Sex‐ and age‐based differences in mortality during the 1918 influenza pandemic on the island of Newfoundland
Bibliographic record
Abstract
OBJECTIVES: Our aim was to understand sex- and age-based differences in mortality during the 1918 influenza pandemic on the island of Newfoundland. The pandemic's impact on different age groups has been the focus of other research, but sex-based differences in mortality are rarely considered. Aspects of social organization, labor patterns, and social behaviors that contribute to mortality between males and females at all ages are used to explain observed mortality patterns. METHODS: Recorded pneumonia and influenza deaths on the island (n = 1871) were used to calculate cause-specific death rates and to evaluate differences in sex-based mortality. Mortality levels in 17 districts and four regions (Avalon, North, South, and West) were compared using standardized mortality ratios (SMRs). A logistic regression model was fit to determine in which regions sex-based mortality could be predicted using age and region as interactive predictors. RESULTS: Differences in sex-based mortality varied across regions; they were not significant for the aggregate population. SMRs were also variable, with no significant sex-based differences. Sex-based differences were highly variable within regions. Results from a logistic regression analysis suggest that females in the South region may have experienced a higher probability of death than other island residents. CONCLUSIONS: Mortality analysis for aggregate populations homogenizes important epidemiologic patterns. Men and women did not experience the 1918 influenza pandemic in the same way, and by analyzing data at the regional and district geographic levels, patterns emerge that can be explained by the economies and social organization of the people who lived there.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".