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Record W4378981285 · doi:10.18280/ijsse.130208

Safety Leadership, Covid-19 Risk Perception, and Safety Behavior: The Moderator Role of Work Pressure

2023· article· en· W4378981285 on OpenAlexvenueno aff
Ho Y Hiep

Bibliographic record

VenueInternational Journal of Safety and Security Engineering · 2023
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Health and Safety Research
Canadian institutionsnot available
Fundersnot available
KeywordsModerationCoronavirus disease 2019 (COVID-19)PerceptionWork (physics)PsychologyWork safety2019-20 coronavirus outbreakSafety climateOccupational safety and healthSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Risk perceptionRisk analysis (engineering)Applied psychologySocial psychologyEngineeringMedicineMechanical engineeringVirology

Abstract

fetched live from OpenAlex

This research examines how safety leadership, workers' risk perception of Covid-19, safety motivation, and work pressure affect safety compliance and safety participation behaviors.A survey questionnaire was distributed to 967 production workers from eight garment and footwear enterprises in Vietnam in 2021.The data analysis of the survey was analyzed using SPSS and SmartPLS.The results of the Structural Equation Modeling technique indicate that all three safety leadership factors (participative management, safety concern, and safety incentive) & risk perception of Covid-19 have a direct, positive influence on both safety motivation and worker's safety behaviors (safety compliance and safety participation behaviors), except that safety incentive does not predict safety participation behavior.Safety concern has the greatest impact on safety compliance behavior, whereas participative management has been identified as the most important factor affecting safety participation behavior.It was also found that work pressure acted as a moderator in explaining the relationship between safety motivation and worker safety compliance behavior.

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)0.003
metaresearch head score (Gemma)0.001
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 score0.154
Threshold uncertainty score0.509

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.053
GPT teacher head0.391
Teacher spread0.338 · 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

Citations7
Published2023
Admission routes1
Has abstractyes

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