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. Author manuscript; available in PMC: 2025 May 13.
Published in final edited form as: Prev Sci. 2024 Dec 13;25(8):1310–1319. doi: 10.1007/s11121-024-01745-z

Investing in Custodial Grandparents: Cost Analysis of the Social Intelligence Program

D Max Crowley 1, Ashley M Tate 1, Yoon Sun Hur 1, Saul Castro 2, Carol M Musil 3, Megan L Dolbin-MacNab 4, Patrick O’Neill 1, Frank J Infurna 2, Gregory Smith 5
PMCID: PMC11740439  NIHMSID: NIHMS2043950  PMID: 39668282

Abstract

Rising child welfare costs and a desire to keep kids out of the system have encouraged the use of kinship care—of which custodial grandparents make up the majority of caregivers. Unfortunately, custodial grandparents report greater needs for social and emotional support to successfully care for their grandchildren. Yet, the resources required to provide preventive social-emotional support to these families are unknown. In the wake of the Family First Act and other policy actions to expand preventive services, we undertake a cost analysis of the social intelligence training (SIT) within a randomized controlled trial spanning 48 states of the United States of America. Estimated implementation costs were $90,638 (CI $45,254–186,998) which equated to $255 (CI $127–526) per participant. This dual-generation online approach offers key lessons into not only how to resource social-emotional learning (SEL) prevention for custodial grandparents—but also sheds light on how we might provide universal supports to this population. Child welfare system costs have risen to over $33 billion dollars a year—with nearly half of all spending being the result of out-of-home placement (Rosinsky et al., 2021) Child Welfare Financing SFY 2018: A survey of federal, state, and local expenditures. https://www.childtrends.org/wp-content/uploads/2021/05/ChildWelfareFinancing_ChildTrends_March2021.pdf). Practitioners, policymakers, and child advocates are seeking solutions for how to both better protect children and manage these growing public costs (Ringel et al., 2018). Improving child welfare outcomes: Balancing investments in prevention and treatment. Rand health quarterly, 7(4)). Further, many extended families seek ways to keep children out of the “system” when parents are unable to care for their offspring (Lin, Children and Youth Services Review 93:203–216, 2018). A strategy used by all of these groups is the use of kinship care arrangements where extended family provides formal or informal care of children. Several important benefits are recognized from kinship care, including providing connections to family members, communities, and culture. Yet, little is known about how social-emotional supports could enhance kinship arrangements, and to date, no studies have systematically evaluated the costs of such supports. In this context, we conduct a cost analysis of such a program—known as social intelligence training.

The Value of Social-Emotional Learning

Research on the economic impact of the SEL programs indicates the potential for substantial economic benefits. Few cost analyses have been carried out on social-emotional learning programs. Cost analyses provide needed insights not only into the costs of program implementation, but also the distribution of resources across program complements. Further, cost analyses can facilitate projections to model the costs of implementing at scale—a major goal of the SEL field. Belfield et al. (2015) reviewed the costs and benefits of four SEL programs (4Rs, Second Step, Life Skills Training, and Responsive Classroom) and found these programs cost between $158 and $1400 per participant (2022 dollars; Belfield et al., 2015). The net present value of these programs (i.e., the benefits minus the costs) was estimated to range between about $500 and $14,800 per child. Further, the benefit–cost ratio ranged from every dollar spent resulting in a $2 return to every dollar spent resulting in a $13 in return-on-investment from improved health, education, criminal justice, and/or labor market outcomes. Yet, little is known about the economics of delivering SEL programs specifically with Custodial Grandparents (CG). In particular, the cost of dual-generation SEL intervention—such as delivering SEL supports to the CG and grandchild simultaneously—is unknown. To determine the feasibility of SEL prevention at scale for CG and their grandchildren, it is essential to understand program costs.

Cost analyses seek to quantify and value the resources consumed by an intervention in order to estimate the economic costs. These costs include both budgetary and non-budgetary costs with the goal of understanding market impacts of intervention delivery. One historically limiting factor around conducting cost analyses of SEL interventions has included a lack of standardization around the methods for conducting these analyses. SEL programs are generally delivered outside of formalized service delivery contexts—often being offered on an ad hoc basis in informal community settings or “piggy-backing” on systems that were not explicitly built for supporting social and emotional development. This makes cost analyses more difficult and increases uncertainty in estimates.

