Canadian medical oncology workforce and cancer incidence trends: Supply and proxy demand analyses from 1994 to 2020.
Bibliographic record
Abstract
e13526 Background: It is unclear whether the Canadian medical oncology workforce has kept pace with the country’s rising cancer incidence over the last three decades. This study sought to characterize the national and provincial trends in the medical oncologist (MO) and MO trainee workforce during the last 27 years and explore the relationship between workforce and cancer incidence. Methods: Publicly available databases from the Canadian Medical Association (CMA; 1994-2019) and Canadian Institute of Health Information (CIHI) database (1994-2020) were utilized to estimate the number, demographics, and regional distribution of practicing MOs in Canada from 1994 to 2020. Cancer incidence by province was obtained from Statistics Canada for the period 1990 to 2018, except for Quebec where only 1990-2010 data was available, and then projected to 2020 across five regions (The West Coast, The Prairies, Ontario, Quebec and Atlantic Canada) using Canadian population statistics from Statistics Canada and age-period-cohort modeling. To estimate changes in demand for, and supply of, medical oncology services over time, annual cancer incidence to MO provider ratios were calculated. Finally, the Canadian Post MD Education Registry (CAPER) database (1994-2020) was used to characterize the number of MO trainees. Results: Between 1994 and 2020, annual cancer incidence nationally rose from 120,255 cases to 225,800 cases (88%), while the number of MOs increased from 161 to 642 (298%). Incident cancer cases to medical oncologist (MO) ratio dropped from 749:1 in 1994 to 352:1 in 2020. This ratio fell in all five analyzed regions from 1994 to 2020 (Atlantic Canada 1836:1 to 447:1, The Prairies 1257:1 to 377:1, The West Coast 909 to 261:1, Ontario 682:1 to 411:1 and Quebec 543:1 to 300:1). In 1994, 24% of MO providers were ≥ 50 years old compared with 40% in 2020. Nationally, the MO workforce has nearly reached gender parity with 46% female in 2020 versus 25% in 1994. Trends in Canadian MO trainees have largely mirrored MO trends with a 203% increase in the annual trainee cohort from 34 in 1994 to 103 in 2020. In 1994, 34% of trainees were female compared with 63% in 2020. The largest proportion of the country's MOs (34% in 2020) and MO trainees (49%) are located in Canada’s most populous province, Ontario. Conclusions: Although the Canadian MO workforce has shown considerable growth between 1994-2020, a higher proportion of MO providers are nearing retirement age and may influence future workforce trends. Moreover, our study was not able to take into consideration referral patterns or the increasing number and complexity of systemic therapies that also influence MO workload. Ongoing monitoring of human resource levels in medical oncology and cancer incidence data are key metrics to meaningfully inform MO training programs of an appropriately sized trainee cohort and ensure future demands for MO services in cancer care are met.
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 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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.018 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".