Workforce Trends Among Canadian Medical Oncologists and Medical Oncology Trainees over Two Decades
Bibliographic record
Abstract
Background: Understanding oncology health human resources across Canada is critical to the delivery of quality cancer care. Little has been published about the medical oncology (MO) workforce and trainees; this study sought to characterize trends in the MO workforce and explore the relationship between medical oncologists and cancer incidence as a surrogate demand marker. Materials and Methods: Publicly available databases from the Canadian Medical Association, the Canadian Institute of Health Information, and the Canadian Post MD Education Registry were utilized to estimate the number, demographics, and regional distribution of practicing MOs and MO trainees between 1994 and 2020. Cancer incidence by province was obtained from Statistics Canada. To estimate changes in demand for, and supply of, medical oncology services over time, annual cancer incidence to MO provider ratios were calculated. Results: Between 1994 and 2020, annual cancer incidence nationally rose from 120,255 to 225,800 cases, while the number of MOs increased by 298%. Incident cancer case to medical oncologist (MO) ratio dropped from 749:1 to 352:1 in the same time. However, the MO workforce is aging; in 2020, 40% of providers were ≥50 years old versus 24% in 1994. Trends in Canadian MO trainees mirror MO trends. Ontario has the largest proportion of the country’s MOs (34% in 2020) and MO trainees (49%). Conclusions: Although the Canadian MO workforce has grown, more MO providers are nearing retirement age, which may influence future workforce trends. Ongoing monitoring of human resources in oncology is essential to ensure future demands for services are met.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".