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Record W2078036075 · doi:10.1037/1076-8998.11.1.76

Divergent effects of transformational and passive leadership on employee safety.

2006· article· en· W2078036075 on OpenAlexafffund
E. Kevin Kelloway, Jane Mullen, Lori Francis

Bibliographic record

VenueJournal of Occupational Health Psychology · 2006
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Health and Safety Research
Canadian institutionsSaint Mary's University
FundersSocial Sciences and Humanities Research Council of CanadaNova Scotia Health Research Foundation
KeywordsTransformational leadershipPsychologyStructural equation modelingSocial psychologyMultilevel modelPsychological safetyLeadership studiesConfirmatory factor analysisSituational leadership theoryApplied psychologyLeadership styleServant leadershipMathematicsStatistics

Abstract

fetched live from OpenAlex

The authors concurrently examined the impact of safety-specific transformational leadership and safety-specific passive leadership on safety outcomes. First, the authors demonstrated via confirmatory factor analysis that safety-specific transformational leadership and safety-specific passive leadership are empirically distinct constructs. Second, using hierarchical regression, the authors illustrated, contrary to a stated corollary of transformational leadership theory (B. M. Bass, 1997), that passive leadership contributes incrementally to the prediction of organizationally relevant outcomes, in this case safety-related variables, beyond transformational leadership alone. Third, further analyses via structural equation modeling showed that both transformational and passive leadership have opposite effects on safety climate and safety consciousness, and these variables, in turn, predict safety events and injuries. Implications for research and application 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 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)0.007
metaresearch head score (Gemma)0.036
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.039

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.036
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0010.002
Scholarly communication0.0010.001
Open science0.0000.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.165
GPT teacher head0.521
Teacher spread0.356 · 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

Citations399
Published2006
Admission routes2
Has abstractyes

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