Bibliographic record
Abstract
To the Editor: The COVID-19 pandemic has heightened existing disparities, including the disproportionate burden of pandemic planning that falls on the shoulders of female residents and physicians with young children. Residents working in higher-risk settings have to move away from their family homes and their children. Children and mothers may feel psychological stressors associated with physical separation for an indefinite period of time. These impacts on the psyche cannot be quantified. Also, at a time when most commercial childcare centers are closed, finding alternatives requires effort. Pandemic planning by academic institutions and hospitals should incorporate childcare options for employees and provide ample time for parents to find childcare options when scheduling shifts. It is important to note that female physicians spend 8.5 hours more per week caring for children, caring for elderly parents, and completing other domestic duties compared with male physicians. 1 Although physicians who are pregnant may be exempt from COVID-related duties, those who are breastfeeding may not be and may worry about respiratory transmission due to close contact with their child. A small study from Wuhan, China, did not find SARS-CoV-2 shedding in breast milk, and pumping may be an option. 2 Many hospital sites, however, do not have dedicated pumping areas for employees. In addition, academic physicians face the maternal penalty of reduced research productivity. 3 Academic journals have been seeing a substantial decrease in solo submissions from female scientists since the start of the COVID-19 pandemic, which has implications for career advancement. 4 Further, professional videoconferences throughout the day are not possible while caring for young children at home. Finally, the financial impact of a worsening economy on female physicians’ incomes and retirement security remains understated. 5 Compensation is on average 27.7% lower for female physicians than for their male counterparts. 6 A deteriorating economy will have long-term impacts on retirement plans, asset management, and family planning for women in medicine. Physicians who are mothers to young children feel proud to contribute their skills in taking care of COVID patients. The medical profession should recognize physician mothers who rise to the challenge despite these odds.
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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 | 0.004 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.011 | 0.011 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".