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Prevention of Bullying at Work

2017· other· en· W2907433083 on OpenAlexaff
Andrea Chambers, Peter Donnelly

Bibliographic record

VenueThe Wiley Handbook of Violence and Aggression · 2017
Typeother
Languageen
FieldSocial Sciences
TopicWorkplace Violence and Bullying
Canadian institutionsUniversity of TorontoPublic Health Ontario
Fundersnot available
KeywordsPsychological interventionWorkplace bullyingCitizen journalismProcess (computing)Work (physics)PsychologyUpstream (networking)Applied psychologyKnowledge managementPublic relationsProcess managementBusinessEngineeringComputer sciencePolitical scienceSocial psychology

Abstract

fetched live from OpenAlex

Abstract There is a need to mitigate upstream factors that contribute to workplace bullying to prevent the far‐reaching consequences that impact individuals, teams, and organizations. In this chapter, we review literature on interventions designed to prevent workplace bullying targeting groups of employees or organizations as a whole and strategies to support implementation. We identified a number of prevention strategies at the team, organizational, and society level; however, there are a number of gaps in available research evidence on effective prevention strategies in this area. Recommendations are provided on the development of comprehensive organizational strategies to address workplace bullying involving a review and development process that breaks down existing processes and conditions that are supporting, precipitating, or enabling workplace bullying and incorporating a participatory approach. We also point to opportunities for further research through the validation of existing measurement instruments and the incorporation of recent advancements in the evaluation of complex interventions.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.910
Threshold uncertainty score0.474

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.024
GPT teacher head0.318
Teacher spread0.294 · 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 designOther design
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

Citations0
Published2017
Admission routes1
Has abstractyes

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