Exploring Motivating Factors for Pursuing Radiation Oncology: A Comparative Analysis of Medical Students and Residents
Bibliographic record
Abstract
Purpose Increasing medical student (MS) interest in radiation oncology (RO) is important to meet the rising demand for radiation oncologists. Understanding the factors that drive MS to pursue RO is crucial. This study compares motivating factors between MS and RO residents to inform interventions to increase recruitment and sustained interest in the specialty. Methods Data from two similar studies investigating factors motivating MS and residents to pursue RO were analyzed. The first study surveyed Canadian RO residents to characterize enablers when applying for RO residency. The second study analyzed application essays from MS applying to an RO studentship. A mixed methods approach was used to compare themes ("career aspects," "prior exposure," and "personal experiences") between the datasets. Results Qualitative analysis demonstrated that both MS and residents identified "career aspects" as the most common theme facilitating interest in RO careers. "Multidisciplinary work" and "direct clinical contact and patient care" were prominent sub-themes. MS emphasized "serious illness and palliative care" and "advanced technology," while residents prioritized RO as a "rewarding career." "Prior exposure," particularly through shadowing/observerships, was more important for MS than residents who valued clinical experiences. Practical career considerations including "mentorship" and "career satisfaction and lifestyle" were significant motivators for residents. Conclusion MS value content-based aspects of RO and emphasize shadowing. In contrast, RO residents prioritize lifestyle-based considerations. These differences highlight the opportunity for intervention throughout medical training to sustain interest in RO and facilitate applications to RO residency programs.
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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 | 0.006 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".