Skip to main content
Download PDF
Download ePub
  • Study protocol
  • Open access
  • Published:

Can an evidence-based mental health intervention be implemented into preexisting home visiting programs using implementation facilitation? Study protocol for a three variable implementation effectiveness context hybrid trial

Implementation Science volume 19, Article number: 76 (2024) Cite this article

Abstract

Background

Perinatal mental health conditions are the most common complication of pregnancy and childbirth (1 in 8 women). When left untreated, perinatal depression and anxiety adversely affects the entire family with pregnancy complications and negative outcomes including preterm birth, impaired mother-infant bonding, impaired lactation, substance abuse, divorce, suicide, and infanticide. Significant disparities persist in the diagnosis and treatment of perinatal depression and anxiety and these inequities are often intersectional. Preliminary research with stakeholders including community advisory boards, underrepresented and minority birthing people, and state departments of health, demonstrates the importance of social support as a mechanism for reducing disparities in perinatal depression, particularly in rural geographies. Home visiting programs (HVPs) can provide the social support needed to improve mental health outcomes in pregnant and postpartum women. Our project aims to explore the impact of context on the implementation of a mental health intervention, focusing on the lived experiences of diverse populations served by HVPs to reduce disparities in adverse maternal outcomes.

Methods

Using implementation facilitation, our study will engage multilevel stakeholders (e.g., policymakers, front-line implementers, and intervention recipients) to adapt facilitation to integrate a maternal mental health intervention (i.e., Mothers and Babies) across two midwestern, rural states (Iowa and Indiana) with multiple HVP models. Given the complexity and heterogeneity of the contexts in which Mothers and Babies will be integrated, a three variable hybrid implementation-effectiveness-context trial will test the adapted facilitation strategy compared with implementation as usual (i.e., standard education) and will assess contextual factors related to the outcomes. Using an evidence-based implementation strategy that tailors implementation delivery to the needs of the specific populations and context may improve fidelity and adoption, particularly in rural states where residents have limited access to care.

Discussion

The immediate impact of this research will be to show whether adapted facilitation can improve the uptake and fidelity of Mothers and Babies across multiple HVP models and thus positively affect depressive symptoms and perceived stress of recipients. Our implementation protocol may be used by researchers, practitioners, and policy makers to better integrate evidence-based interventions into diverse contexts, leading to more equitable implementation and improved health outcomes.

Trial registration

ClinicalTrials.gov Identifier: NCT06575894, registered on August 29, 2024 https://clinicaltrials.gov/study/NCT06575894?id=NCT06575894&rank=1.

Peer Review reports

Background

Perinatal (during pregnancy and up to 1 year after birth) depression and anxiety are common medical complications of pregnancy contributing to a rising maternal mortality rate. When women experiencing anxiety and mild depression are included, the estimated prevalence of perinatal mental health conditions may be as high as 25% to 50% [1]. Moreover, the COVID-19 pandemic has exacerbated the mental health crisis across the globe, increasing the prevalence of anxiety and depression by 25% [2]. When left untreated, perinatal depression adversely affects the entire family with pregnancy complications and negative outcomes including preterm birth, impaired mother-infant bonding, impaired lactation, substance abuse, divorce, suicide and infanticide [1, 3,4,5,6,7]. Research suggests that only half of women with antenatal depression are identified, and only about 14% receive treatment [8]. This is particularly noticeable in data from the 2003–2007 National Violent Death Reporting System, where the maternal mortality rate from pregnancy-associated suicide was higher than reported mortality rates for hemorrhage/placenta previa or eclampsia and preeclampsia [9]. Therefore, addressing perinatal depression is of critical public health importance and could significantly lower maternal mortality rates due to pregnancy-associated suicide.

Significant racial/ethnic, rural/urban, and socioeconomic status (SES) disparities exist in the diagnosis and treatment of perinatal depression and anxiety. While some studies have found higher rates of perinatal depression and lower rates of treatment in Black, Hispanic, low-SES, and rural women, these inequities are often tied to one another [10, 11]. For example, previous research examined rural–urban differences in the risk of perinatal depression among 17,229 women who participated in the Pregnancy Risk Assessment Monitoring System survey [12]. We found that depression during pregnancy was 21% higher among rural compared with urban-residing women but that this risk is attenuated after adjusting for education, insurance coverage, and Women, Infants, and Children Program (WIC) participation, suggesting that socioeconomic factors in rural areas explain part of this disparity [12]. Intersectional identities face additional disparities; the Federal Office of Rural Health Policy (FORHP)-funded Rural Health Research Centers showed that rural counties with majority Black, Indigenous, and People of Color (BIPOC) populations have less access to local prenatal care and perinatal mental health services than do majority-White rural counties [13]. Although all rural counties studied had limited access to evidence-based family-centered models of care, this disparity was more pronounced for majority-BIPOC counties. Majority-BIPOC counties also had less access to doula care and postpartum peer support groups [13]. Largely rural states like Iowa and Indiana rank 23rd and 42nd, respectively, on overall measures of mental illness prevalence and access to care [14]. This correlates with the maternal mortality rate, which is higher in Indiana (43.6 deaths per 100,000) than in Iowa (18.3 deaths per 100,000) [15].

Home visiting programs (HVPs) can provide the social support needed to improve mental health care outcomes for perinatal women. By enabling early detection and intervention, social support can mitigate the negative outcomes of perinatal depression [16]. A recent scoping review showed HVPs have been used worldwide to provide mental health screening, psychoeducation, case management, and social support through weekly or monthly home visits made by nurses, social workers, paraprofessionals, or trained volunteers [16]. Community-based HVPs are a practical and evidence-based intervention that can improve maternal and child health outcomes [17]. The same risk factors that qualify a woman for home visiting (e.g., low socioeconomic status, low social support, single or teen mother, inadequate prenatal care, low maternal education) are also risk factors for perinatal depression [16]. Moderate to severe levels of depressive symptoms are found in 45–50% of HVP recipients and home visitors report that clients with depression are more difficult to engage with during visits [18, 19]. Therefore, a need exists to specifically address perinatal mental health in community-based HVPs to ensure maximum benefit of home visiting services. The review by Tabb et al. identified 12 intervention studies conducted during the perinatal period and delivered by HVPs to improve postpartum depressive symptoms; these studies showed improvement in depressive symptoms from baseline to follow-up. Studies show that HVPs can be used to deliver mental health services without relying on the already-taxed mental health care system but produced mixed results due to research design factors and challenges with implementation [16, 20].

