Coronavirus Disease 2019’s (COVID-19’s) Silver Lining—Through the Eyes of Radiation Oncology Fellows
Bibliographic record
Abstract
PURPOSE: The coronavirus disease 2019 (COVID-19) pandemic has propelled health care workers to the front lines against the pandemic. In addition to anxiety related to infection risks, trainees have the additional burden of learning and career planning while providing care in an uncertain and rapidly changing environment. We conducted a survey to evaluate the practical and psychosocial impact on radiation oncology fellows during the first month of the pandemic. METHODS AND MATERIALS: A 4-part survey was designed and distributed to the fellows in our program. The survey was designed to evaluate the impact of the pandemic on scope of activity and well-being ("Impact on You") and to identify key lessons learned and social factors ("About You") using Likert scales and open-ended response options. The survey included items from the Oldenburg Burnout questionnaire. RESULTS: = .002), this was replaced by virtual consults and other COVID-related activities. The proportion of respondents demonstrating features of burnout in the domains of "disengagement" and "exhaustion" was 71% and 64%, respectively. However, there was also evidence of resilience, with 47% respondents "feeling energized." Top "concerns" and "negative changes" identified related to learning, infection risk and safety, patient care, coping, and concerns about their home country. Top "positive changes" highlighted include work culture, appreciation for leadership caring for the team, the insistence on evidence to guide change, and the implementation of virtual health care. CONCLUSIONS: Negative impact needs to be anticipated, acknowledged, and managed. We anticipate understanding the positives that have emerged under these extraordinary circumstances is the "silver lining" of the pandemic, giving us tools and the best leverage to plan for the future.
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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 | 0.007 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".