Bibliographic record
Abstract
The pandemic of coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), poses serious challenges to the Canadian Armed Forces (CAF). They are not, however, novel challenges, as the CAF weathered an almost identical situation in its history. This article presents new research findings on the 1918–1919 H1N1 influenza A pandemic’s effect upon the Canadian Corps (Cdn Corps) fighting in France and Belgium during the Hundred Days Campaign of 1918. This was a quantitative and qualitative study, randomly sampling 685 cases from the 5,542 Cdn Corps deaths between September 15 and November 11, 1918. Of these 685, 44 were killed by influenza or pneumonia (6.4%), suggesting with the margin of error that between 260 and 450 Canadians died of influenza in France, a comparatively low number considering the Corps was undertaking high-intensity operations in a region of France badly afflicted with influenza. Qualitative population traits are assessed. Among many important elements that may have bearing upon COVID-19 response, one in particular is drawn out for discussion: non-pharmaceutical interventions in the light of effective leadership. This study ultimately makes recommendations for how the experience of the 1918–1919 pandemic on Canadian fighting formations can inform force health protection (FHP) measures in 2020.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.006 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".