Using the functional resonance analysis method (FRAM) to model and analyze lifeboat training in a simulator
Bibliographic record
Abstract
Lifeboat operation is a complex procedure in which safety and rescue are of utmost importance. Training coxswain to perform these operations and acquiring sufficient skills and competencies to face unforeseen risks in harsh weather conditions is challenging. However, lifeboat simulators facilitate the training by removing the risk of training in real environments and improving training courses and trainees' skills. In this study, a Functional Resonance Analysis Method (FRAM) model for launching a lifeboat and on-water tasks was created based on the approved lifeboat training course materials, rubric grading, and lifeboat course scenarios. Two scenarios were used to identify some essential functions in a lifeboat operation. Launch a lifeboat, get away from the platform and drive to a safe zone, pick up Person In Waters (PIWs), recover people from the life raft, tow a life raft, stop by a vessel and transfer the PIW are some tasks covered in this FRAM model. The model was tested with the simulator to identify variabilities in terms of accuracy and time. Five volunteers were asked to perform these scenarios. FRAM signatures of different performances were created to visualize various ways of doing an operation. Successful and unsuccessful operations were monitored using the FRAM, and key elements to having successful and unsuccessful outcomes were determined. Identifying functions and their variations helped to discover where and how trainees act differently in the lifeboat operation. The results of building the FRAM model showed that four categories of functions contributed to lifeboat operation, including action, assessment, decision-making, and skill. The comprehensive model presented in this study enables the researcher to better understand lifeboat operations and helps identify the variations that can affect an operation. Effective processes and key features to diagnose acceptable vs. unacceptable performance extracted by FRAM can be considered a perfect source of observational learning to inform trainees. The FRAM approach used in this study can be employed to determine work practices that are more or less effective, allowing for the adaptation of processes and techniques to steer lifeboat training in the direction of routes that would provide better results.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".