Hazard Identification and Risk Assessment : Analysis of a Risk Assessment Process in Emergency Preparedness
Bibliographic record
Abstract
A challenging yet crucial component of emergency planning is to identify relevant hazards and assess their risk level. Within the Province of Ontario, Canada, governmental emergency management stakeholders are required to use the Hazard Identification and Risk Assessment (HIRA) process, developed by the Province, to meet legislative compliance. The HIRA process is based on the use of risk matrices and hence faces many of the inherent challenges of this method, potentially resulting in a poor risk assessment process with a low quality outcome. The aim of this thesis is to analyze Ontario’s Provincial HIRA process to identify weaknesses, strengths, and gaps, in order to increase understanding for potential issues related to this type of hazard identification and risk assessment process within emergency preparedness. The Provincial HIRA process will be analyzed, as it is implemented in the Regional Municipality of York, including the Public Health Unit, by comparing the process to six points identified in the literature as potential challenges with the ability to compromise the quality of a risk assessment process. The main focus is on the use of risk matrices although some aspects more generally related to risk assessments have been included. Overall the Provincial HIRA has several weaknesses and gaps. It is evident that the process demonstrates many of the issues that impair the quality of risk assessments supported by the use of risk matrices such as ambiguous input and out-puts, errors, poor resolution and sub-optimal resource allocation. Additionally, a significant amount of resources and access to hazard subject matter expertise would be required to execute the HIRA in accordance with the guideline. Such resources are not necessarily available to the target audience. All these aspects contribute to a risk assessment process that struggles to meet one of its main objectives, to provide the user with a quantitative risk ranking with the capacity to distinguish between risk levels of different hazards. Subsequently the outcome may not accurately support the emergency planning or the decision making process related to resource allocation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".