Radiation oncology nursing: Highlights of the first multi-disciplinary pan-Canadian workforce survey.
Bibliographic record
Abstract
This study examines the Canadian radiation oncology nursing workforce through a Pan-Canadian Radiation Oncology Health Human Resources survey conducted by the Canadian Association of Nurses in Oncology/Association canadienne des infirmières (CANO/ACIO) partnered with the Canadian Association of Radiation Oncologists (CARO), the Canadian Organization of Medical Physicists (COMP), and the Canadian Association of Medical Radiation Technologists (CAMRT). The survey aimed to gather data on workforce capacity, workload, and scopes of practice, providing critical insights for predictive workforce modelling and policy development. The survey revealed significant variability in nursing full-time equivalents across radiation oncology centres, with large centres averaging more nursing staff per linear accelerator than small ones. The study also highlighted challenges in recruitment and retention, influenced by high workloads, prescriptive work schedules, and the need for specialized education. Despite these challenges, the shift toward team-based care models presents an opportunity to optimize nursing roles within radiation oncology, emphasizing the importance of specialized education and workforce planning. The findings underscore the necessity for a standardized approach to workforce modelling, considering patient acuity and other factors to ensure balanced resource allocation and improve care quality in radiation oncology settings.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.014 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".