Cardiovascular Physicians, Scientists, and Trainees Balancing Work and Caregiving Responsibilities in the COVID-19 Era: Sex and Race-Based Inequities
Bibliographic record
Abstract
BACKGROUND: The ongoing COVID-19 pandemic has exposed a work-life (im)balance that has been present but not openly discussed in medicine, surgery, and science for decades. The pandemic has exposed inequities in existing institutional structure and policies concerning clinical workload, research productivity, and/or teaching excellence inadvertently privileging those who do not have significant caregiving responsibilities or those who have the resources to pay for their management. METHODS: We sought to identify the challenges facing multidisciplinary faculty and trainees with dependents, and highlight a number of possible strategies to address challenges in work-life (im)balance. RESULTS: To date, there are no Canadian-based data to quantify the physical and mental effect of COVID-19 on health care workers, multidisciplinary faculty, and trainees. As the pandemic evolves, formal strategies should be discussed with an intersectional lens to promote equity in the workforce, including (but not limited to): (1) the inclusion of broad representation (including equal representation of women and other marginalized persons) in institutional-based pandemic response and recovery planning and decision-making; (2) an evaluation (eg, institutional-led survey) of the effect of the pandemic on work-life balance; (3) the establishment of formal dialogue (eg, workshops, training, and media campaigns) to normalize coexistence of work and caregiving responsibilities and to remove stigma of gender roles; (4) a reevaluation of workload and promotion reviews; and (5) the development of formal mentorship programs to support faculty and trainees. CONCLUSIONS: We believe that a multistrategy approach needs to be considered by stakeholders (including policy-makers, institutions, and individuals) to create sustainable working conditions during and beyond this pandemic.
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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 | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".