Text-based mentoring for postpartum mothers: a feasibility study

ABSTRACT

Although text messaging interventions targeting parents of older students have shown promise for improving a variety of outcomes, evidence on text-based outreach and support for parents of young children is just emerging. We explore programmatic data from a text-based mentoring intervention designed to support postpartum mothers and promote healthy child development. We coded 18,897 texts from 162 mother-mentor pairs to describe their interactions. On average, mothers remained engaged for 296 days. Mothers and mentors discussed both child-focused (28% of exchanges) and mother-focused topics (8%). Mentors responded to 86% of mothers’ problems within 48 hours, offering emotional support (39% of offers) and advice (35%) most frequently. Based on the demonstrated ability of the programme to engage mothers commensurate with other early parenting interventions, the content addressed in mother-mentor interactions, and the mentor offered supports, we conclude text-based mentoring may be a promising strategy for providing outreach and support to postpartum mothers.

KEYWORDS:

• Parenting intervention
• infants and toddlers
• maternal mental health
• technology
• mentoring

Acknowledgement

We are grateful to NurturePA, particularly Phil Keys and Kate Brennan, for their support and partnership. We also wish to thank our coding team, Ariella Meltzer, Maya Reyes, and Taylor Wynn, for their many hours of careful work. Nell Duke, Shawna Lee, and Pamela Moss provided very helpful comments and feedback. Funding to support Eleanor Martin’s work on this study was generously provided by the University of Michigan’s Rackham Spring/Summer Research Grant Program. We are also grateful for support from the University of Michigan Undergraduate Research Opportunities Program.

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes on contributors

Eleanor Martin is a doctoral candidate in Educational Policy, Leadership, and Innovation at the University of Michigan School of Education. She received her B.A. in Social Studies from Harvard University and spent two years teaching kindergarten at a public school in Detroit before coming to U of M. Her current research interests include interventions in the zero-to-three space that support child development, particularly for disadvantaged children. In her doctoral programme, Eleanor analyzes administrative datasets from the Boston prekindergarten programme to examine programmatic questions and she also is working with Dr. Weiland and Dr. Lindsay Page of the University of Pittsburgh on a mixed-methods randomized control trial of a text-based mentoring intervention for new mothers created and delivered by the non-profit organization NurturePA in Pittsburgh, PA. Her dissertation research focuses on this RCT and is funded in part by a grant from the Office of Planning Research, and Evaluation within the Administration for Children & Families as part of their Behavioral Intervention Scholars programme.

Christina Weiland is an assistant professor at the School of Education at the University of Michigan, where she is affiliated with the Educational Studies department, the Combined Program in Psychology and Education programme, and the Education Policy Initiative at the Ford School of Public Policy. She also serves as core faculty for the University of Michigan’s Predoctoral Training and Postdoctoral Training Programs in Causal Inference in Education Policy Research. Dr. Weiland’s research focuses on the effects of early childhood interventions and public policies on children’s development, especially on children from low-income families. She is particularly interested in the active ingredients that drive children’s gains in successful, at-scale public preschool programmes. She is also interested in quantitative research methods, educational measurement, and developmental processes research. Her work is also characterized by strong, long-standing research collaborations with practitioners, particularly the Boston Public Schools Department of Early Childhood. She holds an EdD (quantitative policy analysis in education) and a MA from the Harvard Graduate School of Education, and a BA from Dartmouth College.

Lindsay C. Page is an assistant professor of research methodology at the School of Education and a research scientist at the Learning Research and Development Center at the University of Pittsburgh. Her work focuses on quantitative methods and their application to questions regarding the effectiveness of educational policies and programmes across the pre-school to postsecondary spectrum. Much of her recent work has focused on implementing large-scale randomized trials to investigate potential solutions to ‘summer melt,’ the phenomenon that college-intending students fail to transition successfully from high school to college. Lindsay’s research has been published in a variety of academic journals, and she is the co-author of a new book on summer melt published by the Harvard Education Press. She holds a doctorate in quantitative policy analysis from the Harvard Graduate School of Education, master’s degrees in statistics and in education policy from Harvard, and a bachelor’s degree from Dartmouth College.

Notes

1 Pittsburgh is the second largest city in Pennsylvania, with a population of approximately 300,000. The county as a whole is 83% White, 14% Black, 3% Asian, and 1% Hispanic (U.S. Census Bureau, 2010a). Pittsburgh is 66% White, 26% Black, 4% Asian, and 2% Hispanic (U.S. Census Bureau, 2010b). The median household income in the county and the city is $52,390 and $40,009 respectively, with 13.1% of Allegheny County residents and 22.8% of Pittsburgh residents living in poverty (U.S. Census Bureau, 2010a, 2010b). Over 12,000 children are born in the county each year (Gally & Kokenda, 2010).

2 The number of turns is not the same as the number of individual texts in the exchange. In many cases, a mother’s question or mentor’s response will not fit in a single text and so multiple lines of text are sent and appear in the transcript. However, to count each line individually would overestimate the amount of interaction between the mother and the mentor. Thus, in instances where a single response spanned multiple lines of text, all lines were counted as a single turn.

3 Stylized examples based on actual exchanges are provided because our data agreement with NurturePA prohibits the use of verbatim quotes from the data.

4 Comparing mothers with two children to mothers with three or more children, there were no statistically significant differences on any of the measures of interest. Comparisons of all exchange topics indicated that mothers with three or more children were statistically significantly more likely (p < 0.05) to discuss child care with their mentor, but these mothers were not significantly more likely than mothers of two to discuss any other topic. Thus, we collapsed the categories for number of children into mothers of one and mothers of two or more.

Additional information

Funding

Funding to support Eleanor Martin’s work on this study was generously provided by the University of Michigan’s Rackham Spring/Summer Research Grant Program. We are also grateful for support from the University of Michigan Undergraduate Research Opportunities Program.