One such intervention that has proven to be particularly effective is Mothers and Babies (MB). MB is a well-supported, evidence-based cognitive behavioral intervention with demonstrated efficacy and effectiveness in reducing depressive symptoms and preventing perinatal depressive episodes among perinatal women [2, 11, 12]. Randomized controlled trials have shown that MB can prevent the onset of major depression, reduce depressive symptoms, and improve mood management in perinatal women. The most recent of these trials assessed the MB intervention one-on-one as delivered by trained lay health workers conducted in the setting of HVPs [18, 21,22,23,24]. In a type 2 hybrid implementation-effectiveness trial, even though less than 50% of eligible pregnant people received MB, participants who received at least one session of the MB intervention had better scores on the Beck Depression Inventory II (BDI) and the Perceived Stress Scale (PSS) than did control participants. While results showed the intervention was most effective when the participants received the full dose, only 34% of participants received MB without fidelity-inconsistent adaptations [25]. They concluded using evidence-based strategies that tailor implementation to the needs of specific populations and context could improve fidelity and adoption.

Anthropological ethnography and hybrid trial designs can provide the structure to ensure context-specific implementation. Anthropological ethnography can provide a deeper, richer, and more nuanced understanding of how minoritized populations experience embedded, structural, and systemic aspects of health and healthcare. Context is the explicit focus of ethnography and can ensure that our research not only includes previously underrepresented groups but prioritizes and amplifies their voices over those whom the system has historically privileged. MB is a maternal mental health intervention that has been proven efficacious, [21] and its implementation has been studied in a hybrid trial [25]. The results of these trials have explicitly concluded that better understanding of context, better engagement with stakeholders, and more thoughtful and granular analysis of differential delivery and uptake by diverse populations, is needed to effectively implement MB for all members of priority populations [25]. Three-variable hybrid designs expand the current hybrid implementation effectiveness designs by providing the structure to explicitly consider context as a third independent variable together with the intervention and implementation strategy [26]. The MB studies thus far demonstrate that understanding the effects of contextual determinants (e.g., funding, program, population, etc.) is the missing piece connecting previous implementation and effectiveness work on the intervention and implementation strategy to equitable implementation for all populations across variable contexts (Fig. 1) [26].

Fig. 1
figure 1

Conceptual model with i-PARIHS implementation framework

Full size image

We will conduct a pragmatic cluster randomized trial to test the implementation effects of MB within HVPs on mental health outcomes (depressive symptoms and perceived stress). Given the complexity and heterogeneity of the contexts in which MB will be integrated, a hybrid implementation-effectiveness-context trial will test the adapted facilitation strategy compared with implementation as usual (i.e., standard education and ongoing technical assistance) and will assess contextual factors related to the outcomes. This study will address the following specific aims:

  • Specific Aim 1: Adapt implementation facilitation to support uptake of MB using stakeholder input.

  • Specific Aim 2: Analyze the effects of the adapted facilitation on implementation outcomes (i.e., adoption and fidelity) [26] using mixed rapid ethnographic methods (i.e., implementer and patient questionnaires, interviews, focus group discussions (FGD), observations, and geospatial data) [27, 28].

  • Specific Aim 3: Determine the effect of adapted facilitation on the clinical outcomes: severity of depressive symptoms and perceived stress.

Approach

Our multimethod data collection and analysis will be theory-driven by key concepts of the integrated Promoting Action on Research Implementation in Health Services (iPARIHS), [29] the updated Consolidated Framework for Intervention Research (CFIR 2.0), [30] and Proctor et al., [31] and more broadly by our overarching commitment to reproductive justice and birth equity [32, 33]. Previous research has established the evidence base for the intervention; [25, 34] the challenge is better implementation that fully engages stakeholders and uses a context-responsive strategy. We chose iPARIHS because context is a critical component and facilitation is its main strategy to respond to the specific context and setting of implementation [29, 35,36,37]. As a determinant framework, CFIR 2.0 will provide the structure for the stakeholder conversations in Aim 1; the updated framework centers recipients and reprioritizes relationships between multilevel stakeholders [30]. Additionally, we will use Proctor et al.’s implementation outcomes to frame our work in Aims 2 and 3 so we can disentangle core components of the intervention, how to adapt the implementation strategy, and how context may impact these differently [38]. The hybrid type 2 study of MB used RE-AIM, [25] but Proctor et al.’s more granular measures will clarify how certain contextual elements mediate or moderate implementation outcomes [26].

Context: home visiting programs

Indiana and Iowa have HVPs funded by both the federal HRSA Maternal, Infant, and Early Childhood Home Visiting (MIECHV) program and the state. In Iowa, MIECHV supports four evidence-based HVPs: Healthy Families America (HFA), Early Head Start-Home-Based Option, Nurse Family Partnerships (NFP), and Parents as Teachers in 9 rural and 4 non-rural counties. State and MIECHV HVPs combined serve 92 of 95 counties in Iowa. In Indiana, MIECHV supports HFA and NFP in 6 non-rural counties. State and MEICHV HVPs combined serve all 92 counties in Indiana with 73% having more than one HVP available.

Intervention: Mothers and Babies (MB)

MB is a well-supported, evidence-based cognitive behavioral intervention with demonstrated efficacy and effectiveness in reducing depressive symptoms and preventing perinatal depressive episodes among perinatal women [21, 25, 39]. MB is a 9-session intervention with each session designed to be delivered in 20–25 min. Program recipients are taught ways to increase thoughts and behaviors that lead to a positive mood state. The protocolized intervention has 3 modules that focus on (1) increasing enjoyable activities, (2) reframing harmful thoughts, and (3) increasing social support [39]. The MB training will present basic cognitive-behavioral theory, the MB 1-on-1 curriculum content, and the process for implementing the intervention in conjunction with delivering home visits (e.g., positioning of the MB session within the home visit agenda, alignment with the goals of the visit, modeling concepts for clients) [21, 25].

