Transition to practice: creation of a transitional rotation for radiation oncology
Bibliographic record
Abstract
BACKGROUND: Implementation of Competence by Design (CBD) will require residency training programs to develop formalized "Transition to Practice" (TTP) experiences. A multidisciplinary group of Radiation Oncology stakeholders from tertiary care centres in Atlantic Canada were surveyed regarding a proposed TTP rotation. METHODS: The survey asked participants to quantitatively rank various learning objectives based on defined CanMEDS skills that are expected to be mastered by a graduating resident. Mean perceived importance scores were calculated for each objective as well as for their CanMEDS category. Specific written qualitative feedback was also collected. RESULTS: The survey was circulated to 59 participants with a response rate of 73%. The three objectives with the highest mean importance score were "Independently assessing and managing patients seen in consultation," "Developing and demonstrating communication skills with patients at an advanced level," and "Independently assessing and managing follow up patients," respectively from highest to lowest. The CanMEDS roles with the highest importance score was "Communicator." CONCLUSION: Quantitative and qualitative data from a multidisciplinary survey based on CanMEDS roles guided the implementation of a TTP rotation for PGY-5 residents at a tertiary care centre in Atlantic Canada. These results may be relevant to other training programs developing TTP experiences.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".