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Record W2604716893 · doi:10.1108/jhom-12-2016-0243

Gender bias in hospital leadership: a qualitative study on the experiences of women CEOs

2017· article· en· W2604716893 on OpenAlexaffabout
Sophie Soklaridis, Ayelet Kuper, Cynthia Whitehead, Genevieve Ferguson, Valerie H. Taylor, Catherine Zahn

Bibliographic record

VenueJournal of Health Organization and Management · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicGender Diversity and Inequality
Canadian institutionsThe Wilson CentreUniversity Health NetworkWomen's College HospitalUniversity of TorontoCentre for Addiction and Mental Health
Fundersnot available
KeywordsAmbivalenceOriginalityQualitative researchPsychologySocial psychologyValue (mathematics)FeminismGender studiesSociologySocial science

Abstract

fetched live from OpenAlex

Purpose The purpose of this paper is to examine the experiences of gender bias among women hospital CEOs and explore to what these female leaders attribute their success within a male-dominated hospital executive leadership milieu. Design/methodology/approach This qualitative study involved 12 women hospital CEOs from across Ontario, Canada. Purposeful sampling techniques and in-depth qualitative interview methods were used to facilitate discussion around experiences of gender and leadership. Findings Responses fell into two groups: the first group represented the statement "Gender inequality is alive and well". The second group reflected the statement "Gender inequity is not significant, did not happen to me, and things are better now". This group contained a sub-group with no consciousness of systemic discrimination and that claimed having no gendered experiences in their leadership journey. The first group described gender issues in various contexts, from the individual to the systemic. The second group was ambivalent about gender as a factor impacting leadership trajectories. Originality/value Representations of women's leadership have become detached from feminism, with major consequences for women. This study reveals how difficult it is for some women CEOs to identify gender bias. The subtle everyday norms and practices within the workplace make it difficult to name and explain gender bias explicitly and may explain the challenges in understanding how it might affect a woman's career path.

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)0.005
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.092
Threshold uncertainty score0.468

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.346
GPT teacher head0.412
Teacher spread0.066 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations34
Published2017
Admission routes2
Has abstractyes

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