Strategies for monitoring mentoring relationship quality to predict early program dropout

Bibliographic Data

ID11055431
AuthorsMichael D Lyons (0000-0002-2675-1131, Department of Human Services, School of Education and Human Development University of Virginia Charlottesville Virginia USA, corresponding author), Kelly D Edwards (0000-0001-5697-1699, Department of Human Services, School of Education and Human Development University of Virginia Charlottesville Virginia USA)
Year2022
Volume70
Issue1-2
Pages127-138
Publication date2022-09-01
Open AccessYes
TypeARTICLE
VenueAmerican Journal of Community Psychology (JOURNAL)
Journal identifiersISSN: 0091-0562 • E-ISSN: 1573-2770
PublisherWiley (PUBLISHER • GB)
DOI10.1002/ajcp.12585
PMID35230715
OpenAlexW4214508815
LanguageEN
Citations received10
References cited33

We examined data from a nationally implemented mentoring program over a 4‐year period, to identify demographic and relationship characteristics associated with premature termination. Data were drawn from a sample of 82,224 mentor and mentees. We found matches who reported shared racial or ethnic identities were associated with lower likelihood of premature termination as was mentee's positive feelings of the relationship. We also found that, if data were used as a screening tool, the data were suboptimal for accuracy classifying premature closure with sensitivity and specificity values equal to 0.43 and 0.75. As programs and policymakers consider ways to improve the impact of mentoring programs, these results suggest programs consider the types of data being collected to improve impact of care

Dropout (neural networks) · Health psychology · Public health · Quality (philosophy) · Applied Psychology · Career Development and Diversity · Computer Science · Medicine · Mentoring and Academic Development · Nursing · Perfectionism, Procrastination, Anxiety Studies · Psychology

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