is not captured by the current metric. Though volume of new consults continues to increase, COVID-19 has highlighted additional changes in the delivery of care, likely with lasting impact, little of which are included in the current workload metric.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".