The ‘Be Prepared’ programme: Enhancing local emergency preparedness through public education in Alberta
Bibliographic record
Abstract
The AEMA, within the Government of Alberta, Canada, is the coordinating agency for emergency management across the province. The Be Prepared programme, within the AEMA, aims to educate individuals, households and communities on how to recognise risks, improve risk literacy and make preparedness a part of daily life. The rising frequency and severity of disasters, which can put lives at risk and disrupt day-to-day life, necessitates increased risk literacy, hazard understanding and disaster risk reduction actions among all sectors of society. Disaster research and post-incident assessments reflect the reality that while many people may recognise the need for disaster preparedness, some may fall short in practice, whether due to lack of resources, knowledge, or a failure to recognise the urgency of risks. The lack of preparedness among the public remains a consistent finding in disaster studies, highlighting the need for improved education and resources to address these gaps. Creating disaster-ready and resilient communities requires a cultural shift towards preparedness. This can be achieved by applying lessons from public relations strategies to influence behaviour change, emphasising frequent and targeted communications using an intersectional lens and equipping local champions with the tools to bring communities together and motivate preparedness action. This paper explores the development and implementation of the Government of Alberta's Be Prepared programme, a cost-effective, non-structural mitigation initiative aimed at enhancing local emergency preparedness through public education. The programme seeks to foster a culture of preparedness by promoting the Be Prepared initiative as a trusted resource for communities to champion. It examines how public relations strategies, such as year-round engagement, communication, partnership building and research-driven evaluation, can support the emergency management sector in encouraging disaster risk reduction behaviours. These strategies collectively form an evidence-based approach to strengthening community resilience and preparedness. This article is also included in The Business & Management Collection which can be accessed at https://hstalks.com/business/.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".