Radiation Oncology Workforce Recruitment Survey of 2000-2010 Graduates: Is There Need for Better Physician Resource Planning?
Bibliographic record
Abstract
PURPOSE OF THE STUDY: To survey employment and training characteristics of Canadian radiation oncology training program graduates and foreign medical graduates with Canadian radiation oncology post-graduate education or specialist certification. METHODS: A 38-question, web-based survey was distributed to radiation oncologists who completed specialty training between 2000-2010. RESULTS: Out of 256 radiation oncologists contacted, 148 completed the survey (58% response rate). Thirty-two respondents (22%) were foreign MD graduates. One-hundred and fifteen respondents (78%) undertook fellowship training after residency. Many Canadian MD graduates (77%) and foreign MD graduates (34%) had staff positions in Canada, while 11% of all respondents had staff positions outside Canada, and 21% did not have a commitment for staff employment. Of the 31 respondents without a staff position, 22 graduated from Canadian residency training in 2009 or 2010, and 21 had completed medical school training in Canada. CONCLUSIONS: The majority of respondents were successful in securing staff positions in Canada. A sizeable proportion extended training with fellowships. New graduates may have more difficulty in finding Canadian staff positions in radiation oncology in the near future. Implications for specialty training programs and for an improved national strategy for physician resource planning are discussed.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".