Impact of the COVID-19 Pandemic on Oncologists: Results of an International Study
Bibliographic record
Abstract
PURPOSE As frontline workers facing the COVID-19 pandemic, healthcare providers should be well-prepared to fight the disease and prevent harm to their patients and themselves. Our study aimed to evaluate the knowledge, attitude, and practice of oncologists in response to the COVID-19 pandemic and its impact on them. METHODS A cross-sectional study was conducted using a validated questionnaire disseminated to oncologists by SurveyMonkey. The tool had 42 questions that captured participants’ knowledge, attitude, and practice; their experiences; and the pandemic’s impact on various aspects of their lives. Participants from Middle East and North African countries, Brazil, and the Philippines completed the electronic survey between April 24 and May 15, 2020. RESULTS Of the 1,010 physicians who participated in the study, 54.75% were male and 64.95% were medical or clinical oncologists. The level of knowledge regarding the prevention and transmission of the virus was good in 52% of participants. The majority (92%) were worried about contracting the virus either extremely (30%) or mildly (62%), and 84.85% were worried about transmitting the virus to their families. Approximately 76.93% reported they would take the COVID 19 vaccine once available, with oncologists practicing in Brazil having the highest odds ratio of intention to receive the COVID-19 vaccine (odds ratio, 11.8, 95% CI, 5.96 to 23.38, P < .001). Participants reported a negative impact of the pandemic on relations with coworkers (15.84%), relations with family (27.84%), their emotional and mental well-being (48.51%), research productivity (34.26%), and financial income (52.28%). CONCLUSION The COVID-19 pandemic has adverse effects on various personal and professional aspects of oncologists’ lives. Interventions should be implemented to mitigate the negative impact and prepare oncologists to manage future crises with more efficiency and resilience.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".