Implementation strategy: facilitation

Implementation facilitation is a multifaceted, flexible, evidence-based implementation strategy; [40] that has been shown to improve implementation across interventions and contexts, including mental health [41, 42]. Facilitation is delivered as a bundled subset of a range of activities (a recent scoping review identified 32 discrete activities) that vary from goal-setting and problem-solving to training and marketing, depending on stakeholder needs [43]. This flexibility makes facilitation the most appropriate choice as the implementation strategy for our trial since it can be adapted to the patchwork of local contextual factors across the HVPs in Iowa and Indiana. Research team members will be trained and receive ongoing support as external facilitators through the Department of Veterans Affairs (VA) Quality Enhancement Research Initiative Training Hub’s Implementation Facilitation Training Program [35].

Methods

Formative evaluation

Our objective is to amplify the voices of pregnant/birthing people and to empower front-line implementers as active partners in our implementation research. We will conduct formative evaluation using participatory approaches to ensure engagement of stakeholders in the adaptation of the strategy as well as to improve adoption and fidelity. The context and evidence domains of iPARIHS in conjunction with specific determinant domains of CFIR 2.0 will provide the framework to better understand the contextual factors that impact implementation of MB and to account for different stakeholders’ perspectives as well as confirm the contextual variables to be measured in the trial. The context-specific adaption of implementation facilitation will be based on a shared understanding of the complex patchwork of HVPs in each state [44] and the current elements of psychosocial support. The research team will work with stakeholders to develop a shared understanding of the context of funding, policy, home visitors, training, recipients (as well as their families and communities), and the resources of existing HVPs in which MB will be implemented.

Key informant interviews

To gather context-specific information on the structural and social factors that may impact the participation of BIPOC birthing people in home visiting services, our research team will conduct brief key informant interviews with a purposefully sampled group of BIPOC birthing people. Interviews will focus on participants’ experiences, preferences, and barriers and opportunities to engage with HVPs with an interview guide informed by iPARIHS and CFIR 2.0.

Survey of HVPs

MIECHV services are tracked at the state level, but less is known about locally or state funded HVPs. We will conduct a brief survey of all HVPs within Iowa and Indiana through a REDCap survey asking each program to provide the numbers of 1) home visiting supervisors, 2) home visitors and 3) pregnant HVP recipients served each year. We will also assess which, if any, mental health services each program currently provides to determine initial interest our study.

Stakeholder meetings

There will be two groups of HVP and maternal mental health stakeholders for each state who will meet within the same timeframe. To ensure all voices are heard equally and not affected by power dynamics, one group per state will consist of multilevel care delivery stakeholders and one group will consist of program recipient stakeholders. The care delivery group will include representatives from state departments of health (e.g., HVP Directors, home visiting epidemiologists, applied research coordinators, professional development coordinators), individual HVPs, and front-line implementers (e.g., home visitors, home visiting supervisors, social workers, embedded mental providers, doulas). The other group will consist of program recipient stakeholders (e.g., birthing people, families, and community members). All conversations will be audio recorded and research team members will take field notes.

We will use constructs of CFIR 2.0 organized by evidence, context, and facilitation (iPARIHS domains) that might influence differential uptake of MB, including local conditions and attitudes, assessing needs, setting goals, adaptations) [30, 36]. At the beginning of each meeting, the research team will briefly describe MB and the purpose of the meeting to participants. To identify the mechanisms of change responsive to specific contextual elements, we will present a review of annual MIECHV Program Reports, results of the HVP survey and key informant interviews, and relevant literature. The discussion will be framed by broad questions such as “What would it take to integrate MB into your current workflow?”; “What resources would you need to successfully facilitate implementation of MB?”; and “How can we best support you and your family during pregnancy?” to allow for breadth and depth of responses.

The transcripts of the discussions will inform the development of a context-adapted implementation facilitation through implementation mapping between the items and the standard elements of implementation facilitation [45]. For example, home visitors might identify a need for ongoing training in MB which would result in the facilitation adaptations to include additional education and capacity building sessions, while a program recipient might highlight stigma around mental health or need for participant education materials. These types of participant-identified issues will allow for adapted facilitation-trained home visiting supervisors to incorporate support and capacity building for the home visitors specifically to address those issues. We will reconvene the groups virtually to present the findings and propose the adapted facilitation for feedback and adjustment.

Three variable implementation effectiveness context Cluster Randomized Control Trial (CRT)

We propose a parallel two-arm CRT in Iowa and Indiana. Randomization will occur at the home visiting supervisor level (Fig. 2). We will randomly assign home visiting supervisors (Fig. 2) in a 1:1 plus adapted facilitation. Our 65% participation rate is based on a recent survey of home ratio to a control arm or an implementation strategy: 1) the control arm will receive standard MB training and implementation support and the 2) implementation facilitation arm will receive standard MB training visitor well-being in Iowa that had a response rate of 60%; we expect we will have a higher rate for our study as it is offering a training opportunity rather than completion of a survey. Outcome data to be used in Aims 2 and 3 will still be captured from home visitors and HVP recipients for supervisors who refuse to participate in the trial as MB training through administrative records. Therefore, adoption and fidelity outcomes will still be captured for home visitors and clinical outcomes on HVP recipients who are not part of the CRT.

Fig. 2
figure 2

Study design

Full size image

Three-variable hybrid design

Three-variable hybrid designs, while relatively new, seek to make explicit what many hybrid implementation effectiveness trials already do implicitly, which is take into account the role of contextual determinants in implementation [46,47,48]. Recent implementation research identifies the need to assess context explicitly as it impacts both implementation strategies and the intervention itself [49]. As the science moves toward more fully embedding equity into implementation work, it is critical that researchers incorporate context and stakeholder engagement more fully. Doing so will allow us to better measure and track the equitable delivery of evidence-based strategies and practices, in particular through a better understanding of structural factors (e.g., environmental exposures, systemic bias, etc.) [49,50,51]. The three-variable hybrid design is uniquely suited for the proposed implementation trial (expanding on a type 3) because 1) both the intervention and the implementation strategy are evidence-based; therefore, it is critical to test their relationship to context; and 2) the heterogeneity of contextual determinants (HVPs, funding, training, population, structural and environmental variables) requires explicit consideration.

