Management of Medical Oncology Services in Canada: Redefined Workload With a Novel Supply-and-Demand Workforce Projection Model
Bibliographic record
Abstract
PURPOSE: We developed a workforce-planning model to predict Canadian medical oncologist (MO) supply and clinical demand during the next 10 years. MATERIALS AND METHODS: A forward calculation model was created to forecast the balance of MO supply and demand. MO supply was estimated by using Canadian Institute for Health Information, Canadian Medical Association, and Canadian Post-MD Education Registry data. Care demand was estimated by using data from Canadian Cancer Statistics and Alberta Cancer Registry. The Canadian Royal College MO Committee confirmed its face validity. RESULTS: The MO workforce is expected to grow from 541 staff in 2016 to 830 staff in 2026. During this period, new hires will increase from 39 to 56 per year, and departures will increase from 15 to 24 per year. Although cancer incidence rates will grow from 202,149 to 257,497, a projected increase in MO supply will mean fewer initial consultations, from an average of 168.5 consultations per MO in 2016 to 129.2 consultations per MO in 2026. The initiation of systemic therapy is projected to remain stable at 102.3 new systemic therapy starts per MO per year. CONCLUSION: We have developed a forward calculation MO workforce model that predicts a growing Canadian MO workforce and redefines MO workload dynamics. MO providers will increasingly support more follow-up care with the initiation of multiple lines of systemic therapy relative to the medical management of patients at the time of initial cancer diagnosis. Workload metrics, including follow-up and new therapy initiation rates, must be measured to appropriately to meet increasingly complex and growing care demands.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".