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Record W4295788665 · doi:10.32469/10355/91637

Social inequalities and mortality during the 1918 influenza pandemic on the island of Newfoundland

2022· dissertation· en· W4295788665 on OpenAlexaboutno aff
Taylor Van Doren

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicYersinia bacterium, plague, ectoparasites research
Canadian institutionsnot available
Fundersnot available
KeywordsPandemicSocioeconomic statusGeographyInequalityTuberculosisEpidemiological transitionInfectious disease (medical specialty)Influenza pandemicPublic healthSocial inequalityHistoryDiseaseDemographyMedicineSociologyPopulationCoronavirus disease 2019 (COVID-19)

Abstract

fetched live from OpenAlex

Pandemics are anthropogenic events, and an anthropological perspective can be advantageous for a comprehensive understanding of the impact of the 1918 influenza pandemic. Research on the 1918 influenza pandemic has shown that there are considerable socioeconomic differences in risk for infection and mortality, whereby low socioeconomic status individuals with low nutritional levels, lack of access to medical resources, and crowding are at higher risk of adverse health outcomes. Using a combination of vital statistics, death registers, census data, and historical archives from The Rooms Provincial Archives in Newfoundland and Labrador, as well as supplemental historical qualitative archives from the Memorial University of Newfoundland Special Collections and Digital Archives, this dissertation investigates social inequalities and impacts of the 1918 influenza pandemic on the island of Newfoundland, an independent dominion of the British Empire in the early 20th century. This dissertation is comprised of three journal articles. The first article (Chapter 2) is a broad assessment of how anthropology, specifically the integration of biocultural anthropology and epidemiological transition theory, can encourage a more holistic understanding of human infectious diseases. Through a focus on tuberculosis and its contemporaneous co-morbidities, this review article emphasizes the non-mutually exclusive nature of health conditions and shows how diseases like tuberculosis are wholly entangled in human biological evolution, infectious disease dynamics, and demography. The second article (Chapter 3) investigates whether there is support in Newfoundland for the selective mortality hypothesis of tuberculosis and the 1918 influenza pandemic. This article highlights the inequalities on the region level of the island that contributed to overall post-pandemic tuberculosis dynamics, which were not significantly affected by the severe mortality of the 1918 influenza pandemic. Instead, persistently high tuberculosis prevalence and pre-existing health issues linked to nutritional deficiencies are discussed as context for the lack of significant effects of the pandemic. The third article (Chapter 4) contextualizes the mortality and survivorship of (1) influenza and pneumonia, (2) tuberculosis, and (3) bronchitis, measles, and whooping cough during the 1918 influenza pandemic with those of two surrounding time periods: a pre-pandemic period (1909-1911) and a post-pandemic period (1933-1935). This research compares patterns of mortality and survivorship in the urbanizing region of the island with the more isolated, rural regions, and further emphasizes the importance of broadening the temporal depth of pandemic inquiry to better understand impacts and consequences. The ability to place epidemic patterns in a comprehensive historical and anthropological context alongside an understanding of how culture, history, and biology have shaped the modern world and the health of its people will impact strategies for public health preparedness against inevitable future infectious threats.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.054
Threshold uncertainty score0.112

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0030.002
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.035
GPT teacher head0.353
Teacher spread0.318 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2022
Admission routes1
Has abstractyes

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