WORK-LIFE BALANCE IN RADIATION ONCOLOGY: CHALLENGES AND FUTURE DIRECTIONS
Bibliographic record
Abstract
Work-life balance (WLB) is recognized as an important factor influencing physician wellness and burnout and serves as a determinant in career choice. Within radiation oncology, WLB should be considered during the process of recruitment, training and retention. This review evaluates WLB in radiation oncology; aiming to assess its significance, identify contributing factors, and explore interventions for improvement. A comprehensive search of Medline, PubMed, and Embase was conducted for all articles published up until December 2024. After a two-stage screening process of titles, abstracts, and full texts, appropriate articles were included. Data extraction assessed key parameters, including definitions of WLB, contributing factors, and possible interventions to improve WLB. A thematic analysis was performed to identify recurring patterns and relationships. Seven out of 76 articles met the search criteria and were subsequently evaluated. While WLB remains challenging to define, it is generally associated with increased job satisfaction, lower stress, and reduced risk of burnout. Influencing factors included clinical/ research workload, education (access to opportunities, comfort with technology), financial pressures (salary, debt), ancillary support (childcare services, time off for family/personal commitments), demographics (gender, family status), and work environment (peer support/perceptions). Most radiation oncologists appear satisfied with their career choices and would choose radiation oncology again, but challenges and concerns exist. Only 5 studies assessed WLB directly, and one study specifically assessed, and found, significant gender disparities in WLB. Approaches to addressing WLB and supporting a sustainable radiation oncology workforce included protected time for research/academics, streamlined administrative tasks and targeted supports for female radiation oncologists. Limitations include potentially inadequate survey measures to assess WLB, and absence of a standardized definition or perception of WLB. Maintaining WLB is important for radiation oncologists and influences job satisfaction. Addressing factors contributing to poor WLB and implementing tailored solutions will be crucial for supporting a sustainable workforce.
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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 | 0.024 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.005 | 0.005 |
| 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".