Bibliographic record
Abstract
Introduction: Tuberculosis (TB) is an infectious disease that causes significant morbidity and mortality. Despite the fact that the total burden of TB has decreased dramatically, the distribution of that burden across the Canadian population has not changed. A century ago, the Indigenous population of Canada had a significantly higher TB mortality than the non-Indigenous population. This gap still exists today. TB is a disease of poverty, and understanding the role of the social determinants of health (SDH) may provide insights into the causes of persistence of TB in the Indigenous population. Research questions: This thesis tackles three questions: 1) Can a TB outbreak that took place over a century ago be reconstructed? 2) What can we learn about the relationship between the disease, the population it afflicted, and the environment in which the outbreak took place? 3) How can reconstruction of a TB outbreak be used to evaluate policy interventions? Area studied: Analyses were limited to the Qu’Appelle Agency, located in Southeastern Saskatchewan. Methodology: An agent-based model of socioeconomic environment of the Qu’Appelle Agency was developed to study the relationship between TB and SDH. Data on TB mortality, demographics, agricultural production, material circumstances, and economic factors of production were used to study the relationship between TB and SDH at the aggregate level. Results: 1) Extensive aggregate data analyses were carried out and an agent-based model of TB transmission and of the socioeconomic environment of the Qu’Appelle Agency was developed. 2) Results of these analyses identify a number of important parameters responsible for the high TB mortality in the Agency. These parameters include biological factors, housing, social characteristics, agricultural output, and policies of the Department of Indian Affairs. Conclusions: This research demonstrates that reconstruction of an outbreak of an infectious disease that took place over a century ago is a complex undertaking that hinges on availability of data and significant expertise in a variety of fields, such as health sciences, economics, mathematics, and modelling approaches. The further one goes into the past, the more one is forced to rely on assumptions, which make the reconstructed web of relationships between agent, host, and environment that caused the outbreak less certain. Despite the inherent uncertainty, the process of outbreak reconstruction provides a deep and multi-faceted understanding of the interactions among the agent, the host, and the environment. The resulting model is a useful way of studying policy interventions that could be applied in other contexts as well – to other infectious diseases or TB outbreaks on other reserves. Keywords: [population health, epidemiology, tuberculosis, Indigenous peoples, agent-based modelling, social determinants of health]
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".