Home visitors in control arm and outside the CRT

Home visitors in the control group and those not part of the CRT (i.e., declined participation) will receive standard MB training as developed and tested in RCT and hybrid type 2 trial, which involves 1.5-day group trainings. Following the training, control arm home visitors will receive standard support from their supervisors while those outside the trial will receive no support from their supervisors with respect to the MB intervention.

Adapted implementation facilitation arm

Home visitors in the adapted facilitation group will receive standard MB training plus adapted facilitation delivered by home visiting supervisors trained in adapted implementation facilitation. We will use facilitation adapted by the findings from Aim 1, and based on evidence that recognizes while dynamic and flexible, facilitation has core activities that have been identified as effective at different stages of implementation and for different types of implementers [52, 53]. The home visitor supervisors (internal facilitators) will receive a half-day training in adapted facilitation and ongoing self-driven and identified support from the external facilitators (i.e., research team members). In this pre- and early implementation, the external facilitators will employ core components of facilitation, including rapport and trust building, priority and goal setting, and clarifying roles and responsibilities, to support home visiting supervisors’ training as internal facilitators [54]. The training and ongoing support will be shaped by the findings from Aim 1 and may include issues such as: different needs and preferences of rural vs. urban HVP recipients; additional mental health resources for facilitators beyond MB-related issues; and if the HVP has existing mental health provision other issues the home visitors may face such as stigma, obstetric deserts, food insecurity, etc.

During implementation, the adapted facilitation-trained home visiting supervisors will draw upon core components of the strategy to support the home visitors in the front-line implementation of MB. These may include addressing resistance to change in specific home visiting contexts (e.g., rural) and supporting accountability by providing real-time feedback on implementation performance [54]. Additionally, internal facilitators will employ interactive problem-solving focused on supporting the home visitors to implement MB – working with pregnant people, challenges with resources, additional training needs, etc. – based on understanding the individual communities, contexts, and recipients’ and home visitors’ needs over the course of the project [35, 55, 56]. In the sustainment phase, the external and internal facilitators will continue to provide context-specific support as needed [54].

Randomization

Randomization will occur at the level of the home visiting supervisor. Supervisors will be randomized by study personnel who will not complete follow-up data collection. Using a sequence generated by study statistician, randomization will occur in permuted blocks of 4 and 6 and will be stratified by state. Allocation to intervention arm will remain concealed until inclusion and exclusion have been determined. This approach minimizes potential for experimenter and participant bias by protecting the randomization sequence and maintaining concealment of intervention allocation until the last moment. The home visiting supervisors will be aware of the trial and whether they are receiving training in adapted facilitation or in the control group. However, the home visitors and HVP recipients will not be aware there is an CRT in process so will not know to which treatment group they have been allocated or if they are not included in the cRCT.

Trial data collection and analysis

The first objective is to analyze the uptake of MB by home visitors who receive adapted facilitation from their supervisors compared to those home visitors who receive standard support from their supervisors and those supervisors who refuse participation. This rapid ethnographic assessment (REA) will involve collecting qualitative and quantitative data (i.e., semi-structured interviews, observations, FGDs, questionnaires, and geospatial data) concurrently and iteratively and triangulating the results [27, 57, 58]. The questionnaires will allow us to measure implementation outcomes in real time and the qualitative data will provide a more nuanced understanding of why and how MB will be more readily adopted by certain home visitors, in certain HVP models, and whether delivery of the intervention varies for populations with different characteristics. As a secondary outcome, we will investigate which contextual factors identified in Aim 1 mediate or moderate the impact of adapted facilitation on implementation outcomes. The geospatial data will provide measurable indicators that will allow us to investigate the mediating or moderating effect of structural and systemic factors. We will also be able to examine factors that impact the choice of home visitor supervisors to participate in the CRT and how that impacts adoption and fidelity of MB among home visitors that are not part of the CRT.

Qualitative component

Semi-structured interview and FGD guides

The research team will refine guides for the FGDs and interviews based on literature review, the iPARIHS and CFIR 2.0 frameworks, and any in vivo topics that may emerge from the Aim 1 formative evaluation. The guides will then be pilot tested with nonparticipants at partner organizations familiar with the topic and experiences of home visiting. This pilot testing will ensure that the questions are comprehendible and relevant to the Iowa and Indiana contexts. Research team members will conduct in-person or virtual interviews based on participant preferences and scheduling availability. The FGDs and interviews will be recorded, transcribed, and reviewed for accuracy. All transcripts will be analyzed using MAXQDA, secure qualitative data software [59].

Ethnographic observations

We will leverage existing meetings and site visits currently being conducted by IDHHS and IDOH with a combination of in-person and audio-recorded observations of: 1) weekly meetings between supervisors and visitors across all clusters in both arms to assess supervisor practice of adapted facilitation compared to standard implementation; 2) the existing biannual site visits conducted by the departments of health across a sample of models and staff, one of which is for monitoring, compliance, and needs assessment and the other for shadowing home visits, attending parent group meetings, etc. We will also attend or record monthly contractor calls, quarterly home visitor expert panel meetings, and quarterly participant advisory meetings.