Over the last half-decade, several advancements in the methods for conducting cost analyses of SEL programs were developed (Crowley et al., 2012, 2018). In 2013, the Children’s Bureau outlined best practices for cost analysis of interventions for youth (Calculating the Costs of Child Welfare Services Workgroup, 2013). Then in 2016, the National Academies published recommendations for the generation and use of economic evidence for children and families (National Academies of Sciences, Engineering, and Medicine, 2016). Particularly germane to this work is the Society for Prevention Research’s standards for an economic evaluation for prevention. These standards provide guidance for conducting cost, cost-effectiveness, and benefit–cost analysis (Crowley & Jones, 2017). These standards guide our cost analysis of the SIT program to provide comparability and inform decisions to adopt, implement, and sustain the use of this intervention.

Kinship Care in the United States

Kinship care can involve risks including access to fewer resources and a lower likelihood that caregivers are licensed foster caregivers—especially in informal arrangements not overseen by the child welfare system (Argent, 2009; Xu et al., 2020). A major threat to the ultimate success of kinship care arrangements includes the ability for the custodian and child to make meaningful connections and navigate the often intense social and emotional experiences resulting from parent separation, new caregiving responsibilities, and other trauma experienced by child and caregiver (Winokur et al., 2018). Providing direct dual-generational support to both custodian and child during this time is key to the success of such arrangements—but how to accomplish this in a sustainable fashion is unclear. To shed light on how to invest in these familial arrangements, we conducted a cost analysis of a social-emotional intervention known as the social intelligence training (SIT). First, we provide background on the state of kinship care in the US and the largest kinship care group—CG. Then, we discuss the potential of social-emotional learning-based prevention for these families.

The largest caregiver category of kinship care is grandparents with over 2.5 million children in the US now living in a household headed by a grandparent, reflecting 52% of kinship arrangements (ACS, 2020; Smith et al., 2018). Many of these living arrangements occur outside of formal foster care placements—many times preventing children from entering the child welfare system in the first place (GAO, 2020). Over the last decade, the number of children living with CG has continued to grow. Among other external factors, parental incarceration, death, and/or loss of parental rights due to misuse of opioids and other substances has placed a strain on the child welfare systems across the country (Dubowitz et al., 1993). Specifically, this has placed significant burdens on system resources and the available placement opportunities for children separated from their parents. The formality of grandparent caregiving over childhood can vary, with grandparents providing full-time care for brief periods of time leading to more formal arrangements, or more formal arrangements giving way to family reunification, as well as numerous other scenarios.

Kinship care arrangements have several benefits. Notably, for marginalized populations that are navigating the child welfare system, non-kinship placement opportunities can often be in homes or settings coordinated by individuals who do not share the racial/ethnic and sociocultural identities of the children in their care (Rankin, 2002). Some scholars point to the ethical and moral dilemmas of trying to protect children—while also ensuring that children from minoritized groups are not exposed to further trauma from racial/ethnic prejudice (Scannapieco & Jackson, 1996). Kinship care, when safe and appropriately resourced, may be part of the solution to further compounding systemic inequality in these spaces (Davis-Sowers, 2006).

Despite the potential of kinship care, CG and the grandchildren they provide care to, are significantly more likely to have had adverse experiences in their own childhood compared to the general population (Smith et al., 2023, 2024). It is particularly common that only children’s female-identifying grandparent (i.e., grandmother) will be available or involved in the custodianship. Relative to households headed by parents, CG also experience higher levels of caregiver aggravation and greater difficulties with child temperament. Recent work found that nearly a third of CG felt they did not have adequate resources to emotionally handle their day-to-day lives (Di Gessa & Glaser, 2019). A major antecedent of placement failure for children in the care of grandparents is a failure to make fundamental connections that allow them to successfully protect and parent their grandchild. The experiences of CG highlight the potential value of social-emotional support for individuals living in this family arrangement (Jones et al., 2020).

