Learning from the past, as we look to the future: A new approach for public health leaders responding to pandemics and epidemics
Bibliographic record
Abstract
Humanity has experienced pandemics and epidemics for centuries (Hays, 2009; Loomis, 2018; McNeill, 1998; Waltner-Toews, 2020). In every event, humanity seeks to learn from these experiences (Naylor et al., 2003; PHAC, 2018), but the lessons learned are often forgotten or neglected for those that follow (McLean & Hasham, 2020). This thesis will demonstrate the importance of knowing the history of pandemics and epidemics for public health leaders and what strategic leadership actions can be applied from these events to current and future pandemic and epidemic events. The question to be answered in this research is the following: “What specific strategic leadership actions can public health leaders learn from the influenza pandemic of 1918 in Canada and the United States in responding to contemporary public health crises?” A secondary question is: “What sociocultural environment and social structures/systems in this specific public health crisis of 1918 in Canada and the United States do public health leaders need to account for in learning how to respond to contemporary public health crises?”. This research has used a thematic analysis to evaluate the responses of leaders in the influenza pandemic of 1918. The analysis was built on a historical research method based primarily on the study of secondary published materials and documents related to the 1918 influenza pandemic. The reading of these sources centred on specific strategic actions identified by those in leadership positions during the influenza pandemic of 1918 in both a Canadian and American context. The goal of this dissertation research is to lay the foundation for future work in developing a “pandemic playbook” (Goldschmidt, 2022), which would be based on a further analysis of the actions that various leaders have utilized in other pandemic and epidemic events in history.
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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 | 0.033 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.022 | 0.074 |
| Scholarly communication | 0.027 | 0.044 |
| Open science | 0.004 | 0.016 |
| Research integrity | 0.005 | 0.016 |
| Insufficient payload (model declined to judge) | 0.007 | 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".