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Record W4396221850 · doi:10.3390/buildings14051245

How Can Conflicts with Supervisors or Coworkers Affect Construction Workers’ Safety Performance on Site? Two Cross-Sectional Studies in North America

2024· article· en· W4396221850 on OpenAlexaffabout
Yuting Chen, Douglas Hyatt, Arash Shahi, Awad S. Hanna, Mahdi Safa

Bibliographic record

VenueBuildings · 2024
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Health and Safety Research
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsAffect (linguistics)Cross-sectional studyPsychologyEngineeringMedicine

Abstract

fetched live from OpenAlex

A safety plateau in the construction industry has been reported in the US and Canada, which has prompted researchers to seek new factors affecting construction safety performance. Tapping into advancements in the theory of human and organizational behaviors can yield valuable new perspectives. Therefore, by leveraging the advancement of the Job Demand Control Support model in the field of occupational safety and health, this paper firstly tested the impact of one newly added hindrance stressor (i.e., interpersonal conflicts on construction sites) by researchers on organizational behaviors on the safety performance of construction workers, based on two cross-sectional studies in the US and Canada. Differentiations were made between conflicts with supervisors and conflicts with coworkers. One personal resource factor, i.e., individual resilience, was also considered in this paper. A “causal” chain that shows the mitigation impact of individual resilience on conflicts with supervisors or coworkers, and the adverse impact of conflicts with supervisors or coworkers, on unsafe events were found to hold true for both US and Canadian construction sites, based on the results from measurement invariance tests and structural equation modelling. Recommendations regarding how to improve construction workers’ individual resilience and reduce interpersonal conflicts on site, thereby reducing safety incidents on site, are provided.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.030
Threshold uncertainty score0.826

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.119
GPT teacher head0.460
Teacher spread0.341 · 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

Citations4
Published2024
Admission routes2
Has abstractyes

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