Learning Lessons during Recovery from Disasters
Bibliographic record
Abstract
Introduction: The Fort McMurray Alberta wildfire was one of Canada’s largest natural disasters in history, burning 589,995 hectares of land until being controlled on July 5, 2016. In responding to the fire, Alberta Health Services (AHS) prompted a province-wide coordinated response. Through a combination of pre-emptive strategies and responsive activities, the AHS response has been considered a success. Underlying the successful response is the collective experiences and contextual knowledge of AHS staff members acquired from past events. While the frequency and severity of risks associated with extreme weather and climate change are increasing worldwide, there is a persistent knowledge gap in the evidence-base informing public health emergency preparedness. It is imperative that lessons learned from past events inform future preparedness activities. Learning lessons is a systematic implementation process that can be used to inform future responses and best practices that are transferable to similar situations. Aim: To describe strategies employed and challenges encountered during recovery after the Alberta wildfires. Methods: A single-case study approach was employed to understand the AHS method to “learning lessons,” and the process involved in translating lessons into actionable goals. Semi-structured interviews with senior leaders (n=11) were conducted and internal documents were obtained. Results: The analysis revealed a strategic learning process, including debriefs, staff surveys, interviews, and member validity checking. The implementation process used to translate the lessons identified included a project management framework, evaluation techniques, and the utilization of tacit and explicit knowledge. Key challenges for implementation involve clarification of processes, leadership commitment, resource and time constraints, staff turn-over, and measuring outcomes. Discussion: Translating the lessons from the Alberta wildfires is crucial for enhancing preparedness, and exploratory research in this area can contribute to building a program of research in evaluation during disaster recovery.
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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".