New study identifies predictors of youth mentorship termination

Lyons, M. D., & Edwards, K. D. (2022). Strategies for monitoring mentoring relationship quality to predict early program dropout. American Journal of Community Psychology.

Summarized by Ariel Ervin

Notes of Interest:

  • Despite the popularity of mentoring in America, many studies on these interventions have small to moderate effect sizes.
  • Assessment data have the potential to help improve mentoring program services.
  • This study explores what predicts premature mentoring relationship termination in Big Brothers Big Sisters (BBBS).
    • It also assesses the value of using the Strength of Relationship (SOR) scale as a potential screening tool for predicting premature termination.
  • Findings indicate that mentors and mentees who have the same racial &/or ethnic identity have a lower risk of terminating their relationship prematurely.
    • This also applied to mentees’ positive perceptions of their relationship.
  • Mentoring relationship quality, on average, correlated with premature termination.
  • Using data as a screening tool isn’t ideal since the data was substandard for accuracy classifying premature closure.
  • Further diversifying the available mentor pool for more matching opportunities can help reduce the risk of premature mentorship termination.
  •  Mentoring programs can also further train & support their mentors (especially White mentors) by providing culturally sensitive support.

Introduction (Reprinted from the Abstract)

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.

Implications (Reprinted from the Discussion)

Youth mentoring programs are popular prevention services that are often thought to produce positive effects through a close, long-lasting relationship with a nonfamilial adult (Rhodes, 2005). This means that unexpected and premature termination of the mentoring relationship can be especially harmful for youth (Spencer et al., 2017). To mitigate risks of premature termination, programs are moving toward models of service in which mentoring activities are guided by research-based practices and supported by data (Lyons & McQuillin, 2021). BBBS, for example, routinely collects data from mentors and mentees, and employs staff to support and advise participants in effective strategies for mentoring. Although the data BBBS collects on the match characteristics and relationship strength has been shown to correlate with important outcomes (e.g., match length and positive reciprocal relationships; Raposa, Ben-Eliyahu, et al., 2019; Rhodes et al., 2017), the extent to which these data can act as a screening tool to identify matches at risk for premature termination has not been examined—until now.

As mentoring programs move to use available data to make informed, research-based decisions about the types of services offered, the results of the study suggested that existing data collection efforts within BBBS provide a promising foundation upon which to begin to identify those at risk for prematurely terminating the service. As data were collected from a large national sample of mentors and mentees participating in the program (N = 82,224), results presented provide new evidence about the ways in which the assessment data do (and do not) provide a signal for those who may be at risk for termination. Three major findings related to the construct validity, predictive validity, and classification accuracy of the available data are described below.

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