Lived Experiences of Female Executives Leading During the COVID-19 Pandemic
Bibliographic record
Abstract
AbstractThis basic qualitative study was conducted to explore the authentic leadership experiences of women as they navigated through the COVID-19 pandemic, including the challenges they faced, the decisions they made, the lessons learned, and what they would do differently in the future. The conceptual framework used in this study was the role congruity theory of prejudice toward female leaders and humanistic motivation theory, which were used to examine the gender disparities that pose challenges for female leaders. The research involved conducting online conference interviews with nine individuals. Volunteers met the following selection criteria: identified as a female with five or more years of experience at the executive level, with titles equivalent in responsibility to Director, Controller, Assistant Vice President, Vice President, up to C-Suite, and responsible for a minimum of five direct reports; and were executive women in a U.S. organization that was not part of a health care system during the COVID-19 pandemic through 3rd quarter or more. The interviews were analyzed through coding and the development of themes. The study’s findings included four major themes: crisis leadership, fear of the unknown, empathy, and work and life balance. The study’s insights may be used to promote positive social change through increased opportunities for women to assume leadership roles in diverse industry organizations, creating a more balanced and equitable professional landscape.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".