Contextualizing pandemics: Respiratory survivorship before, during, and after the 1918 influenza pandemic in Newfoundland
Bibliographic record
Abstract
Abstract Background Research on the 1918 influenza pandemic often focuses exclusively on pandemic years, reducing the potential long‐term insights about the pandemic. It is critical to frame the 1918 pandemic within the underlying population dynamics, health, and sociocultural context to understand what factors contributed to pandemic mortality and survivorship, with respect to observed inequality, and consequences of the pandemic. Materials & Methods Individual death records and censuses from The Rooms Provincial Archives and Memorial University of Newfoundland Digital Archives for three major causes of death—influenza and pneumonia; tuberculosis; and pooled bronchitis, measles, and whooping cough—were collected for three periods in the early 20th century: pre‐pandemic (1909–11), pandemic (March 1918–Janaury 1919), and post‐pandemic (1933–1935). We calculated pooled age‐standardized mortality rates and changes in pre‐ to post‐pandemic mortality rates by region. We fit Kaplan–Meier and Cox proportional hazards models to each period, controlling for age, cause of death, and region. Results Pandemic mortality was higher than that of pre‐ and post‐pandemic periods. Post‐pandemic mortality was significantly lower than pre‐pandemic mortality in all regions, except Western Newfoundland. Survival was lowest during the pandemic and increased significantly post‐pandemic (p < 0.0001), with no significant differences among regions during the pandemic (p = 0.32). Significant differences in survivorship in 1933–1935 were driven by increasing differences in survivorship for P&I among the regions more than other causes of death. Discussion Myopic perspectives of pandemics can obscure our understanding of observed outcomes. Inequalities in respiratory disease mortality are evident in pre‐ and post‐pandemic periods, but these would have been missed in investigations of the pandemic period alone.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".