A Better Way to Train Personnel to Be Safe in Emergencies
Bibliographic record
Abstract
Offshore petroleum platforms present complex, time-sensitive situations that can make emergency evacuations difficult to manage. Virtual environments (VE) can train safety-critical tasks and help prepare personnel to respond to real-world offshore emergencies. Before industries can adopt VE training, its utility must be established to ensure the technology provides effective training. This paper presents the results of two experiments that investigated the training utility of VE training. The experiments focused particularly on determining the most appropriate method to deliver offshore emergency egress training using a virtual environment. The first experiment used lecture-based teaching (LBT). The second experiment investigated the utility of a simulation-based mastery learning (SBML) pedagogical method from the medical field to address offshore emergency egress training. Both training programs (LBT and SBML) were used to train naïve participants in basic onboard familiarization and emergency evacuation procedures. This paper discusses the training efficacy of the SBML method in this context and compares the results of the SBML experimental study to the results of the LBT training experiment. Efficacy of the training methods is measured by a combination of time spent training and performance achieved by each of the training groups. Results show that the SBML approach to VE training was more time effective and produced better performance in the emergency scenarios. SBML training can help address individual variability in competence. Limitations to the SBML training are discussed and recommendations to improve the delivery of SBML training are presented. Overall, the results indicate that employing SBML training in industry can improve human reliability during emergencies through increased competence and compliance.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".