Integrating human factors and prevention systems to improve safe operations and performance
Bibliographic record
Abstract
The high-risk nature of the oil and gas industry means the search for best practices to ensure employees suffer no harm is ongoing. Our industry has undergone multiple-step changes in safety, including the eras of death, engineering, regulations, and, more recently, behaviours. The unifying aspect to expand the results achieved from these step changes is the seamless merging of human factors and prevention systems, which are explored in further detail in this extended abstract. Human factors are how individuals behave physically and psychologically to their work environment. Prevention systems are the equipment, systems, and processes the organisation provides and implements to keep individuals safe in the work environment. The manner in which human factors and prevention systems collaborate delineates Intertek’s processes in safety. To better understand both human factors and prevention systems, the authors analysed their sub-components, ultimately making their use more relevant for the needs of this industry. A consistent understanding of each subsequently allows better identification of where the gaps may exist and allows focus on processes for not only improved business results but also achievement of no harm to employees. Although this extended abstract primarily concentrates on human factors, a means of assessing employees’ abilities to behave in their work environment alongside an organisation’s prevention systems is also discussed.
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 | 0.020 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".