Impact of the COVID‐19 Pandemic Onset on the Early Careers of Pediatric Oncology Health Professionals and Researchers: A Report From the Children's Oncology Group Young Investigators Committee, Young SIOP Network, and Young SIOPE
Bibliographic record
Abstract
INTRODUCTION: The COVID-19 pandemic onset had a global debilitating impact on individuals and on burgeoning careers. In 2021, the Children's Oncology Group Young Investigators Committee, Young SIOP (International Society of Paediatric Oncology) Network, and Young SIOPE (European Society for Paediatric Oncology) co-sponsored a survey to explore the impacts of the first year of the pandemic on early-career pediatric oncology professionals with respect to working practices, productivity, professional and career development, personal wellbeing, and changing childcare needs. METHODS: The survey comprised demographic, multiple-choice, and free-text questions, and was distributed via email and social media with English, French, and Spanish versions available. Descriptive statistics and chi-square tests were used to compare quantitative data by self-designated gender and country of origin. Qualitative data were described using content analysis. RESULTS: Professionals (N = 499, 26.3% male, 77.2% MDs) in 48 countries (77.6% high income) responded in English (79.4%), Spanish (12.4%), and French (8.2%). Respondents had difficulty obtaining and keeping jobs (26.9%), worsened overall academic productivity (50.7%, with higher rates among bench scientists, p < 0.01), and decreased career opportunities (40.9%). Childcare challenges impacted 56.7% of respondents and was felt more negatively among women (p = 0.008) and in high-income settings (p < 0.0001). Qualitative data (n = 300) highlighted these differences were often attributable to diminished professional/personal boundaries and impacted their personal wellbeing. CONCLUSION: The COVID-19 pandemic significantly impacted early-career academic and clinical professionals working in pediatric oncology, with unique challenges noted among those with childcare responsibilities. Career disruptions that resulted from the pandemic should be considered and mitigated by governing bodies and hiring institutions.
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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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".