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Record W7030018320

Lived Experiences of Female Executives Leading During the COVID-19 Pandemic

2023· article· en· W7030018320 on OpenAlexaboutno aff

Bibliographic record

VenueScholarWorks (Walden University) · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicGender Diversity and Inequality
Canadian institutionsnot available
Fundersnot available
KeywordsPandemicLived experiencePrejudice (legal term)Qualitative researchQuarter (Canadian coin)Conceptual frameworkWork (physics)Coronavirus disease 2019 (COVID-19)
DOInot available

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.273
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0020.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.190
GPT teacher head0.333
Teacher spread0.143 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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