Evidence map›Paper›PMID 40726916›Full record

ReviewMethodsX2025

Investigation into the determinants of occupational safety for achieving zero accidents in multi-storey building construction projects: A Bayesian belief network approach.

Ryan Pramanda, Sri Gunani Partiwi, Adithya Sudiarno, Hamdani

Abstract readReview
In one paragraph

Review in MethodsX, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Ryan PramandaDepartment of Industrial and System Engineering, Institut Teknologi Sepuluh Nopember, Surabaya 60111 Indonesia.
Sri Gunani PartiwiDepartment of Industrial and System Engineering, Institut Teknologi Sepuluh Nopember, Surabaya 60111 Indonesia.
Adithya SudiarnoDepartment of Industrial and System Engineering, Institut Teknologi Sepuluh Nopember, Surabaya 60111 Indonesia.
HamdaniDepartment of Industrial and System Engineering, Institut Teknologi Sepuluh Nopember, Surabaya 60111 Indonesia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The Indonesian construction sector is vital for economic development but faces high accident rates because of inadequate occupational health and safety (OHS) measures. This study examines causal factors in high-rise construction accidents, focusing on human factors, organization, work environment, and payment systems. A Bayesian Belief Network (BBN) was used to evaluate OHS implementation and identify key elements for achieving zero accidents. Data were collected through a structured questionnaire completed by 28 experts assessing 277 workers across 33 critical safety variable relationships. Analysis using RStudio's bnlearn revealed that regulatory requirements strongly influence safety design, corporate culture, and OHS management, while overly complex safety designs hinder supervision and training. Findings show that workers' physical and mental conditions are vital for zero accidents, whereas safety awareness alone is insufficient. Improved training enhances payment systems, boosting motivation and productivity. The study recommends simplifying safety design and refining OHS procedures to reduce accident risks. The main advantages of the proposed method are:•Using Bayesian Belief Network to analyse work accidents at high-rise projects.•Examined the human, organisational, work environment, and payment system aspects.•Found that simplifying safety design and improving the training are important for achieving zero accidents.

Indexed as

Bayesian belief networkConstruction projectWork accidentWork safetyZero accident

Identifiers

PMID40726916
PMCPMC12301787

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.