Coloring the white plague: a syndemic approach to immigrant tuberculosis in Canada
Bibliographic record
Abstract
OBJECTIVE: In this article, we adopt a syndemic approach to immigrant tuberculosis (TB) in Canada as a way of challenging contemporary epidemiological models of infectious diseases that tend to racialize and medicalize the risk of infections in socio-economically disadvantage populations and obscure the role of social conditions in sustaining the unequal distribution of diseases in these populations. DESIGN: A syndemic approach unravels social and biological connections which shape the distribution of infections over space and time and is useful in de-racializing and de-medicalizing these epidemiologic models. The socio-historic framework allows us to examine social factors which, refracted through medical science, were central to the development of TB control in Canada at the beginning of twentieth century. RESULTS: We expose the ideological assumptions about race, immigration, and social status which underpin current policies designed to control TB within the immigrant population. We argue that TB control policies which divert the attention from structural health determinants perpetuate health and social inequities of racialized populations in Canada. Medical screening and surveillance is an ineffective control policy because the proportion of TB cases attributed to immigrants increased from 18 to 66% between 1970 and 2007. CONCLUSION: More effective TB control policies require shifting the focus from the individual disease carriers toward social inequities which underlie the problem of immigrant TB in Canada. In addition, de-racialization and de-medicalization of the contemporary epidemiological models of infectious diseases entail an in-depth exploration of how the categories of ethnicity, culture, and immigration status are played out in everyday health-related experiences of racialized groups.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.018 | 0.008 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".