Data analysis

The qualitative analysis team will conduct qualitative analyses using both deductive rapid and inductive in-depth methods. The analysis team will complete a rapid ethnographic analysis (e.g., template analysis, document review, etc.) throughout the data collection period to allow for real-time actionable adaptions to ongoing facilitation, and understand processes about MB and other main themes in a timely way [27, 57, 58]. Using a template developed with deductive, a priori iPARIHS and CFIR 2.0 domains particularly focused on inner setting variables including access to knowledge, culture, relational connections, compatibility, and structural characteristics, the analysis will also capture inductive, emergent themes. The analysis team will read a subset of transcripts to pilot the template and establish agreement and consistency among team members. Each transcript will be summarized independently by two members and then summaries will be reconciled until consensus is reached. The summaries will be entered into a matrix organized by the same domains and each domain will be analyzed vertically through analytic memos shared and discussed with the team. Ongoing feedback will be shared through external facilitation with HV supervisors (i.e., internal facilitators) to inform real-time adaptions to facilitation. In addition, the research team will conduct a thematic analysis that will allow for greater depth of analysis that can inform our understanding of the implementation outcomes by incorporating questions about differences in delivery and health outcomes for different populations in different contexts. The research team will read a subset of transcripts to generate a preliminary codebook, which the team will use to code a subset of three interviews independently and compare coding and examine agreement. We will conduct thematic analysis of each transcript independently to identify theoretical domains and constructs and to identify patterns. Once the threshold is reached, all subsequent coding will be performed by two coders.

Quantitative component

Education and implementation questionnaires

We will use the validated baseline and follow-up education and retention questionnaire developed by the MB trainers [25] to assess knowledge and attitudes about MB and perceived self-efficacy among all the home visitors. We will also use a 5-item REDCap questionnaire for the home visiting supervisors (participating in the CRT) that will include closed questions about number of home visitors supervised, hours spent on supervision/facilitation, and open-ended questions about challenges and opportunities around supervision and additional support and resources needed. Fidelity will be assessed using the MB Home Visitor Fidelity Rating Form, which will be completed by the home visitors after each session.

Home visit documentation

We will leverage the DAISEY data system currently used by IDHHS which contains a home visit record form, completed within 48 h of a home visit [60]. We will develop a similar system to collect the same data from the Indiana HVPs.

Geospatial data

We will use the publicly available environmental justice screening and mapping tool (EJSCREEN), developed, and maintained by the Environmental Protection Agency (EPA). This nationally consistent dataset and methodology will allow us to calculate "EJ indexes" which assess the cumulative impact of environmental exposures (i.e., pollution, land use, green space, etc.) together with health and social vulnerabilities based on participant zip codes [61, 62]. EJ indexes will be our proxy variable for structural bias, which encompasses the complex interplay of systems that reinforce and perpetuate discrimination including housing, health care access, environmental exposures, etc. [63]. These geospatial indicators will provide quantifiable, intersectional contextual data as the critical and inextricable third variable in our model to help us understand how environmental and structural factors mediate or moderate both our implementation but also our clinical health outcomes [64]. These variables align with contextual variables with the CFIR outer setting domain, including financing, systemic conditions, policies and laws, and partnerships and connections [30].

In the analysis of cluster randomized trials, the generalize linear mixed model with the logit link function will be used to assess the effect of the adapted facilitation on implementation outcomes adoption and fidelity. We will include context specific variables (Table 1) in the model including type of HVP model, funding mechanism, EJ indices, and patient characteristics (race, age, geographic location) to identify the mediating or moderating effects. To account for the intracluster correlations, we include random effects for the level of home visitor and recipient in the model. Intent-to-treat analyses will be conducted on all those in the CRT. We will also examine implementation outcomes among home visitors that are not part of the CRT because their supervisor declined to participate and determine which contextual factors contribute to 1) participation in the trial; and 2) adoption and fidelity outcomes compared to the control and intervention arms of the CRT.

Table 1 Measures, definitions, instruments, and sources
Full size table

Power analysis

We defined cluster size (K) as the total number of HV recipients across home visitors within each supervisor. Given the K = 25, we calculated power based on different intracluster correlations (IC). We provided the total number of home visitor supervisors (N) required to achieve at least 90% power at a 5% Type I error rate (Table 2). We are well powered with the current number of home visitor supervisors estimated in each state to have ~ 100 per arm. Based on the work of Tandon et al. where the fidelity was ~ 35% we estimated the power needed to increase fidelity to 50%. The numbers were not drastically affected when changing K to 15 or 20.

Table 2 Power analysis for aim 2
Full size table

Clinical outcomes

We will test the hypothesis that recipients in the adapted facilitation arm have better clinical outcomes (e.g., lower depressive symptoms and perceived stress) compared to recipients receiving standard implementation of MB.

Data collection

Depression screening and perceived stress questionnaires. We will use similar baseline and follow-up surveys used in the effectiveness trials conducted by Tandon et al. We will harmonize with current practices being utilized among home visiting programs for depression and stress screening. We will ask that home visitors deliver both the EPDS and the Perceived Stress Scale (PSS), which is a 4-item questionnaire to their home visiting recipients as part of standard practice.

Statistical analysis

In the analysis of cluster randomized trials, the linear mixed model will be used to assess the effect of the adapted facilitation on each of two outcomes depressive symptoms and PSS at month 6 controlling for the baseline outcome. We will include the context specific variables mentioned in Aim 2, including the EJ indexes, and the random effects for the level of home visitor and recipient in the model. Intent-to-treat analyses will be conducted on all those in the CRT. We will also compare EPDS and PSS outcomes of those not in the CRT to both the control and intervention arms and identify the contextual factors that affect these outcomes.

Power analysis

We provided the total number of home visiting supervisors (N) required to achieve at least 90% power at a 5% Type I error rate (Table 3). We based our power calculations on the Tandon et al. study which found those receiving MB had Beck Depression Inventory II (BDI) and PSS scores that were 1.014 and 0.639 lower than the control group that did not receive the intervention. Because in this study all groups are receiving the intervention, we hypothesize that the adapted facilitation group will have depressive symptom scores and PSS scores that are lower (by approximately half) to the original study. We are well powered at all IC’s to detect the difference in depressive scores and at minimal IC’s for PSS.

