The roles of emergency managers and emergency social services directors to support disaster risk reduction in Canada
Bibliographic record
Abstract
Emergency managers (EMs) and Emergency Social Services Directors (ESSDs) are essential service providers who fulfill critical roles in disaster risk reduction. Despite being positioned throughout all levels of government, and in the private sector, EMs and ESSDs fulfill roles which occur largely behind the scenes. The purpose of this phenomenological study was to explore the roles of EMs and ESSDs from different regions across Canada. Specifically, we wanted to understand their perceptions of barriers, vulnerabilities and capabilities within the context of their roles. EMs (n = 15) and ESSDs (n = 6) from six Canadian provinces participated in semi-structured telephone interviews. Through content analysis, five themes and one model were generated from the data: 1) Emergency management is not synonymous with first response, 2) Unrealistic expectations for a "side-of-desk" role, 3) Minding the gap between academia and practice with a 'whole-society' approach, 4) Personal preparedness tends to be weak, 5) Behind the scenes roles can have mental health implications. We present a model, based on these themes, which makes explicit the occupational risks that EMs and ESSDs may encounter in carrying out the skills, tasks, and roles of their jobs. Identification of occupational risks is a first step towards reducing vulnerabilities and supporting capability. This is particularly relevant in our current society as increased demands placed on these professionals coincides with the increasing frequency and severity of natural disasters due to climate change and the emergence of the world wide COVID-19 pandemic.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.031 | 0.007 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".