Navigating the medical physics education and training landscape
Bibliographic record
Abstract
PURPOSE: The education and training landscape has been profoundly reshaped by the ABR 2012/2014 initiative and the MedPhys Match. This work quantifies these changes and summarizes available reports, surveys, and statistics on education and training. METHODS: We evaluate data from CAMPEP-accredited program websites, annual CAMPEP graduate and residency program reports, and surveys on the MedPhys Match and Professional Doctorate degree (DMP). RESULTS: From 2009-2015, the number of graduates from CAMPEP-accredited graduate programs rose from 210 to 332, while CAMPEP-accredited residency positions rose from 60 to 134. We estimate that approximately 60% of graduates of CAMPEP-accredited graduate programs intend to enter clinical practice, however, only 36% of graduates were successful in acquiring a residency position in 2015. The maximum residency placement percentage for a graduate program is 70%, while the median for all programs is only 22%. Overall residency placement percentage for CAMPEP-accredited program graduates from 2011-2015 was approximately 38% and 25% for those with a PhD and MS, respectively. The disparity between the number of clinically oriented graduates and available residency positions is perceived as a significant problem by over 70% of MedPhys Match participants responding to a post-match survey. Approximately 32% of these respondents indicated that prior knowledge of this situation would have changed their decision to pursue graduate education in medical physics. CONCLUSION: These data reveal a substantial disparity between the number of residency training positions and graduate students interested in these positions, and a substantial variability in residency placement percentage across graduate programs. Comprehensive data regarding current and projected supply and demand within the medical physics workforce are needed for perspective on these numbers. While the long-term effects of changes in the education and training infrastructure are still unclear, available survey data suggest that these changes could negatively affect potential entrants to the profession.
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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 | 0.008 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".