Table 3 Power analysis for aim 3
Full size table

Data triangulation

We will interrogate relationships between administrative, qualitative, quantitative, and geospatial data with a focus on understanding local perspectives on the variables to inform our analysis. We will develop preliminary matrices in our initial review of the data and will conduct pattern analysis to capture convergent, latent themes across all data sources. During this phase of analysis, our full research team will meet frequently to integrate qualitative and quantitative data using side-by-side comparisons of the qualitative data and joint displays, which include qualitative themes and selected dimensions from the quantitative data. The resulting triangulation will identify the effects of structural and programmatic determinants on both the fidelity and adoption of the intervention and then more clearly disentangle how contextual determinants impact clinical outcomes for program recipients. We will use participatory interpretation with our stakeholders by explaining our methods, presenting them with the results, and applying meaning together [65, 66]. For example, we will show relationships between lack of resources and fidelity and ask, ‘This is happening in your program/community, why do you think that is?’. By interpreting the results together, we will be able to disseminate findings quickly and effectively to relevant stakeholder groups in the forms of public presentations, state reports, white papers, conference papers and manuscripts in peer-reviewed journals.

Discussion

Prioritizing user preferences to drive implementation is essential for the sustainability of mental health interventions that improve not only maternal well-being but that of the whole family. Resources available from patchwork funding sources are scarce given the multitude of other priority issues and unexpected pandemics; solutions that are responsive to local conditions are crucial. Implementation studies that are co-developed with stakeholders with the goal of sustainability can expedite the translation of research findings into practice. By understanding the role of specific contextual variables together with the effectiveness of the intervention and the best strategies for implementation, stakeholders can quickly scale up to new contexts. The next steps will be to provide the MB team with information about adapting to new contexts. Additionally, these study findings will inform the process for implementing future evidence-based practices into often heterogenous public health programs to improve health equitably for all populations. Finally, our theoretical and methodological approach to understanding context as the variable of interest in pragmatic implementation trials may be used broadly by researchers, implementers, and policy makers across international contexts, for example when adapting an intervention developed in a high-income country to low- or middle-income countries.

Limitations

It is possible that we may not enroll enough home visitors or supervisors to obtain maximum power. The original effectiveness trial trained 473 home visitors with 51% delivering at least one MB session and completing the follow-up survey. Although the CRT examines home visiting supervisors delivering adapted facilitation, we estimate collecting administrative outcome data on all home visitors and HVP recipients, regardless of supervisor participation in the trial. We can alternatively randomize directly at the home visitor level, which would substantially increase our power to detect the effects. The external facilitators would then work with groups of home visitors delivering the adapted facilitation and home visitors, not the supervisors, would be randomized to the adapted facilitation intervention.

Conclusion

The overall goal of this study is to engage with community members to improve the equitable delivery and uptake of a maternal mental health intervention, MB, into preexisting HVPs in Iowa and Indiana, which is essential to reduce unacceptable disparities in maternal morbidity and mortality. We will accomplish this overall goal through 1) needs assessment of contextual factors affecting facilitation as an implementation strategy and the integration of MB into current programs and models; 2) analyzing the effects of context and adapted facilitation on implementation outcomes (primarily adoption and fidelity), and 3) exploring our hypothesis that recipients in the adapted facilitation arm of the trial will have better clinical outcomes.

Data availability

The datasets used and/or analyzed in the current study will be available on the NIMH Data Archive.

Abbreviations

BIPOC:

Black, Indigenous, People of Color

SES:

Socioeconomic status

HVP:

Home visiting programs

MB:

Mothers and Babies

IDHHS:

Iowa Department of Health and Human Services

IDOH:

Indiana Department of Health

CFIR 2.0:

Consolidated Framework for Implementation Research

i-PARIHS:

Integrated Promoting Action on Research Implementation in Health Services

RE-AIM:

Reach, Effectiveness, Adoption, Implementation, and Maintenance

HRSA:

Health Resources and Services Administration

MIECHV:

Maternal, Infant, and Early Childhood Home Visiting

NFP:

Nurse Family Partnership

CRT:

Cluster Randomized Control Trial

FGD:

Focus group discussions

EJScreen:

Environmental justice screening and mapping tool

EPA:

Environmental Protection Agency

EPDS:

Edinburgh Perceived Depression Scale

PSS:

Perceived Stress Scale

References

  1. Kimmel M. Maternal mental health matters. N C Med J. 2020;81(1):45–50.

    PubMed  Google Scholar 

  2. Organization WH. COVID-19 pandemic triggers 25% increase in prevalence of anxiety and depression worldwide. Retrieved from World Health Organization: https://www.who.int/news/item/02-03-2022-covid-19-pandemic-triggers-25-increase-in-prevalence-of-anxiety-and-depression-worldwide. 2022.

  3. Shonkoff JP, Garner AS, Child CoPAo, Family Health, CoEC, Adoption,, Dependent Care, Developmental So, et al. The lifelong effects of early childhood adversity and toxic stress. Pediatrics. 2012;129(1):e232–46.

    Article  PubMed  Google Scholar 

  4. Muzik M, Bocknek EL, Broderick A, Richardson P, Rosenblum KL, Thelen K, et al. Mother–infant bonding impairment across the first 6 months postpartum: the primacy of psychopathology in women with childhood abuse and neglect histories. Arch Womens Ment Health. 2013;16(1):29–38.

    Article  PubMed  Google Scholar 

  5. Lindahl V, Pearson JL, Colpe L. Prevalence of suicidality during pregnancy and the postpartum. Archives of Women’s Mental Health. 2005;8(2):77–87.

    Article  PubMed  CAS  Google Scholar 

  6. Woolhouse H, Gartland D, Mensah F, Brown S. Maternal depression from early pregnancy to 4 years postpartum in a prospective pregnancy cohort study: implications for primary health care. BJOG. 2015;122(3):312–21.

    Article  PubMed  CAS  Google Scholar 

  7. Lazareck S, Robinson JA, Crum RM, Mojtabai R, Sareen J, Bolton JM. A longitudinal investigation of the role of self-medication in the development of comorbid mood and drug use disorders: findings from the National Epidemiologic Survey on Alcohol and Related Conditions (NESARC). J Clin Psychiatry. 2012;73(5):10579.

