Assessing emergency management training and exercises
Bibliographic record
Abstract
Purpose The purpose of this paper is to investigate how training or exercises are assessed in local government emergency management organisations. Design/methodology/approach An investigative review of the resources available to emergency managers across North America and within New Zealand, for the evaluation and monitoring of emergency management training and exercises was conducted. This was then compared with results from a questionnaire based survey of 48 local government organisations in Canada, USA, and New Zealand. A combination of closed and open ended questions was used, enabling qualitative and quantitative analysis. Findings Each organisation's training program, and their assessment of this training is unique. The monitoring and evaluation aspect of training has been overlooked in some organisations. In addition, those that are using assessment methods are operating in blind faith that these methods are giving an accurate assessment of their training. This study demonstrates that it is largely unknown how effective the training efforts of local government organisations are. Research limitations/implications Further study inspired by this paper will provide a clearer picture of the evaluation of and monitoring of emergency management training programs. These results highlight that organisations need to move away from an ad hoc approach to training design and evaluation, towards a more sophisticated and evidence‐based approach to training needs analysis, design, and evaluation if they are to maximise the benefits of this training. Originality/value This study is the first investigation to the authors’ knowledge into the current use of diverse emergency management training for a range of local government emergency offices, and how this training impacts the functioning of the organisation's emergency operations centre during a crisis.
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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 | 0.011 | 0.067 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".