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Record W2614981839 · doi:10.5465/ambpp.2015.58

Safety Climate on Safety: The Mediating Role of Management Commitment

2015· article· en· W2614981839 on OpenAlexaff
Madelynn Stackhouse, Joanna M. McDouall

Bibliographic record

VenueAcademy of Management Proceedings · 2015
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Health and Safety Research
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsOperationalizationSafety cultureDisengagement theoryPsychologyApplied psychologySample (material)Social psychologyManagementMedicine

Abstract

fetched live from OpenAlex

Past research on safety climate has tended to focus on measurement and operationalization issues or predictive validity (Zohar, 2010). The present research furthers this work by investigating the interplay between different facets of safety climate on safety effectiveness. In a sample of oil and gas workers (N = 263) we show that the relationship between several distal safety climate variables (training effectiveness, procedure effectiveness, and work pressure) on safety effectiveness is partly mediated by management commitment (study 1). In study 2, we replicate these findings in a sample of railway construction workers (N = 167). We further extend these findings to show a boundary condition for this indirect path; perceptions of co-worker disengagement. When perceptions of co-worker disengagement are high the negative effect of low management commitment on safety effectiveness is diminished. Study 3 further explores the key role of management commitment to safety by conducting qualitative focus group interviews in oil and gas organizations (N = 49 participants) to delve into safety culture. Analyses suggest that employee sensemaking regarding safety are perceived through a lens of low management commitment to safety operationalized as management blame. Taken together, these findings highlight the role of tacit management-oriented climate perceptions as key to explaining the role of other distal safety climate factors for predicting safety effectiveness.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.922
Threshold uncertainty score0.683

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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.086
GPT teacher head0.431
Teacher spread0.345 · 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 designTheoretical or conceptual
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

Citations1
Published2015
Admission routes1
Has abstractyes

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