A whole community approach to emergency management: Strategies and best practices of seven community programs
Bibliographic record
Abstract
OBJECTIVE: In 2011, the Federal Emergency Management Agency (FEMA) published the Whole Community Approach to Emergency Management: Principles, Themes, and Pathways for Action, outlining the need for increased individual preparedness and more widespread community engagement to enhance the overall resiliency and security of communities. However, there is limited evidence of how to build a whole community approach to emergency management that provides real-world, practical examples and applications. This article reports on the strategies and best practices gleaned from seven community programs fostering a whole community approach to emergency management. DESIGN: The project team engaged in informal conversations with community stakeholders to learn about their programs during routine monitoring activities, site visits, and during an in-person, facilitated workshop. A total of 88 community members associated with the programs examples contributed. Qualitative analysis was conducted. RESULTS: The findings highlighted best practices gleaned from the seven programs that other communities can leverage to build and maintain their own whole community programs. The findings from the programs also support and validate the three principles and six strategic themes outlined by FEMA. CONCLUSIONS: The findings, like the whole community document, highlight the importance of understanding the community, building relationships, empowering action, and fostering social capital to build a whole community approach.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.025 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.011 | 0.008 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.004 | 0.018 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".