Social-Emotional Learning and Social Intelligence

Social-emotional learning refers to the processes through which people learn to develop healthy identities, manage emotions, empathy for others, supportive relationships, and make responsible and caring decisions. Fundamental competencies of SEL interventions include five interrelated domains. These include self-awareness, self-management, social awareness, relationship skills, and responsible decision-making. An array of social-emotional learning programs have been developed and delivered for individuals across the lifespan. Meta-analyses of the SEL intervention space indicate the potential of these programs to effectively improve social and emotional outcomes (Durlak et al., 2011). SEL interventions can improve social skills, attitudes, behavior, and academic performance (Sklad et al., 2012), especially for children who are raised by CG (Smith et al., 2018; Xu et al., 2022).

A component of social-emotional learning, referred to as social intelligence, was historically recognized as the capacity and competency at understanding, managing, and acting wisely in human relations (Thorndike, 1920). While social intelligence originally was reduced to a subcategory of general intelligence, studies from neuroscience have highlighted how neuronal activity when exhibiting social intelligence indicates it to be distinct from other conceptualizations of intelligence (Fox et al., 2005; Lieberman, 2013). Whereas traditional operationalizations of intelligence consider it to be a largely stable construct, social intelligence is recognized as a dynamic and malleable human capacity (Kihlstrom & Cantor, 2011). Specifically, social intelligence can be improved with self-reflection and intention as well as from efforts to improve understanding of social rules and practice being attentive to others (Snow, 2010). Even the act of teaching an individual about the malleability of social intelligence is a form of behavioral intervention for improving social-emotional learning (Yeager & Dweck, 2012).

More recent conceptualizations of social intelligence recognize it as the fundamental capacity for social connection (Snow, 2010), possessing knowledge of processes that underlie social development (Kihlstrom & Cantor, 2011), and a commitment to reducing prejudice towards others (Castro & Zautra, 2016). Growth in social intelligence exhibits as improved knowledge and awareness of how the mind draws on experience to create social expectations and schemas and increased proficiency in creating and maintaining positive social bonds. The social intelligence training (SIT) assessed here draws on this theory and research stemming from neuroscience, psychology, and interdisciplinary studies of social-emotional learning generally (Zautra et al., 2015).

Social Intelligence Training

The social intelligence training program was developed to improve key socio-emotional skills (e.g., emotional awareness, perspective-taking) regarding how people engage with others (Masi et al., 2011). This intervention was designed to encourage recognition that social-emotional capacities are malleable and can be learned (Schumann & Dweck, 2014; Yeager & Dweck, 2012). The underlying process of “humanization” informs this intervention strategy—seeking to increase recognition that people are social (Lieberman, 2013), and human connection originates from understanding and empathy for one another (Jolliffe & Farrington, 2006). This intervention, described in detail below, is comprised of 42 5–10-min online sessions within 7 modules comprising content videos, whiteboard explainers, and interactive quizzes. The SIT encourages personal connection with the material presented. After each session, participants write their responses to awareness-raising questions designed to provoke thoughtful attention onto current and past personal experiences relevant to the material presented. Each session ends with practice exercises, designed to enhance readiness to change and increase self-efficacy (Fig. 1).

Fig. 1.

Fig. 1

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Social intelligence training inputs and outputs

Previous evaluations of SIT have identified several benefits from program receipt. For instance, in a study of 311 university students who received SIT, participants significantly improved their perspective-taking, social skills, emotional sensitivity, and ability to recognize and engage in socially appropriate behaviors (Zautra et al., 2015). A randomized trial of 220 middle-aged adults, oversampled for those with a history of trauma in childhood, found significant increases for the intervention group on daily levels of “in-tune” social interactions, emotional awareness, and perspective-taking, and attenuated within-person changes in social engagement on stressful and uplifting days. Participants who experienced greater childhood trauma experienced the greatest improvements in social engagement and emotional awareness.

