Abstract

Professional networks are important for the success of doctoral students and early career faculty members, yet there is little research about what types of experiences help emerging scholars develop these networks. Social network analysis may be an ideal method for studying the effectiveness of training programs in nurturing network development among emerging scholars. We describe one application of this method, which was used to examine the professional networks formed through participation in the Association of Gerontological Education in Social Work (AGESW)’s Pre-Dissertation Fellowship Program (PDFP). Alumni (n = 12) from the first three cohorts of the program (2010–2012) reported meeting an average of 20 scholars (SD = 13.2) through AGESW, which led to potential professional interactions and collaborations on conference presentations and manuscripts. Although challenges with missing data limited the conclusions that can be drawn, we find that this method holds promise for helping to identify key factors that facilitate professional network development in pre-dissertation training programs such as the PDFP.

Document Type

Article

Publication Date

2019

Notes/Citation Information

Published in Journal of Gerontological Social Work, v. 62, issue 8.

This is an Accepted Manuscript version of the following article, accepted for publication in Journal of Gerontological Social Work. Mauldin, R. L., Greenfield, J. C., Kusmaul, N., Fields, N. L., Wladkowski, S. P., & Gibson, A. (2019). Using social network analysis to assess professional network development among AGESW pre-dissertation fellowship program participants. Journal of Gerontological Social Work, 62(8), 873-888. https://doi.org/10.1080/01634372.2019.1686673

It is deposited under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License (https://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited, and is not altered, transformed, or built upon in any way.

Digital Object Identifier (DOI)

https://doi.org/10.1080/01634372.2019.1686673

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