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Record W2803452145 · doi:10.1108/gm-11-2016-0178

Women leader/ship development: mindfulness and well-being

2018· article· en· W2803452145 on OpenAlexaff
Teri Phillips, Gina Grandy

Bibliographic record

VenueGender in Management An International Journal · 2018
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicJob Satisfaction and Organizational Behavior
Canadian institutionsUniversity of ReginaMcGill University
Fundersnot available
KeywordsMindfulnessMeditationOriginalityPsychologyValue (mathematics)Leadership developmentMindfulness meditationWell-beingApplied psychologyPsychotherapistEngineering ethicsSocial psychologyPublic relationsPolitical scienceEngineeringCreativityComputer science

Abstract

fetched live from OpenAlex

Purpose The purpose of this paper is to propose mindfulness (e.g. practices such as yoga, meditation) as a fruitful addition to women leader/ship development research and practice, specifically as a means through which to prevent and mitigate negative health outcomes. Design/methodology/approach This is a literature review-based paper that integrates scholarly research on gender and women’s leadership, well-being (including stress reduction), women leader/ship development and mindfulness. Findings Women continue to be under-represented in senior leadership roles and many confront a “labyrinth” of “gender-organization-system” challenges throughout their careers. These challenges can have material effects on the well-being of women leaders. Research reveals positive psychological and biological effects of mindfulness practices on well-being (including stress). Some of this research reveals that women are more likely to engage in mindfulness practices and experience greater benefits. Leader/ship development programs designed specifically for women are needed, and it is proposed that incorporating mindfulness into these programs may result in beneficial outcomes for women leaders. Originality/value This research contributes to the limited body of work on women leader/ship development, mindfulness and leadership.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.134
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.029
GPT teacher head0.263
Teacher spread0.234 · 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 designObservational
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

Citations21
Published2018
Admission routes1
Has abstractyes

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