Methods

We conduct a cost analysis of the SIT’s implementation with CG and their grandchildren within a randomized clinical trial (RCT) examining the efficacy of an online social intelligence training program delivered to custodial grandmothers (CGMs) and their adolescent grandchildren. The present study was only focused on CGMs, and not custodial grandfathers, as it is common that the CGMs are the only grandparent involved in custody of the child. The SIT was administered only to CGMs, but the focus of our analysis was costs associated with both CGMs and their grandchildren. Recruitment occurred through a multi-pronged approach, including digital messages to state and county social service and health providers, high school counselors and principals, advocacy and support organizations, and announcements and brochures mailed to targeted mailing lists. The seven modules that were implemented as part of the SIT program include the following:

  1. The first module, called Neuroplasticity, addressed brain development and the life-long capacity of each person to form new neuronal connections that can support their social connections and well-being (Klimecki et al., 2014). This module includes six sessions comprised of accessible content about neuroplasticity, neuronal pruning, developmental neurobiology (“teen brain”), genetics, and a refutation of genetic determinism and how these biological processes relate to participants lives.

  2. The second module introduced the “contagious” nature of emotions, conscious and unconscious processing. It covers how the brain processes information, guided by individual schemas and heuristics, as well as overarching cognitive biases. It further discusses how heuristics and schemas can exacerbate biases, as well as how awareness of these biases can improve the accuracy of social perceptions (e.g., Kahneman, 2011). This module explained how being aware of the benefits and consequences of these ways of thinking improves the capacity to understand oneself and others.

  3. The third module, perspective-taking, addressed the ability to identify feelings and thoughts of another person to respond appropriately and predict how others may react to one’s actions. This module introduced perspective-taking as a skill that improves with deliberate attention to the feelings and actions of others (Galinsky et al., 2005).

  4. The fourth module discussed in-group and out-group biases. It addressed how one’s thoughts and behavior toward others are shaped by in-group favoritism, which often occurs outside of conscious awareness (Harris & Fiske, 2006; Haslam, 2006). These sessions were designed to raise awareness of the nature of prejudice (Crisp & Turner, 2009) and offered thoughtful ways of responding to out-group members.

  5. The fifth module explored face-to-face and online communications and discussed the ebb and flow of positive face-to-face social interactions, as well as factors that disturb that natural cadence. The importance of connecting with others was addressed by contrasting the plentiful but relatively shallow online connections and the potential richness of connecting face-to-face (Bayer et al., 2016; Ybarra & Winkielman, 2012). Pathways to improving communication with one another, through awareness and practice, were introduced (Reis et al., 2017).

  6. Resistance to learning new ways of relating to others was confronted directly in the sixth module (Bowlby, 1969; Hughes & Ensor, 2007). This module addressed how past experiences, particularly interactions with parents and other family members early in life, shape schemas formed about the trustworthiness of social relationships, and the willingness to engage meaningfully with others.

  7. The final module emphasized choice. Specifically, each person is not destined to repeat old patterns of relating. People have the capacity to form new social connections, modify their schemas, and enhance the quality of long-standing relations in need of repair, if they choose to do so. Well-established ways of relating can be changed through both awareness and self-regulation efforts (Snow, 2010).

Participants

Participants were 349 CGMs (M = 61.4 years; SD = 5.9) and a target grandchild (ages 12–18). Families resided in 48 states. Inclusion criteria were that CGMs provided care to the target grandchild for at least 6 months in their homes (in absence of child’s birth parents), were without cognitive impairments, and were fluent in English.

Most CGMs were White, non-Hispanic, and cared for one grandchild. CGMs varied regarding their annual income, education, employment, marital status, and duration of caring for the target grandchild. The primary reasons the grandchild was with CG were parental substance abuse (49.6%), neglect of the child (22.5%), parental incarceration (18.6%), and parents’ unwillingness to provide care (17.5%).

Measures

Cost data were collected prospectively across the start-up and implementation period. Personnel inputs were prospectively tracked monthly for all implementation staff. This included leadership and program manager inputs as well as wage-based technical assistance providers. Salary personnel oversee implementation whereas technical assistance providers supported families use of the digitial intervention. Further, the quantity, cost, and timing of equipment and fees were prospectively tracked to understand both total costs, but also when these goods were consumed in preparation for and during implementation.

