Establishing physician to patient ratios and predicting workforce needs for Canadian pediatric hematology‐oncology programs
Bibliographic record
Abstract
BACKGROUND: A Human Resources (HR) Committee of C17, the national network of Canadian academic pediatric hematology/oncology programs, obtained comprehensive data enabling analysis and planning for the physician workforce. This study establishes physician to patient ratios and predicts workforce needs for Canadian pediatric hematology/oncology programs. PROCEDURES: Over a 10-year period, six surveys were sent to the 17 pediatric tertiary care centers treating children with cancer and blood disorders. Data were obtained on physician demographics, full time equivalent (FTE) positions, and time spent in clinical, research, education, and administrative activities. Survey results were debated at the C17 national meetings to obtain consensus on workload ratios. RESULTS: Since 1999, the pediatric hematologist/oncologist workforce has increased from 71 FTE (43 oncology, 20 hematology, 8 BMT) to 109.5 FTE positions (69.7 oncology, 29.4 hematology, and 10.4 BMT). The median age of pediatric hematologists/oncologists increased from 46 years to 52 years and the male to female ratio changed from 1.8:1 to 0.9:1. The 2011 job profile showed the median time spent on activities was 60% clinical, 15% education, 15% research, and 10% administration. After assessing workload, models of care, and optimal physician FTE per program, the C17 HR Committee recommended a ratio of one oncologist per 15 newly diagnosed patients with malignancy and a ratio of one BMT physician per 15 transplants. For every 2.5 oncologists, a 1.0 hematologist is the minimum required. CONCLUSION: Physician staffing ratios for pediatric hematology/oncology programs have been established and should be adopted across Canadian academic institutions as a standard.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".