Emerging Infectious Disease Outbreak Response: Exploring the Sensemaking Process of an Expert Advisory Group
Bibliographic record
Abstract
ABSTRACT The COVID‐19 pandemic has highlighted the importance of crisis research, particularly in the response phase. This research explores long duration crisis sensemaking of an Expert Advisory Group (EAG) during an emerging disease outbreak which has implications for the conduct of COVID‐19 public inquiries. The members of the Ontario SARS Scientific Advisory Committee (OSSAC) for the 2003 SARS outbreak in Canada, provide the context for this study. Among their duties, these experts were tasked to write directives (mandated protocols) that govern all aspects of hospital life, such as the protocol for transferring SARS patients. Data were collected in multiple forms, including: public inquiry reports, meeting minutes, newspaper articles, and interviews. Following a constructivist grounded theory strategy, several iterations of data collection and analysis were completed. The findings include a conceptual framework that depicts the sensemaking process and illuminates the relationship between retrospective (after an event has occurred) and prospective (future‐oriented) sensemaking.
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 | 0.093 | 0.155 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.017 | 0.017 |
| Scholarly communication | 0.017 | 0.012 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".