A rapid review of best practices in the development of risk registers for public health emergency management
Bibliographic record
Abstract
Introduction: Public health organizations (PHO) must prepare to respond to a range of emergencies. This represents an ongoing challenge in an increasingly connected world, where the scope, complexity, and diversity of public health threats (PHT) have expanded, as exemplified by the COVID-19 pandemic. Risk registers (RR) offer a framework for identifying and managing threats, which can be employed by PHOs to better identify and characterize health threats. The aim of this review is to establish best practices (BP) for the development of RRs within Public Health Emergency Management (PHEM). Methods: In partnership with a librarian from Health Canada (HC), and guided by the Cochrane Rapid Review Guideline, journal articles were retrieved through MEDLINE, and a comprehensive search strategy was applied to obtain grey literature through various databases. Articles were limited to those that met the following criteria: published on or after January 1, 2010, published in the English language and published within an Organisation for Economic Co-operation and Development setting. Results: 57 articles were included for synthesis. 41 papers specifically discussed the design of RRs. The review identified several guidelines to establish RRs in PHEM, including forward-looking, multidisciplinary, transparent, fit-for-purpose, and utilizing a systems approach to analyze and prioritize threats. Expert consultations, literature reviews, and prioritization methods such as multi-criteria-decision-analysis (MCDA) are often used to support the development of RRs. A minimum five-year-outlook is applied to assess PHTs, which are revisited yearly, and iteratively revised as new knowledge arises. Discussion: Based upon this review, RRs offer a systems approach to PHEM that can be expanded to facilitate the analysis of disparate threats. These approaches should factor in the multidimensionality of threats, need for multi-sectoral inputs, and use of vulnerability analyses that consider inherent drivers. Further research is needed to understand how drivers modify threats. The BPs and recommendations highlighted in our research can be adopted in the practice of PHEM to characterize the public health (PH) risk environment at a given point in time and support PHOs policy and decision-making.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.050 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".