All implementation activities were completed virtually across the implementation period. Monetary values for resources were based upon those available on the CostOut Tool Kit. All estimates are in 2021 dollars.

Analytic Approach

A cost analysis of the resources required to implement the social intelligence training was conducted. An ingredients-based approach was employed in accordance with the Society for Prevention Research’s standards for cost analysis of prevention programs (Crowley & Jones, 2017). This approach entails prospective tracking of resource consumption from all intervention activities during pre-implementation and implementation. The ingredients cost method relies on the use of market prices to estimate the value of resources consumed by an intervention (e.g., personnel time, equipment, supplies). Its goal is to ascertain the cost of all the resources required to replicate the same level of effectiveness in future implementations if provided with equivalent resources.

This methodological approach has several steps. First, an intervention’s theory of change is articulated in a logic model inclusive of both the expected inputs and outputs of the model. This allows for a priori specification of measurement and planning for prospective tracking of implementation cost drivers. Next, the specific resources consumed (i.e., ingredients) are measured in terms of both quantity and quality—regardless of where the resources come from (e.g., budgetary, participant). For instance, while participant time may not be paid for by a project budget—these costs are part of the larger societal investment to bring about change. Next, market prices are assigned to all resource consumption. Again, this is done independently of funding sources and inclusive of participant and/or volunteer costs when applicable. Finally, costs are analyzed to facilitate understanding and utility by decision-making audiences.

Sensitivity Analyses

Like effectiveness analyses, cost analysis estimates contain uncertainty in estimates from a variety of sources. To understand the uncertainty of estimates, best practices are to conduct sensitivity analyses to test key areas of uncertainty to develop confidence intervals around cost estimates. For the purpose of this study, we assess (1) variation in personnel costs and (2) variation in equipment costs (e.g., providing electronic tablets with the SIT program on them to participants who did not have or could not afford the device).

Local labor markets can influence the costs of personnel and subsequently the costs of intervention implementation. We assess variation in compensation across local labor markets for project management within child and family services contexts. Bureau of Labor Statistics indicates compensation in these settings to vary between $36,780 and $93,540 in 2021 (BLS, 2021). Further, the intensity of families’ technical assistance needs can influence personnel costs during implementation. In this context, we use Bureau of Labor Statistics data on the high and low compensation rates of child and family service professionals to assess estimate sensitivity to labor market variations.

Equipment costs can vary over time due to new innovations, the creation of new public–private partnerships, and disruptions of global and local supply chains. For instance, the costs of audio-visual equipment have dropped precipitously during the last decade and storage and memory continue to follow a steep decline in cost-per-unit. Further rollout of large-scale interventions for supporting public good can benefit from public–private partnerships to access equipment at reduced rates when operating at scale (as well as benefit from government purchasing power). Despite these advantages, technology-assisted programs are also vulnerable to supply chain disruptions from a number of sources (e.g., shortages in rare earth metals, computer chips, fuel). This can particularly extend to consumer electronic products such as those used in SIT. In this context, we observe how a reduction (50%) of equipment costs from potential future costs declines or public–private partnerships. We also observe how a doubling of equipment costs may impact participant costs.

Sensitivity analyses for this study test bidirectional assumptions of “best” and “worst” case scenarios for the above one- and multi-way sensitivity analyses. This allows us to test the robustness of cost estimates and develop confidence intervals around point estimates of program cost.

Results

The RCT revealed that, compared to an attention control condition, the online SIT revealed favorable outcomes for both CGMs and their adolescent grandchildren. This was particularly true for relationship-oriented outcomes, with effect sizes being large when baseline values of outcomes were examined as moderators of differential treatment efficacy (Smith et al., 2024). The ingredients-based cost analysis of the SIT analysis observed delivery of SIT during both a 5-week pre-implementation period and a subsequent 3-year implementation period. The total costs of implementation were estimated to be $90,638 (CI $45,254–186,998). The average cost per participant was estimated as $255 (CI $127–526). Table 1 provides a breakdown of major cost categories across the implementation period.

