Development of a residency program in radiation oncology physics: an inverse planning approach
Bibliographic record
Abstract
Over the last two decades, there has been a concerted effort in North America to organize medical physicists' clinical training programs along more structured and formal lines. This effort has been prompted by the Commission on Accreditation of Medical Physics Education Programs (CAMPEP) which has now accredited about 90 residency programs. Initially the accreditation focused on standardized and higher quality clinical physics training; the development of rounded professionals who can function at a high level in a multidisciplinary environment was recognized as a priority of a radiation oncology physics residency only lately. In this report, we identify and discuss the implementation of, and the essential components of, a radiation oncology physics residency designed to produce knowledgeable and effective clinical physicists for today's safety-conscious and collaborative work environment. Our approach is that of inverse planning, by now familiar to all radiation oncology physicists, in which objectives and constraints are identified prior to the design of the program. Our inverse planning objectives not only include those associated with traditional residencies (i.e., clinical physics knowledge and critical clinical skills), but also encompass those other attributes essential for success in a modern radiation therapy clinic. These attributes include formal training in management skills and leadership, teaching and communication skills, and knowledge of error management techniques and patient safety. The constraints in our optimization exercise are associated with the limited duration of a residency and the training resources available. Without compromising the knowledge and skills needed for clinical tasks, we have successfully applied the model to the University of Calgary's two-year residency program. The program requires 3840 hours of overall commitment from the trainee, of which 7%-10% is spent in obtaining formal training in nontechnical "soft skills".
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 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".