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Abusive Managers/Supervisors' Impact on the Psychological Capital of Employees

2022· book-chapter· en· W4281695482 on OpenAlexaff
Jason Walker, Deborah Circo, DaLissa Alzner, Erica Bearss, Laura G. Stephenson

Bibliographic record

VenueAdvances in human resources management and organizational development book series · 2022
Typebook-chapter
Languageen
FieldSocial Sciences
TopicWorkplace Violence and Bullying
Canadian institutionsYorkville UniversityUniversity Canada West
Fundersnot available
KeywordsIncivilityWorkplace violenceWorkplace bullyingHarassmentMobbingPsychologyAngerMental healthInterpersonal communicationIntervention (counseling)PsychosocialSocial psychologyClinical psychologySuicide preventionPoison controlPsychiatryMedicineMedical emergency

Abstract

fetched live from OpenAlex

Workplace bullying is a severe, violent, and pervasive issue present across the industry worldwide. Typically defined as interpersonal mistreatment that is more severe than incivility, it is a complex, deliberate, and maladaptive group of harmful actions towards individuals and creates oppressive work environments. Bullying can range from derogatory comments towards a target to social isolation and physical violence. Individuals typically evolve into the perpetrator role due to low self-esteem, dark personality traits, and anger management difficulties. Harassment and abusive incivility by managers and supervisors directed towards employees are associated with severe adverse and long-term outcomes, including psychological trauma, mental health disorders, and in extreme cases, suicide. The prevalence of workplace bullying ranges between 10-20% in multiple domains and cultures. When considering the economic, psychological, and health costs of incivility in the workplace, the epidemic of workplace bullying requires comprehensive prevention, intervention, and postvention strategies.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.020
Threshold uncertainty score0.065

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0200.002

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 head0.013
GPT teacher head0.271
Teacher spread0.258 · 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 source (direct Gemma or distilled Codex), not a consensus.

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

Citations6
Published2022
Admission routes1
Has abstractyes

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Same venueAdvances in human resources management and organizational development book seriesSame topicWorkplace Violence and BullyingFrench-language works237,207