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Record W1962697989 · doi:10.1108/joepp-02-2016-0008

Respect in the workplace: an evaluation of a short online intervention program

2016· article· en· W1962697989 on OpenAlexaff
Shauna L. Smith, E. Kevin Kelloway

Bibliographic record

VenueJournal of Organizational Effectiveness People and Performance · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicWorkplace Violence and Bullying
Canadian institutionsSaint Mary's University
Fundersnot available
KeywordsCivilityIncivilityPsychological interventionIntervention (counseling)Applied psychologyPsychologyTraining (meteorology)OriginalityMedical educationNursingSocial psychologyMedicinePolitical science

Abstract

fetched live from OpenAlex

Purpose The purpose of this paper is to evaluate the effectiveness of a short, online training program focused on respect in the workplace. Design/methodology/approach Using a wait-list control design the authors collected measures of workplace behaviors prior to, immediately following and six weeks following the training from a sample of long-term care employees. Findings The training was associated with a small increase in reported civility. Post hoc subsample analyses suggested that the training was particularly effective in increasing civility and efficacy perceptions among those respondents who had themselves reported engaging in incivility. Practical implications The data offer support for the effectiveness of the training but suggest that more intensive interventions may be necessary. Results also suggest that the effects of training may vary across subgroups of employees. Originality/value Training is one of the most frequently advocated solutions to incivility in the workplace. The findings offer support for a short, online training program.

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)0.006
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.125
Threshold uncertainty score0.198

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.029
GPT teacher head0.361
Teacher spread0.331 · 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 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

Citations18
Published2016
Admission routes1
Has abstractyes

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