Algorithmic assignment of refugees and asylum seekers to locations within
host countries has gained attention in recent years, with implementations in
the US and Switzerland. These approaches use data on past arrivals to generate
machine learning models that can be used (along with assignment algorithms) to
match families to locations, with the goal of maximizing a policy-relevant
integration outcome such as employment status after a certain duration.
Existing implementations and research train models to predict the policy
outcome directly, and use these predictions in the assignment procedure.
However, the merits of this approach, particularly in non-stationary settings,
has not been previously explored. This study proposes and compares three
different modeling strategies: the standard approach described above, an
approach that uses newer data and proxy outcomes, and a hybrid approach. We
show that the hybrid approach is robust to both distribution shift and weak
proxy relationships -- the failure points of the other two methods,
respectively. We compare these approaches empirically using data on asylum
seekers in the Netherlands. Surprisingly, we find that both the proxy and
hybrid approaches out-perform the standard approach in practice. These insights
support the development of a real-world recommendation tool currently used by
NGOs and government agencies.

本文提出和比较了三种不同的建模策略：标准方法、利用新数据和代理结果的方法以及混合方法，发现混合方法对于分布转移和弱代理关系具有鲁棒性，且实际上超越了标准方法和基于代理关系的方法。

建立强韧模型的难民安置中随机分布漂移策略

Random Distribution Shift in Refugee Placement: Strategies for Building  Robust Models

Background: The use of machine learning (ML) in mental health (MH) research
is increasing, especially as new, more complex data types become available to
analyze. By systematically examining the published literature, this review aims
to uncover potential gaps in the current use of ML to study MH in vulnerable
populations of immigrants, refugees, migrants, and racial and ethnic
minorities.
Methods: In this systematic review, we queried Google Scholar for ML-related
terms, MH-related terms, and a population of a focus search term strung
together with Boolean operators. Backward reference searching was also
conducted. Included peer-reviewed studies reported using a method or
application of ML in an MH context and focused on the populations of interest.
We did not have date cutoffs. Publications were excluded if they were narrative
or did not exclusively focus on a minority population from the respective
country. Data including study context, the focus of mental healthcare, sample,
data type, type of ML algorithm used, and algorithm performance was extracted
from each.
Results: Our search strategies resulted in 67,410 listed articles from Google
Scholar. Ultimately, 12 were included. All the articles were published within
the last 6 years, and half of them studied populations within the US. Most
reviewed studies used supervised learning to explain or predict MH outcomes.
Some publications used up to 16 models to determine the best predictive power.
Almost half of the included publications did not discuss their cross-validation
method.
Conclusions: The included studies provide proof-of-concept for the potential
use of ML algorithms to address MH concerns in these special populations, few
as they may be. Our systematic review finds that the clinical application of
these models for classifying and predicting MH disorders is still under
development.

研究通过系统地检查已发表的文献，旨在发现在使用机器学习来研究移民、难民、移民和少数族裔的心理健康中存在的潜在差距。

机器学习在研究移民与少数民族心理健康方面的应用：一项系统综述

Machine Learning Applications in Studying Mental Health Among Immigrants  and Racial and Ethnic Minorities: A Systematic Review

The war in Ukraine seems to have positively changed the attitude toward the
critical societal topic of migration in Europe -- at least towards refugees
from Ukraine. We investigate whether this impression is substantiated by how
the topic is reflected in online news and social media, thus linking the
representation of the issue on the Web to its perception in society. For this
purpose, we combine and adapt leading-edge automatic text processing for a
novel multilingual stance detection approach. Starting from 5.5M Twitter posts
published by 565 European news outlets in one year, beginning September 2021,
plus replies, we perform a multilingual analysis of migration-related media
coverage and associated social media interaction for Europe and selected
European countries.
The results of our analysis show that there is actually a reframing of the
discussion illustrated by the terminology change, e.g., from "migrant" to
"refugee", often even accentuated with phrases such as "real refugees".
However, concerning a stance shift in public perception, the picture is more
diverse than expected. All analyzed cases show a noticeable temporal stance
shift around the start of the war in Ukraine. Still, there are apparent
national differences in the size and stability of this shift.

通过分析欧洲在线新闻和社交媒体上移民话题的表现，本文探究了乌克兰战争对欧洲对乌克兰难民的看法是否有所改观，并发现了一个叫做 ' 真正的难民 ' 的新术语逐渐代替了传统‘移民’的称谓，然而公众态度的变化因国家而异。