Table 1.

Implementation costs of social intelligence training program with custodial grandmothers and grandchildren

Pre-implementation Year 1 Year 2 Year 3 Total
Personnel Salaries $4656 $9069 $13,834 $12,516 $40,075
Wage $788 $3600 $5224 $4961 $14,573
Benefits $899 $1751 $2671 $2417 $7737
Equipment $750 $780 $6886 $8666 $630
Fees $915 $4108 $4366 $1902 $11,291
Total $8037 $25,414 $34,761 $22,426 $90,638
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Personnel accounts for 58% of costs. The decomposition of personnel costs indicates 63% spent on salaries, 25% spent on wages, and 12% on benefits. Equipment costs reflect 26% of total costs, and fees represent 16% of costs. Activity-based cost analyses indicate that total costs across the 3-year period include 13% on recruitment costs, 50% on technical assistance support, 25% on equipment, and 12% on curriculum costs.

Cost Categories Across Implementation

We observe the variation in implementation costs during the pre-implementation period and across 3 years of implementation. This includes relative highs and lows monthly investments—varying by over 600% across implementation (Fig. 2). Further, we observe cumulative costs rise across the implementation period at a largely linear rate.

Fig. 2.

Fig. 2

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Timing and accumulation of resource consumption across implementation period

Sensitivity Analyses

Sensitivity analyses of implementation costs indicate an estimated range of intervention costs for this trial between $45,254 and $186,998. This translates to a per participant range of $127 and $526. Personnel costs are the biggest driver of uncertainty in cost estimates. One-way sensitivity analyses of labor market compensation indicate it can influence the personnel costs by 56%. We explore the variation in equipment costs for the study considering as much as 31% variation in costs (Fig. 3).

Fig. 3.

Fig. 3

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Sensitivity analyses of social intelligence training implementation costs. Note: Tornado diagram describes the higher and lower bounds of participant costs for the assumptions tested highlighting the relative contribution to estimate uncertainty

Participant Costs and Intangible Costs

Three intangible participants’ costs were identified through the course of implementation. In particular, beyond time costs, there is the psychological load within the intergenerational dyad of ensuring the other family member was completing the modules. This emotional burden, while helpful for accountability, also meant the expenditure of relational capital, generally from the grandmother to the child, on encouraging completion of modules. While not a major cost, future work would benefit from exploring this dynamic within particularly contentious dyadic relationships.

Another intangible cost related to the implementation of the program relates to growing social and health expectations to limit children’s use of electronic devices (i.e., screen time). Recommendations from the American Academy of Pediatrics include monitoring and keeping screen time to a minimum—given the expectation that youth are now using a variety of screens at school, at home, and with peers. The difficulties of managing screen time were further exacerbated during the beginning of the COVID-19 pandemic that overlapped with this trial—where many children were required to attend school virtually for many hours.

A third salient intangible participant cost was related to the adoption of the online platform for some families. While most families in this study were well equipped to access and interact with the SIT, some families were less equipped or experienced with online and digital modalities. The implementation costs of helping these families navigate start-up and completion of the program are captured within the personnel costs employed to provide families with technical assistance. We note that, for a subset of families, this startup added an additional psychological burden above and beyond the time costs. Without providing this technical assistance, it is likely many families would not have successfully accessed and completed the program.

Marginal Costs of the SIT

Marginal costs across implementation indicated a relatively linear increase as implementation scaled. Key factors impacting nonlinear marginal costs include project manager time. As the number of families grows, additional project managers will need to be staffed. Project managers were estimated to be able to supervise technical assistance teams serving 1750 and 3500 families (< 1 FTE increase). Thus, the marginal cost of serving 3500 + 1 families likely requires hiring an additional project manager. Further, recruitment costs and how CG are contacted represent another nonlinearity to consider for the scale-up of SIT. For instance, purchase of recruitment leads needs to be updated as lists become exhausted, or time passes and contact information and/or family arrangements change ($200–500 for additional lead lists depending on the geographic area of recruitment).

