Ways mentor training and program support can predict premature mentorship terminations
McQuillin, S. D., & Lyons, M. D. (2021). A National Study of Mentoring Program Characteristics and Premature Match Closure: The Role of Program Training and Ongoing Support. Prevention Science. https://doi.org/10.1007/s11121-020-01200-9
Summarized by Ariel Ervin
Notes of Interest:
- Although mentoring is a common and effective way to advocate for positive youth development, mentorships that end prematurely can be detrimental for mentees.
- This study used data from a national survey on youth mentoring programs in order to get a better understanding of how mentor training and program support determine premature mentorship terminations.
- There are two major findings that arose from the data.
- The rates of premature matched terminations are difficult to foresee.
- While the length of pre- and/or post-mentor training doesn’t predict premature matched termination in a notable way, the monthly frequency of ongoing training and support does.
- It is recommended for mentoring programs to provide ongoing training and support for their mentors, so mentors can continuously develop more skills to more effectively serve their mentees.
Introduction (Reprinted from the Abstract)
Mentoring programs are a popular approach to preventing problem behavior and promoting positive youth development. However, mentoring relationships that end prematurely may have negative consequences for youth. Previous research has investigated match-level indicators of premature match closure, highlighting possible individual mentor- or mentee-level characteristics that might influence the match staying together. However, less work has investigated the importance of program-level variables in match retention. Mentor training and support may be one key modifiable program-level feature that could curtail the risk of premature match closure. In this study, we used data from a national survey of youth mentoring programs (N = 1451) to examine training and other potential predictors of premature match closures (Garringer et al. 2017). We used a Bayesian Additive Regression Trees (BART) model to predict program-reported premature match closure rates from a set of four training-related variables and 26 other covariates (e.g., program size, budget, demographic composition). Findings indicate that the set of predictors explained about one-fifth of the variation in reported rate of premature match closure (cumulative pseudo R2 = .21), and the strongest, and only statistically significant, predictor of premature match closure was the frequency of ongoing training and support contacts per month. Overall, findings indicate that there is substantial noise in predicting program-reported premature match closure, but program-reported provision of ongoing training and support seems to emerge as a relatively stable signal in the noise.
Implications (Reprinted from the Discussion)
In this study, we predicted program-reported premature match closure rates from training variables, controlling for other program-level expectations, capacity variables, and demographics. A unique feature of this study is the use of program-level data, which allows us to make inferences surrounding variation in program practices. This is in contrast to other research which has investigated match-level data, wherein inferences are constrained to variation at the individual level. There are two important findings from this study. First, program-reported premature match closure rates are difficult to predict, and there is substantial noise in understanding why some matches persist and others do not. This is consistent with the following assertion from Rhodes:
Despite careful screening and interviewing, evidence-based training, case management, and even intuition informed by long experience, there is a fair amount of luck involved in determining whether a [mentoring] relationship ends up among the half that flourish or the half that fail. (Rhodes 2015, para. 1)
The second important finding relates to how mentoring programs train and support mentors to prevent premature termination. Specifically, we found that length of mentor trainings occurring before or after mentors were matched with mentees (i.e., pre- and post-match training) did not significantly predict premature match closure. These findings mirror the results of a recent meta-analysis by Raposa and colleagues (2019), who found that the volume of pre-match training expected hours did not predict variability in program effect sizes. However, we found that the reported monthly frequency of ongoing training and support appears to be the best predictor of premature match closure within the available data set, outperforming 29 other predictors. If one were to make an actuarial implication, the optimal amount is between three and four trainings or support contacts per month, yet for reasons mentioned below, there are significant limitations with how this estimate translates to practice. Regardless, this connection between ongoing training and support and premature match closure also resonates with the conclusion of Spencer et al. (2017), which was,
Close to half the relationships ended because the youth or mentor was dissatisfied…This speaks to the need for close monitoring and ongoing support of mentoring relationships by agency staff. Pre-match training may only go so far in preparing mentors for the challenges they may face. (p. 456)
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