Employment Outcomes for Recent Canadian Radiation Oncology Graduates
Bibliographic record
Abstract
Introduction: Radiation oncology (ro) is one of several specialties identified by the Royal College of Physicians and Surgeons of Canada with employment difficulties for graduating trainees. The purpose of the present study was to determine the employment status and location of recent Canadian ro trainees within 2 years after graduation, to monitor workforce recruitment trends over time, and to capture the opinions of program directors about employment difficulty for graduates and resident morale. Methods: Visa trainee graduates were excluded. Results of the survey administered to ro program directors in 2016 and again in 2018, both with 100% response rates, are presented here. Results: In both surveys, approximately 57% of ro graduates had attained staff or locum employment in Canada or abroad within 2 years from graduation (p = 0.92). However, graduates with Canadian staff employment increased by 46% to 32 in 2018 from 22 in 2016, while the proportion of graduates with staff positions abroad decreased to 6% from 27% (p = 0.04). Most trainees without staff positions were employed as fellows. The proportion of program directors reporting employment difficulties for graduates in the Canadian labour market declined to 38% from 85% (p = 0.04), and the morale of residents in training programs remained high. Conclusions: Employment challenges for newly certified Canadian-trained radiation oncologists continue. However, compared with the situation 2 years ago, trends in the Canadian ro job market suggest a modest improvement, with more staff employment in Canada and lower emigration rates for jobs abroad.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".