Please see the attached document for related questions.
PROJECT 4
Prompt: First, review your work in Modules Five and Six, as well as the IDS Four General Education Lenses document in the Reading and Resources section of Module One.
Guidelines for Submission: You are completing two separate analyses: one from the natural and applied sciences and one from the social sciences. You must submit two papers in a single Word document. The entire submission should be 2 to 4 pages in length. Use double-spacing, 12-point Times New Roman font, and one-inch margins. Support your responses with at least two sources for each lens from the assigned course resources, or other scholarly sources located independently via the Shapiro Library. Cite your supporting sources in APA style.
Issue/event – DEPRESSION
Specifically, the following critical elements must be addressed
A. Analyze your issue/event through the lens of the natural and applied sciences for determining its impact on various institutions. Utilize evidence from research to support your analysis.
B. Analyze your issue/event through the lens of the social sciences for determining its impact on various institutions. Utilize evidence from research to support your analysis.
Sources to check in the school library for depression
Assari, S., Gibbons, F. X., & Simons, R. (2018). Depression among black youth; interaction of class and place. Brain sciences, 8(6), 108.
Francis, B., Gill, J. S., Yit Han, N., Petrus, C. F., Azhar, F. L., Ahmad Sabki, Z., … & Sulaiman, A. H. (2019). Religious coping, religiosity, depression and anxiety among medical students in a multi-religious setting. International journal of environmental research and public health, 16(2), 259.
Grob, R., Schlesinger, M., Wise, M., & Pandhi, N. (2020). Stumbling into adulthood: Learning from depression while growing up. Qualitative health research, 30(9), 1392-1408.
Islam, M., Kabir, M. A., Ahmed, A., Kamal, A. R. M., Wang, H., & Ulhaq, A. (2018). Depression detection from social network data using machine learning techniques. Health information science and systems, 6(1), 1-12.
Li, B. J., Friston, K., Mody, M., Wang, H. N., Lu, H. B., & Hu, D. W. (2018). A brain network model for depression: From symptom understanding to disease intervention. CNS neuroscience & therapeutics, 24(11), 1004-1019.
Papathanasiou, I. V., Kelepouris, K., Valari, C., Papagiannis, D., Tzavella, F., Kourkouta, L., … & Fradelos, E. C. (2020). Depression, anxiety and stress among patients with hematological malignancies and the association with quality of life: a cross-sectional study. Medicine and Pharmacy Reports, 93(1), 62.
Qu, C., & Sas, C. (2018). Exploring memory interventions in depression through lifelogging lens.
Ríssola, E. A., Aliannejadi, M., & Crestani, F. (2022). Mental Disorders on Online Social Media Through the Lens of Language and Behaviour: Analysis and Visualisation. arXiv preprint arXiv:2202.03291.
Saunders, R., Cohen, Z. D., Ambler, G., DeRubeis, R. J., Wiles, N., Kessler, D., … & Buckman, J. E. (2021). A Patient Stratification Approach to Identifying the Likelihood of Continued Chronic Depression and Relapse Following Treatment for Depression. Journal of Personalized Medicine, 11(12), 1295.
Shaw, J. (2022). Revisiting the Basic/Applied Science Distinction: The Significance of Urgent Science for Science Funding Policy. Journal for General Philosophy of Science, 1-23.
van den Bosch, M., & Meyer-Lindenberg, A. (2019). Environmental exposures and depression: biological mechanisms and epidemiological evidence. Annual review of public health, 40, 239-259.
Wang, R., Wang, W., DaSilva, A., Huckins, J. F., Kelley, W. M., Heatherton, T. F., & Campbell, A. T. (2018). Tracking depression dynamics in college students using mobile phone and wearable sensing. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 2(1), 1-26.
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