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Record W4402863018 · doi:10.1037/apl0001239

Understanding the impact of witnessed workplace mistreatment: A meta-analysis of observer deontic reactions and employee outcomes.

2024· review· en· W4402863018 on OpenAlexafffund
Zhanna Lyubykh, Rui Zhong, The Ton Vuong, Sandra L. Robinson, M. Sandy Hershcovis

Bibliographic record

VenueJournal of Applied Psychology · 2024
Typereview
Languageen
FieldSocial Sciences
TopicWorkplace Violence and Bullying
Canadian institutionsUniversity of British ColumbiaUniversity of CalgarySimon Fraser University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsDeontic logicPsychologySocial psychologyMeta-analysisObserver (physics)Applied psychologyCognitive psychologyEpistemology

Abstract

fetched live from OpenAlex

This meta-analysis aims to understand the impact of witnessed workplace mistreatment. Bringing together two streams of research, it examines (a) the boundary conditions of observer reactions that reflect a principled moral disapproval of violations of interpersonal justice (i.e., deontic reactions) and (b) the extent to which witnessed mistreatment explains incremental variance in a range of employee outcomes beyond the effects of experienced mistreatment. The results demonstrate that observer psychological and behavioral deontic reactions are not straightforward. For example, while observers have negative reactions toward perpetrators, they fail to intervene and have mixed reactions toward targets. Findings from a series of moderator analyses illuminate the role of perpetrator rank, mistreatment severity, and study context in explaining these disparate observer deontic reactions. Further, although experienced mistreatment explains more variance in most employee outcomes than witnessed mistreatment, witnessed mistreatment still has a unique and sizable contribution. The implications of these findings and future directions for research on witnessed mistreatment are discussed. (PsycInfo Database Record (c) 2025 APA, all rights reserved).

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)0.002
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: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.049
Threshold uncertainty score0.634

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.004
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.403
GPT teacher head0.494
Teacher spread0.090 · 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 designMeta-analysis
Domainnot available
GenreReview

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

Citations11
Published2024
Admission routes2
Has abstractyes

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