Risk Evaluate and Control of Volunteer Firefighters Using Group Decision Making
Bibliographic record
Abstract
Risk management encompasses all measures taken before the occurrence of risk events to mitigate potential future losses. Although volunteer firefighters serve in a voluntary capacity, their disaster response duties expose them to various risks similar to those faced by professional firefighters. Furthermore, due to differences in training, auxiliary methods, and physical conditions, they may encounter distinct risk factors and potentially higher disaster risks. This paper explores risk identification, control, and reduction for volunteer firefighters through organizational responsibilities and risk management perspectives, employing a group decision making model. Utilizing the Operational Risk Management Integration Tools (ORMIT) and the modified Delphi method, the analysis identifies 54 major risk factors for Taitung County's volunteer firefighters during disaster response duties. These risk factors are categorized according to the 5M model (Man, Machine, Media, Management, Mission), prioritized and addressed using the Main Operational Risk Management List (MOL) and the Risk Control Option Matrix (COM). After proposing corresponding risk control measures, the Average Risk Index (ARI) for the "Potential Risks in Volunteer Firefighters' Disaster Response Duties" decreased from 14.31 to 5.06. The Average Risk Rating (ARR) improved from a high-risk level (H-7) to a low-risk level (L-16). The results demonstrate that through risk identification, assessment, and control procedures, the potential risks faced by volunteer firefighters in disaster response can be significantly reduced.
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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 | 0.015 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".