A Scoping Review of Radiation Oncology Educational and Career-Planning Interventions in Undergraduate Medical Education
Bibliographic record
Abstract
Radiation oncology (RO) teaching in undergraduate medical education (UME) is lacking worldwide with potentially detrimental effects on medical student career choices and patient care. The objective of this scoping review is to examine the extent of published literature describing RO educational and career-planning interventions in UME. Online databases were searched from respective dates of inception to June 2020 for articles that reported outcomes from RO educational and career-planning interventions in UME. Two independent reviewers screened entries for inclusion. Following full-text reviews, 25 articles were analyzed. Most interventions were a single session, involved clinical medical students, and were based in North America. Didactic teaching was most commonly used, though a majority included interactive learning in addition to or in place of didactic teaching. As expected, there was a heterogeneity of outcomes reported, and most studies collected data using surveys alone. Recurring topics included the multidisciplinary nature of oncology and psychosocial oncology. There was a paucity of studies reporting on formal mentorship programs and research programs. The data collated in this study can help develop new initiatives based on what has succeeded in the past. Areas that may benefit from future studies include mentorship programs, research programs, and interventions from outside North America.
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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 | 0.014 | 0.054 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.014 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".