Immigrant Arrival and Tuberculosis among Large Immigrant- and Refugee-Receiving Countries, 2005–2009
Bibliographic record
Abstract
Objective. Tuberculosis control in foreign-born populations is a major public health concern for Australia, Canada, New Zealand, United Kingdom, and the United States, large immigrant- and refugee-receiving countries that comprise the Immigration and Refugee Health Working Group (IRHWG). Identifying and comparing immigration and distribution of foreign-born tuberculosis cases are important for developing targeted and collaborative interventions.Methods. Data stratified by year and country of birth from 2005 to 2009 were received from these five countries. Immigration totals, tuberculosis case totals, and multidrug-resistant tuberculosis (MDR TB) case totals from source countries were analyzed and compared to reveal similarities and differences for each member of the group.Results. Between 2005 and 2009, there were a combined 31,785,002 arrivals, 77,905 tuberculosis cases, and 888 MDR TB cases notified at the federal level in the IRHWG countries. India, China, Vietnam, and the Philippines accounted for 41.4% of the total foreign-born tuberculosis cases and 42.7% of the foreign-born MDR tuberculosis cases to IRHWG.Interpretation. Collaborative efforts across a small number of countries have the potential to yield sizeable gains in tuberculosis control for these large immigrant- and refugee-receiving countries.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".