How Mentoring Networks Shape Early-Career Grant Success: Evidence from NIH K-awardees
Setiono, F. J.; Ho, E.; Lambert, W. M.
Show abstract
Effective mentorship is essential for strengthening the STEMM (Science, Technology, Engineering, Mathematics, and Medicine) workforce, yet empirical evidence on how mentorship networks are structured and linked to career success remains limited. Here, we analyze mentorship networks among recipients of NIH career development (K) awards to characterize network size, mentor roles, and their associations with mentee-reported outcomes, including potential variation by sociodemographic characteristics. We found that K-awardees rely on mentors beyond their primary advisor, who play varying roles beyond being a Research mentor. Different mentor roles led to different types of mentoring outcomes; while Research mentors were associated with research-related outcomes such as Publications and Grants, career- and psychosocial-related mentoring outcomes were more likely to come from other types of mentors, such as Coaches, Connectors, and Sponsors. Larger networks, as well as having Peer and Identity mentors are additively beneficial for researchers who identify as underrepresented in science more than their counterparts. This study provides large-scale evidence on how mentorship network configurations relate to early-career grant success.
Matching journals
The top 3 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Efficacy of Centers of Biomedical Research Excellence (CoBRE) Grants to Build Research Capacity in Underrepresented States 92%
- Human studies of mitochondrial biology demonstrate an overall lack of binary sex differences: A multivariate meta-analysis 84%
- Single-cell transcriptomic profiling of the neonatal oviduct and uterus reveals new insights into upper Müllerian duct regionalization 84%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.