Migrant and refugee populations: a public health and policy perspective on a continuing global crisis
Bibliographic record
Abstract
The 2015-2017 global migratory crisis saw unprecedented numbers of people on the move and tremendous diversity in terms of age, gender and medical requirements. This article focuses on key emerging public health issues around migrant populations and their interactions with host populations. Basic needs and rights of migrants and refugees are not always respected in regard to article 25 of the Universal Declaration of Human Rights and article 23 of the Refugee Convention. These are populations with varying degrees of vulnerability and needs in terms of protection, security, rights, and access to healthcare. Their health status, initially conditioned by the situation at the point of origin, is often jeopardised by adverse conditions along migratory paths and in intermediate and final destination countries. Due to their condition, forcibly displaced migrants and refugees face a triple burden of non-communicable diseases, infectious diseases, and mental health issues. There are specific challenges regarding chronic infectious and neglected tropical diseases, for which awareness in host countries is imperative. Health risks in terms of susceptibility to, and dissemination of, infectious diseases are not unidirectional. The response, including the humanitarian effort, whose aim is to guarantee access to basic needs (food, water and sanitation, healthcare), is gripped with numerous challenges. Evaluation of current policy shows insufficiency regarding the provision of basic needs to migrant populations, even in the countries that do the most. Governments around the world need to rise to the occasion and adopt policies that guarantee universal health coverage, for migrants and refugees, as well as host populations, in accordance with the UN Sustainable Development Goals. An expert consultation was carried out in the form of a pre-conference workshop during the 4th International Conference on Prevention and Infection Control (ICPIC) in Geneva, Switzerland, on 20 June 2017, the United Nations World Refugee Day.
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 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 | 0.014 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.010 | 0.016 |
| Scholarly communication | 0.019 | 0.021 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.028 | 0.025 |
| Insufficient payload (model declined to judge) | 0.011 | 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".