WE‐D‐204‐04: Learning the Ropes: Clinical Immersion in the First Month of Residency
Bibliographic record
Abstract
Speakers in this session will present overview and details of a specific rotation or feature of their Medical Physics Residency Program that is particularly exceptional and noteworthy. The featured rotations include foundational topics executed with exceptional acumen and innovative educational rotations perhaps not commonly found in Medical Physics Residency Programs. A site‐specific clinical rotation will be described, where the medical physics resident follows the physician and medical resident for two weeks into patient consultations, simulation sessions, target contouring sessions, planning meetings with dosimetry, patient follow up visits, and tumor boards, to gain insight into the thought processes of the radiation oncologist. An incident learning rotation will be described where the residents learns about and practices evaluating clinical errors and investigates process improvements for the clinic. The residency environment at a Canadian medical physics residency program will be described, where the training and interactions with radiation oncology residents is integrated. And the first month rotation will be described, where the medical physics resident rotates through the clinical areas including simulation, dosimetry, and treatment units, gaining an overview of the clinical flow and meeting all the clinical staff to begin the residency program. This session will be of particular interest to residency programs who are interested in adopting or adapting these curricular ideas into their programs and to residency candidates who want to learn about programs already employing innovative practices. Learning Objectives: To learn about exceptional and innovative clinical rotations or program features within existing Medical Physics Residency Programs. To understand how to adopt/adapt innovative curricular designs into your own Medical Physics Residency Program, if appropriate.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.068 | 0.026 |
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".