Janet McLaughlin is an Associate Professor of Health Studies and a Research Associate with the International Migration Research Centre at Wilfrid Laurier University. She is an interdisciplinary scholar trained in medical anthropology, with interests in the areas of global and environmental health, food systems, labour, social justice, citizenship, transnational migration and the social impacts of autism. Her research and publications have focused on various areas of migrant workers’ health, rights and well-being, including: access to health care and workers’ compensation; women’s experiences of gender-based violence; occupational, mental, sexual and reproductive health; social determinants of health; and the impacts of separation on migrant families. She is co-founder of the Migrant Worker Health Project, www.migrantworkerhealth.ca, which promotes accessible health care for migrant workers. Dr. McLaughlin is currently researching autism policy and family impacts in Ontario.
Janet McLaughlin
From this contributor
Changes to Canada autism program could do more harm than good
The Ontario, Canada, government recently announced its intentions to overhaul the Ontario Autism Program, but the changes could leave autistic children without supports.
Changes to Canada autism program could do more harm than good
Explore more from The Transmitter
How to collaborate with AI
To make the best use of LLMs in research, turn your scientific question into a set of concrete, checkable proposals, wire up an automatic scoring loop, and let the AI iterate.
How to collaborate with AI
To make the best use of LLMs in research, turn your scientific question into a set of concrete, checkable proposals, wire up an automatic scoring loop, and let the AI iterate.
How artificial agents can help us understand social recognition
Neuroscience is chasing the complexity of social behavior, yet we have not answered the simplest question in the chain: How does a brain know “who is who”? Emerging multi-agent artificial intelligence may help accelerate our understanding of this fundamental computation.
How artificial agents can help us understand social recognition
Neuroscience is chasing the complexity of social behavior, yet we have not answered the simplest question in the chain: How does a brain know “who is who”? Emerging multi-agent artificial intelligence may help accelerate our understanding of this fundamental computation.
Methodological flaw may upend network mapping tool
The lesion network mapping method, used to identify disease-specific brain networks for clinical stimulation, produces a nearly identical network map for any given condition, according to a new study.
Methodological flaw may upend network mapping tool
The lesion network mapping method, used to identify disease-specific brain networks for clinical stimulation, produces a nearly identical network map for any given condition, according to a new study.