Discussion

This study of the resources needs and costs of providing social-emotional preventive services to CG and their grandchildren across 48 states reveals several insights for the field. In particular, this work highlights the feasibility of providing quality social and emotional preventive services for CG and their grandchildren. Specifically, taxpayers currently spend over $30,000 per year per child in foster care. The SIT program enhanced with technical assistance reflects less than 1% of that amount.

In 2018, the Family First Prevention Services Acts signed into law allowed for federal foster care funding to states, and tribes to be reallocated toward evidence-based preventive programs and in-home parenting skill training. This study highlights the feasibility of providing social-emotional support services as part of the growing portfolio of prevention services being offered to children and their families across the country.

A number of lessons also resulted from this work. In particular, despite being an online program, this population still requires technical assistance to successfully engage with digital materials and an online environment. As a result, personnel costs are a major driver of implementation costs. Having an adequate state-wide workforce able to interact and successfully support this population’s engagement with digital programming will be key to successful expansion efforts.

Cost analyses of the SIT also indicate the relative efficiencies of technical assistance enhanced implementation. In particular, the combined need for both technology and curriculum supports assistance to maximize participants’ engagement with the curriculum. Further, this implementation and cost analysis included the provision of a device for accessing digital content (tablet). For many CG, access to these devices is limited by their income and resources in the home. We find it important to consider the equipment needs for digital and online interventions—particularly the implications for sourcing these devices during scale-up.

Finally, these analyses highlight that this intervention is quite sensitive to shifts in labor market and equipment costs. In particular, participant costs can nearly double in worst-case scenarios where equipment and labor market costs increased simultaneously. Decision makers considering the adoption and scale-up of this program should have a good understanding of availability and historical compensation for child and family services workers prior to implementation. Similarly, large equipment orders should be negotiated and agreed upon prior to beginning implementation to avoid unexpected costs.

Limitations and Future Opportunities

While this trial delivered services to families living in 48 states around the country, there are numerous variables about how many families within this demographic might be successfully reached within a statewide roll-out. CG report that they are under-supported in understanding their grandchildren’s social-emotional needs—and managing their own—which is well documented in the literature. Despite this, it is likely that not all families would actively engage with the SIT if rolled out nationally—as such estimates of statewide costs could be an overestimate.

Further, this trial was focused on the dyadic relationship between grandparents and grandchildren. As a result, implementation costs focused on delivery to the grandmother and one grandchild. On average, CG have 2.2 grandchildren under their care. Similarly, the cost of supporting CG partners should be further explored. This further exploration should be especially focused on the likely increased true costs of adopting the program for those families who may struggle with obtaining the necessary resources and technological literacy.

Looking to the future, the field would benefit from a greater understanding of how to reach this relatively high-risk family structure and provide effective prevention programming. While this study highlights the magnitude of investment to support CG and their grandchildren—it should largely be considered a lower-bound starting point.

Finally, technology-assisted interventions such as SIT provide valuable platforms to optimize interventions. Specifically, methodological and innovation such as the Multiphase Optimization STrategy (MOST) highlight how behavioral interventions may be deconstructed and individual components valued seperately and combined can be evaluated during service delivery (Linda et al., 2014). Digital interventions reduce the burden of such studies as they make managing logistics of multicomponent random assignment more straightforward. Further, within intervention experimental studies of content framing can be undertaken through the use of web-based A/B trials.

Conclusions

Custodial grandparents and their grandchildren represent the largest proportion of formal and informal kinship care arrangements in the country. Relatively small investments could lead to a nationwide effort to support these families and prevent further family disruption for children and hardship for grandparents. Technology-assisted programs such as these further offer the ability to optimize interventions and develop direct preventive service channels to these important, but vulnerable families.

Funding

This work was funded by the National Institute on Aging (R01 AG054571), with support from Pennsylvania State University’s Edna Bennett Prevention Research Center and the Social Science Research Institute.

Footnotes

Declarations

Conflict of Interest The authors declare no competing interests.

Ethical Approval Not applicable.

Informed Consent Informed consent is not required for the cost analysis. The larger trial included IRB approval from Kent State University (prime) and reciprocal IRBs with all project sites.

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

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