    Article  Google Scholar 

  8. Cox EQ, Sowa NA, Meltzer-Brody SE, Gaynes BN. The perinatal depression treatment cascade: baby steps toward improving outcomes. J Clin Psychiatry. 2016;77(9):20901.

    Article  Google Scholar 

  9. Palladino CL, Singh V, Campbell J, Flynn H, Gold K. Homicide and suicide during the perinatal period: findings from the National Violent Death Reporting System. Obstet Gynecol. 2011;118(5):1056.

    Article  PubMed  PubMed Central  Google Scholar 

  10. Mukherjee S, Trepka MJ, Pierre-Victor D, Bahelah R, Avent T. Racial/ethnic disparities in antenatal depression in the United States: a systematic review. Matern Child Health J. 2016;20(9):1780–97.

    Article  PubMed  Google Scholar 

  11. Míguez MC, Vázquez MB. Risk factors for antenatal depression: a review. World J Psychiatry. 2021;11(7):325.

    Article  PubMed  PubMed Central  Google Scholar 

  12. Nidey N, Tabb KM, Carter KD, Bao W, Strathearn L, Rohlman DS, et al. Rurality and risk of perinatal depression among women in the United States. J Rural Health. 2020;36(1):9–16.

    Article  PubMed  Google Scholar 

  13. Ostmo P, Rosencrans J. Maternal Health Disparities: An Intersection of Race and Rurality. Rural Health Research Gateway. 2022.

  14. Reinert M, Fritze D, Nguyen T. The state of mental health in America 2022. 2021.

    Google Scholar 

  15. Ungar L, Simon C. Which States Have the Worst Maternal Mortality? 50 States. 2018.

  16. Tabb KM, Bentley B, Leano MP, Simonovich SD, Nidey N, Ross K, et al. Home visiting as an equitable intervention for perinatal depression: a scoping review. Front Psychiatry. 2022;13:826673.

    Article  PubMed  PubMed Central  Google Scholar 

  17. Jennings MC, Pradhan S, Schleiff M, Sacks E, Freeman PA, Gupta S, et al. Comprehensive review of the evidence regarding the effectiveness of community-based primary health care in improving maternal, neonatal and child health: 2. maternal health findings. J Glob Health. 2017;7(1):010902.

    Article  PubMed  PubMed Central  Google Scholar 

  18. Perry DF, Tandon SD, Edwards K, Mendelson T. Preventing perinatal depression through home visiting: the mothers and babies course. Zero to Three. 2014;34(5):45–51.

  19. Ammerman RT, Putnam FW, Bosse NR, Teeters AR, Van Ginkel JB. Maternal depression in home visitation: a systematic review. Aggress Violent Beh. 2010;15(3):191–200.

    Article  Google Scholar 

  20. Singla DR, Lawson A, Kohrt BA, Jung JW, Meng Z, Ratjen C, et al. Implementation and effectiveness of nonspecialist-delivered interventions for perinatal mental health in high-income countries: a systematic review and meta-analysis. JAMA Psychiatry. 2021;78(5):498–509.

    Article  PubMed  Google Scholar 

  21. Tandon SD, Ward EA, Hamil JL, Jimenez C, Carter M. Perinatal depression prevention through home visitation: a cluster randomized trial of mothers and babies 1-on-1. J Behav Med. 2018;41(5):641–52.

    Article  PubMed  Google Scholar 

  22. McFarlane E, Burrell L, Duggan A, Tandon D. Outcomes of a randomized trial of a cognitive behavioral enhancement to address maternal distress in home visited mothers. Matern Child Health J. 2017;21(3):475–84.

    Article  PubMed  Google Scholar 

  23. Tandon SD, Perry DF, Mendelson T, Kemp K, Leis JA. Preventing perinatal depression in low-income home visiting clients: a randomized controlled trial. J Consult Clin Psychol. 2011;79(5):707.

    Article  PubMed  Google Scholar 

  24. Tandon SD, Leis JA, Mendelson T, Perry DF, Kemp K. Six-month outcomes from a randomized controlled trial to prevent perinatal depression in low-income home visiting clients. Matern Child Health J. 2014;18(4):873–81.

    Article  PubMed  PubMed Central  Google Scholar 

  25. Tandon SD, McGown M, Campbell L, Smith JD, Yeh C, Brady C. Results from an effectiveness-implementation evaluation of a postpartum depression prevention intervention delivered in home visiting programs. J Affect Disord. 2022;315:113–20.

    Article  PubMed  Google Scholar 

  26. Kemp CG, Wagenaar BH, Haroz EE. Expanding hybrid studies for implementation research: intervention, implementation strategy, and context. Front Public Health. 2019;7:325.

    Article  PubMed  PubMed Central  Google Scholar 

  27. Sangaramoorthy T, Kroeger KA. Rapid ethnographic assessments: A practical approach and toolkit for collaborative community research. London: Routledge; 2020.

  28. Vindrola-Padros C, Johnson GA. Rapid Techniques in qualitative research: a critical review of the literature. Qual Health Res. 2020;30(10):1596–604.

    Article  PubMed  Google Scholar 

  29. Steffen KM, Holdsworth LM, Ford MA, Lee GM, Asch SM, Proctor EK. Implementation of clinical practice changes in the PICU: a qualitative study using and refining the iPARIHS framework. Implement Sci. 2021;16(1):15.

    Article  PubMed  PubMed Central  Google Scholar 

  30. Damschroder L, Reardon C, Widerquist MO, Lowery J. The Updated Consolidated Framework for Implementation Research: CFIR 2.0. Research Square; 2022.

  31. Seers K, Cox K, Crichton NJ, Edwards RT, Eldh AC, Estabrooks CA, et al. FIRE (facilitating implementation of research evidence): a study protocol. Implement Sci. 2012;7(1):25.

    Article  PubMed  PubMed Central  Google Scholar 

  32. Hans SL, White BA. Teenage childbearing, reproductive justice, and infant mental health. Infant Ment Health J. 2019;40(5):690–709.

