Measuring the Causes of Saudi Arabian Construction Accidents: Management and Concerns
Bibliographic record
Abstract
Introduction: Research indicate that construction site accidents are a global concern, and rates are rapidly increasing. In developing countries such as Saudi Arabia, safety issues are frequently ignored, and little is known about their causes. Objectives: This study aimed to shed light on factors causing accidents in Saudi Arabian construction companies. Methods: An online detailed survey, using Google Form, of accident features was distributed randomly to potential employees in 35 construction companies in Saudi Arabia, where one of the top administrators or safety officers were required to respond to the survey. It was conducted from 1st June to 31st August, 2013. The safety practices and perceptions of accident causes were assessed. Results: The response rate was 63%. Over half of the surveyed organizations encountered all of the selected accident types. While 19 (86%) of the construction companies maintained the equipment regularly, 15 (68%) had regular maintenance staff and 13 (59%) inspected the equipment before use. Although 18 (82%) of the workers were supplied with personal protective equipment (PPE), only 12 (55%) emphasized its use and offered site orientation for new employees. In the last part of the survey, respondents were requested to rate 25 factors affecting safety performance at the construction sites on a scale of 1 to 5, with 5 being the most important. The three most important factors of poor safety performance were the firm's top leaders, a lack of training, and the reckless operation of equipment. Conclusion: Changing attitudes of surrounding safety culture have the potential to significantly improve safety outcomes in the Saudi Arabian construction industry. Two Saudi Arabian corporations, Saudi Aramco and Saudi Chevron Petrochemical provide a positive model for increasing construction safety in the country, but there is a paucity of industry-level data. Further scholarly attention is strongly indicated.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".