High Safety Risk Assessment in the Time of Uncertainties (COVID-19): An Industrial Context
Bibliographic record
Abstract
Background: The complexities of the workplace environment in the downstream oil and gas industry contain several safety-risk factors. In particular, instituting stringent safety standards and management procedures are considered insufficient to address workplace safety risks. Most accident cases attribute to unsafe actions and human behaviors on the job, which raises serious concerns for safety professionals from physical to psychological particularly when the world is facing a life-threatening Pandemic situation, i.e., COVID-19. It is imperative to re-examine the safety management of facilities and employees' well-being in the downstream oil and gas production sector to establish a sustainable governance system. Understanding the inherent factors better that contribute to safety behavior management could significantly improve workplace safety features. Objective: This study investigates employees' safety behavior management model for the downstream oil and gas industry to consolidate the safety, health and wellbeing of employees in times of COVID-19. Methods: Nominal Group Technique (NGT) was first employed to screen primary behavioral factors from 10 workplace health and safety experts from Malaysia's downstream oil and gas industry. Consequently, 18 significant factors were identified for further inquiry. Next, the interpretive structural modeling technique was used to ascertain the complex interrelationships between these factors and proposed a Safety Behavioral Management Model for cleaner production. Results: This model shows that management commitment, employee knowledge and training, leadership, and regulations contribute significantly to several latent factors. Our findings support the Social Cognitive Theory, where employees, their environment, and their behaviors are related reciprocally. Conclusion: It is postulated that identifying safety factors and utilizing the proposed model guides various stakeholder groups in this industry, including practitioners and policymakers, for achieving long-term sustainability.
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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 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".