General Morphological Analysis in Public Health Emergency Management: An Environmental Scan
Bibliographic record
Abstract
Background: Uncertainty is inherent in public health emergency management (PHEM) due to the unpredictable nature of emergencies and interplay of public health threats and their drivers. PHEM practitioners must continually develop and adapt methods to manage this uncertainty. General morphological analysis (GMA) is a computer-aided scenario modelling method that effectively addresses issues where uncertainty exists. GMA examines possible components of a complex problem and allows practitioners to consider potential connections and outcomes. Through iterative steps, GMA can generate new knowledge and insights in the development of scenarios to aid in decision-making and planning within PHEM. Method: An environmental scan was designed to identify articles that utilized GMA as one of the primary methodologies across different natural hazards within the context of PHEM. Academic databases included PubMed and Research Gate. A broad search strategy was applied to scan grey literature which included Google Scholar. Results: This environmental scan identified ten examples of GMA employed in PHEM across multiple countries and organizations. Examples in the literature targeted either a specific natural hazard or broadly targeted all known natural hazards. The findings can be divided into three interconnected categories: (a) scenario modelling for managing natural disasters, (b) strategy development and prioritization tools, and (c) decision-making support tools for emergency management teams. Conclusions: GMA is a decision-making and planning tool in PHEM that can be extended beyond scenario modelling to address uncertainties. This modelling method leverages subject matter experts to uncover innovative connections and outcomes when navigating complex problems like those observed within PHEM. Future research can involve applying GMA to PHEM in a Canadian context. Currently, the Public Health Agency of Canada is applying GMA to cyclical events (e.g., wildfires, floods, extreme heat events, and extreme weather events) to create scenarios using a PHEM lens. Future practice should involve integrating GMA with other PHEM methodologies to enhance strategies to prevent, prepare, respond and recover from future public health emergencies.
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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 | 0.014 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.034 | 0.040 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 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".