Migrants in a globalized world — health threats, risks and challenges: an evidence-based framework
Bibliographic record
Abstract
International population mobility is an underlying factor in the emergence of public health threats and risks that must be managed globally. These risks are often related, but not limited, to transmissible pathogens. Mobile populations can link zones of disease emergence to lowprevalence or nonendemic areas through rapid or high-volume international movements, or both. Against this background of human movement, other global processes such as economics, trade, transportation, environment and climate change, as well as civil security influence the health impacts of disease emergence. Concurrently, global information systems, together with regulatory frameworks for disease surveillance and reporting, affect organizational and public awareness of events of potential public health significance. International regulations directed at disease mitigation and control have not kept pace with the growing challenges associated with the volume, speed, diversity, and disparity of modern patterns of human movement. The thesis that human population mobility is itself a major determinant of global public health is supported in this article by review of the published literature from the perspective of determinants of health (such as genetics/biology, behavior, environment, and socioeconomics), population-based disease prevalence differences, existing national and international health policies and regulations, as well as inter-regional shifts in population demographics and health outcomes. This paper highlights some of the emerging threats and risks to public health, identifies gaps in existing frameworks to manage health issues associated with migration, and suggests changes in approach to population mobility, globalization, and public health. The proposed integrated approach includes a broad spectrum of stakeholders ranging from individual health-care providers to policy makers and international organizations that are primarily involved in global health management, or are influenced by global health events.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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 teacher head, 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".