Expert Advisory Groups: Exploring the Sensemaking Process During a Public Health Crisis Response
Bibliographic record
Abstract
Crisis sensemaking research has focused mainly on acute crises such as wildfires or industrial accidents, with crisis response being approximately under 72 hours. However, there is limited research on long duration crisis sensemaking for crisis response that may be several weeks, months, or even years. This research study aims to explore long duration crisis sensemaking during a public health crisis. During the crisis response period, key decision makers (KDMs) face a plethora of challenges, including being inundated with information, with varying levels of quality and relevance, or not having the right kind of information. They may rely on an Expert Advisory Group (EAG) to advise on the scientific/medical aspect of the disease. The EAG is comprised of specialists such as infectious disease physicians, infection prevention and control practitioners, epidemiologists, and public health physicians. The 2003 SARS outbreak in Toronto, Canada, was the context for this research. Participants were recruited who served as members of the Ontario SARS Scientific Advisory Committee (OSSAC) or were stakeholders during the crisis. Among their duties, these experts were tasked to write directives (mandated protocols) that govern all aspects of hospital life, from patient transfers, to cleaning. Data was collected in multiple forms, including: public inquiry reports, meeting minutes, newspaper articles, and interviews. Following a constructivist grounded theory strategy, I conducted several iterations of data collection and analysis. The findings include a conceptual framework of EAG social sensemaking through a long duration crisis, depicting the sequential process of a stream of sensemaking (the creation and revision of one directive). A second conceptual framework on the information dynamics of long duration social sensemaking reflects the learning over the course of the crisis period. Finally, a third conceptual framework on the regulation of expert advisory group sensemaking as a balance between the knowns and unknowns in the greater health system is presented.
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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 | 0.093 | 0.131 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.021 | 0.021 |
| Scholarly communication | 0.015 | 0.016 |
| Open science | 0.004 | 0.018 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".