Scoping Review of Peer-Reviewed Research Regarding Oncologist COVID-19 Redeployment to Emergency Care: The Emergency, Burnout, Patient Outcome, and Coping
Bibliographic record
Abstract
Introduction: A limited March 2024 Google Scholar search regarding COVID-19 redeployment to emergency care in fourteen medical specialties found no oncologist returns. Identifying oncologist redeployment through a scoping review of peer-reviewed research from several databases investigates this anomaly. Method: Searched are Web of Science, Scopus, PubMed, OVID, Google Scholar, and the Cochrane COVID-19 Study Register with the keywords “burnout AND COVID-19 AND emergencies AND oncologists” concerning the emergency experienced, their burnout response, and patient outcome. Results: Following the PRISMA scoping review process, the assessment is of eight reports from 17,848 results. The finding is that there was a redeployment of oncologists to emergency care. It was defined in various ways and caused oncologist burnout for several internally and externally directed reasons. These reasons negatively affected patient outcomes, contributing to the adoption of different coping techniques by oncologists. Oncologists, uniquely among medical specialists, experienced burnout regarding empathy for the increased mortality risk of their patients and the diminished doctor/patient bond. They also lacked symptom-directed coping. Conclusion: The results of this study may reinforce to oncologists the importance of their doctor/patient dyad and of initiating coping strategies that include symptom-directed health improvement techniques when the redeployment of oncologists is again to emergency care.
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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 | 0.055 | 0.221 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.047 | 0.046 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.020 | 0.005 |
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".