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Record W2904443996 · doi:10.3389/fpsyg.2018.02498

Mindfulness as a Buffer of Leaders’ Self-Rated Behavioral Responses to Emotional Exhaustion: A Dual Process Model of Self-Regulation

2018· article· en· W2904443996 on OpenAlexafffund
Megan M. Walsh, Kara A. Arnold

Bibliographic record

VenueFrontiers in Psychology · 2018
Typearticle
Languageen
FieldPsychology
TopicMindfulness and Compassion Interventions
Canadian institutionsMemorial University of NewfoundlandUniversity of Saskatchewan
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsPsychologyMindfulnessProcess (computing)Dual (grammatical number)Social psychologyEmotional regulationCognitive psychologyPsychotherapistDevelopmental psychology

Abstract

fetched live from OpenAlex

In this study we use dual process theory of self-regulation to develop a framework that outlines the mediating and moderating mechanisms explaining the relationship between leader emotional exhaustion and leadership style (transformational leadership and abusive supervision). Using Glomb et al.’s (2011) framework, we identify empathy and negative emotion as mediators that are of particular importance for leaders. In addition, we propose that leader mindfulness moderates these processes to improve leadership style. Using a time-lagged survey of leaders (N = 505) we found that leader empathy and negative emotion mediated the relationships between emotional exhaustion and leadership style. Furthermore, we found that leader mindfulness significantly moderated the indirect effect of leader emotional exhaustion on leadership style through negative emotion. However, leader mindfulness did not moderate the relationship between emotional exhaustion and empathy. Theoretical and practical implications are discussed.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.546
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.059
GPT teacher head0.391
Teacher spread0.333 · 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

Citations22
Published2018
Admission routes2
Has abstractyes

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