Reducing the burden of TB among migrants to low TB incidence countries
Bibliographic record
Abstract
BACKGROUND: International migrants to low TB incidence countries are disproportionately affected by TB compared to the native population: migrants are at increased risk for TB transmission and TB disease due to a variety of personal, environmental and socio-economic determinants experienced during the four phases of migration (pre-departure, transit, arrival and early settlement, return travel).OBJECTIVE: To provide an up-to-date overview of the determinants that drive the TB burden among migrants, as well as effective and feasible interventions to address this for each migration phase.METHODS: We conducted a literature review by searching PubMed and the grey literature for articles and reports on determinants and interventions addressing migrant health and TB.RESULTS: Lowering the risk of TB transmission and TB disease among migrants would be most effective by improving the socio-economic position of migrants pre-, during and after migration, ensuring universal health coverage, and providing tailored and migrant-sensitive care and prevention activities.CONCLUSION: In addition to migrant-sensitive health services and cross-border collaboration between low TB incidence countries, there is a need for international financial and technical support for endemic 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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".