(1) The paper on inequality of educational opportunity (SSM–Population Health) is the first to provide an extensive linkage between sociological theories of mobility and epidemiological and neuropsychological theories of cognitive development. It is also the first to investigate the role of unequal schooling systems (inequality of educational opportunity) on cognitive decline and identify those individual profiles with highest risk to be harmed by unequal schooling conditions.
(2) We improve the understanding between the social and health sciences and machine learning experts by providing an extensive mapping of possible machine learning approaches to research questions of description, prediction, and causal inference in the social and health sciences. This paper is the first of its kind to help researchers from different disciplines understand each other, and advance the uptake of new and improved methods in the social and health sciences:
Leist, A. K., Klee, M., Kim, J. H., Rehkopf, D. H., Bordas, S., Muniz-Terrera, G., & Wade, S. (2021). Machine learning in the social and health sciences. arXiv preprint arXiv:2106.10716.
(3) Continuing the ongoing research in the CRISP project, we expect to make significant contributions to improve our understanding in the following fields:
In the field of dementia prevention, to answer the question, what works for whom and when?, and determine the value of new protective and risk factors in cognitive ageing such as
- Continuing to work up to advanced ages
- Depressive symptom trajectories
- Area- and individual-level socioeconomic status and dementia
- Value of behavior changes to reduce risk of dementia in individuals with depression
- Effects of gender inequalities on later-life gender differences in brain health
