Understanding the Science of Effective Mentorship Networks for Underrepresented Researchers
Understanding the Science of Effective Mentorship Networks for Underrepresented Researchers
批准号:
10713780
负责人:
William Marcus Lambert
金额:
$31.12万
依托单位国家:
美国
项目类别:
财政年份:
2023
资助国家:
美国
项目状态:
未结题
起止时间:
2023-09-01 至 2028-08-31
关键词:
AcademiaAcademic supportAffectBiomedical ResearchCharacteristicsDisparityFacultyFundingGoalsGrantIndividualInterventionInvestmentsMapsMeasuresMentorsMentorshipModelingOutcomeOutcomes ResearchPathway AnalysisPatternPersonsPostdoctoral FellowProgram DevelopmentPsychosocial Assessment and CarePublicationsResearchResearch PersonnelResearch TrainingRoleScienceSelf EfficacyTestingTimeTrainingUnderrepresented Populationscareercareer developmentfeasibility testingmeetingsprimary outcomepsychosocialsecondary outcomesuccesstooltrend
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英文摘要
Project Summary/Abstract
Our long-term goal is to identify the factors that contribute to effective mentorship networks for researchers from
underrepresented (UR) groups. Effective mentorship is critical for the retention of early career researchers in
academia. However, researchers from UR groups are less likely to have effective mentorship, which contributes
to disparities in grant funding and faculty representation. Current trends suggest that dyadic mentorship, which
is the most common form of mentorship, may not be the most effective form of mentorship, especially for
underrepresented groups, since it relies on one person to provide many aspects of academic and psychosocial
support. We hypothesize that mentorship networks, which involve multiple mentors with various roles, is a more
effective model than mentorship dyads for the retention and success of underrepresented researchers in
academia. How mentorship networks affect research success and what characteristics of a mentorship network
are important for early career UR researchers are not well-understood. We have developed a new tool called
Mentorship Network Analysis that assesses and maps the mentoring network of individuals. We will validate this
tool and retrospectively characterize successful mentoring networks of UR career development (K) grant
recipients. Then, we will test the feasibility of an intervention that helps UR researchers build effective mentoring
networks. Primary outcomes include mentor network robustness (i.e. the network is meeting the needs of the
trainee), career outcomes and intentions, and research success as measured by grants and publications.
Secondary outcomes include psychosocial predictors for research success (e.g. research self-efficacy) and
intervention acceptability and fidelity. After five years, we will better understand how the mentorship networks of
successful UR postdoctoral researchers form and change over time, their impact on career outcomes, and
patterns of UR mentorship networks critical for success. The findings will fill a critical need to understand how
best to mentor underrepresented trainees, informing investments in training grants and career development
programs that will close the mentoring gap in research training.
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