Lifetime in Radiation Oncology: Wisdom from Retired Physicians
Bibliographic record
Abstract
PURPOSE: Radiation oncology is a demanding yet deeply rewarding field, with physicians navigating complex clinical decisions in dynamic environments. Existent literature has focused on the challenges and stressors of the profession; however, less attention has been given to the perspective of these who have completed their careers. This study explores the reflections of retired radiation oncologists, highlighting the aspects of their work that brought fulfillment and meaning over time. METHODS AND MATERIALS: Semistructured interviews were conducted with 14 retired radiation oncologists, transcribed verbatim, and analyzed using NVivo 12 to identify these aspects. We employed content and thematic analysis procedures to guide our data analysis by: (1) disassembling data into codes; (2) grouping codes into themes; and 3) interpretating emerging themes. RESULTS: Four main themes were identified in regard to their experiences: "A Good Profession," "Highly Stressful," "Impact on Quality of Life In and Out of the Workplace," and "Retirement." Additionally, we examined variations in experiences based on gender differences and international training. Overall, participants described their career as fulfilling, emphasizing profound satisfaction through patient care and discussed protective factors that helped achieve a high quality of life including mentorship and a strong sense of purpose. At the same time, participants acknowledged challenges faced in this career, such as the high levels of stress that are attributed to evolving technology, administrative burden, and the emotional toll of patient care. A lack of a structured professional support system was noted as an area for improvement, with mentorship identified as particularly valuable. Although retirement decisions were often influenced by personal considerations, these stressors also played a role for some. CONCLUSIONS: This study suggests that fostering mentorship opportunities and strategies for maintaining career satisfaction can help sustain well-being throughout a radiation oncology career. By understanding the perspective of retired physicians, we can better support the next generation in finding long-term fulfillment in the field.
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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 | 0.020 | 0.065 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.012 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.010 | 0.025 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".