Enhancing Safety Culture Among Subcontractors to Improve Safety Performance in the Indonesian Construction Industry
Bibliographic record
Abstract
The construction industry is one of the largest sectors in Indonesia, playing a pivotal role in the country's economic growth.However, it faces significant challenges related to high occupational risks, as demonstrated by the persistently high rate of construction accidents.This ongoing issue reflects the low maturity of safety culture within construction companies, including subcontractors.Insufficient attention to safety culture is a major contributing factor to these accidents, highlighting the need for comprehensive solutions to create safer and more productive work environments.In response to this challenge, this study seeks to validate and analyze the factors influencing construction safety culture among subcontractors to improve safety performance.The validation process employed the Delphi Method, involving construction experts to reach a consensus on key factors.The study successfully identified 27 factors that significantly contribute to the development of construction safety culture.These findings provide a critical foundation for developing strategies to enhance safety culture, particularly in Indonesia.Furthermore, the identified factors cover various aspects, such as managerial roles, worker characteristics, work methods, and safety management, all of which play a crucial role in shaping safety culture on construction sites.The implications of this study are significant for improving subcontractor safety performance.By understanding these factors, construction management can implement more effective strategies to reduce occupational accident risks, enhance compliance with safety standards, and foster safer and more sustainable work environments.The results of this study are expected to serve as a valuable reference for improving subcontractor's construction safety performance in Indonesia.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".