    Article  PubMed  PubMed Central  Google Scholar 

  33. Crear-Perry J, Hassan A, Daniel S. Advancing birth equity in a post-Dobbs US. JAMA. 2022;328(17):1689–90.

    Article  PubMed  Google Scholar 

  34. Tabb KM, Bentley B, Pineros Leano M, Simonovich SD, Nidey N, Ross K, et al. Home visiting as an equitable intervention for perinatal depression: a scoping review. Front Psychiatry. 2022;13:826673.

    Article  PubMed  PubMed Central  Google Scholar 

  35. Kirchner JE, Dollar KM, Smith JL, Pitcock JA, Curtis ND, Morris KK, et al. Development and preliminary evaluation of an implementation facilitation training program. Implement Res Pract. 2022;3:26334895221087475.

    Article  PubMed  PubMed Central  Google Scholar 

  36. Rycroft-Malone J. The PARIHS framework—a framework for guiding the implementation of evidence-based practice. J Nurs Care Qual. 2004;19(4):297–304.

    Article  PubMed  Google Scholar 

  37. Weiss AB, Newnam KM, Wyles C, Shea K, Gephart SM. Exploring internal facilitators’ experience With NeoECHO to Foster NEC prevention and timely recognition through the iPARIHS Lens. Adv Neonatal Care. 2021;21(6):462–72.

    Article  PubMed  PubMed Central  Google Scholar 

  38. Proctor E, Silmere H, Raghavan R, Hovmand P, Aarons G, Bunger A, et al. Outcomes for implementation research: conceptual distinctions, measurement challenges, and research agenda. Adm Policy Ment Health. 2011;38(2):65–76.

    Article  PubMed  Google Scholar 

  39. Darius Tandon S, Leis JA, Ward EA, Snyder H, Mendelson T, Perry DF, et al. Adaptation of an evidence-based postpartum depression intervention: feasibility and acceptability of mothers and babies 1-on-1. BMC Pregnancy Childbirth. 2018;18(1):1–9.

    Article  Google Scholar 

  40. Ritchie MJ, Kirchner JE, Parker LE, Curran GM, Fortney JC, Pitcock JA, Bonner LM, Kilbourne AM. Evaluation of an implementation facilitation strategy for settings that experience significant implementation barriers. Implement Sci. 2015;10:1–3.

  41. Baskerville NB, Liddy C, Hogg W. Systematic review and meta-analysis of practice facilitation within primary care settings. Ann Fam Med. 2012;10(1):63–74.

    Article  PubMed  PubMed Central  Google Scholar 

  42. Mignogna J, Hundt NE, Kauth MR, Kunik ME, Sorocco KH, Naik AD, et al. Implementing brief cognitive behavioral therapy in primary care: a pilot study. Transl Behav Med. 2014;4(2):175–83.

    Article  PubMed  PubMed Central  Google Scholar 

  43. Smith JL, Ritchie MJ, Kim B, Miller CJ, Chinman MJ, Kelly PA, et al. Getting to fidelity: Scoping review and expert panel process to identify core activities of implementation facilitation strategies. SocArXiv. 2022;25.

  44. Schroeck FR, Ould Ismail AA, Haggstrom DA, Sanchez SL, Walker DR, Zubkoff L. Data-driven approach to implementation mapping for the selection of implementation strategies: a case example for risk-aligned bladder cancer surveillance. Implement Sci. 2022;17(1):58.

    Article  PubMed  PubMed Central  Google Scholar 

  45. Fernandez ME, Ten Hoor GA, van Lieshout S, Rodriguez SA, Beidas RS, Parcel G, et al. Implementation mapping: using intervention mapping to develop implementation strategies. Front Public Health. 2019;7:158.

    Article  PubMed  PubMed Central  Google Scholar 

  46. Pearson M, Jackson G, Jackson C, Boland J, Featherstone I, Huang C, et al. Improving the Detection, Assessment, Management and Prevention of Delirium in Hospices (the DAMPen-D study): protocol for a co-design and feasibility study of a flexible and scalable implementation strategy to deliver guideline-adherent delirium care. BMJ Open. 2022;12(7):e060450.

    Article  PubMed  PubMed Central  Google Scholar 

  47. Kariyawasam D, Soukup T, Parsons J, Sevdalis N, Baldellou Lopez M, Forde R, et al. Protocol for a feasibility study and process evaluation of a psychosocially modelled diabetes education programme for young people with type 1 diabetes: the Youth Empowerment Skills (YES) programme. BMJ Open. 2022;12(6):e062971.

    Article  PubMed  PubMed Central  Google Scholar 

  48. Kirk JW, Nilsen P, Andersen O, Stefánsdóttir NT, Grønfeldt B, Brødsgaard R, et al. Adaptations and modifications to a co-designed intervention and its clinical implementation: a qualitative study in Denmark. BMC Health Serv Res. 2021;21(1):1108.

    Article  PubMed  PubMed Central  Google Scholar 

  49. Beidas RS, Dorsey S, Lewis CC, Lyon AR, Powell BJ, Purtle J, et al. Promises and pitfalls in implementation science from the perspective of US-based researchers: learning from a pre-mortem. Implement Sci. 2022;17(1):55.

    Article  PubMed  PubMed Central  Google Scholar 

  50. Shelton RC, Adsul P, Oh A. Recommendations for addressing structural racism in implementation science: a call to the field. Ethn Dis. 2021;31(Suppl 1):357–64.

    Article  PubMed  PubMed Central  Google Scholar 

  51. Shelton RC, Adsul P, Oh A, Moise N, Griffith DM. Application of an antiracism lens in the field of implementation science (IS): recommendations for reframing implementation research with a focus on justice and racial equity. Implement Res Pract. 2021;2:26334895211049482.

  52. Swindle T, Rutledge JM, Martin J, Curran GM. Implementation fidelity, attitudes, and influence: a novel approach to classifying implementer behavior. Implement Sci Commun. 2022;3(1):60.

    Article  PubMed  PubMed Central  Google Scholar 

  53. Smith JL, Ritchie MJ, Kim B, Miller CJ, Chinman M, Kelly PA, et al. Getting to fidelity: scoping review and expert panel process to identify core activities of implementation facilitation strategies. 2022.

    Google Scholar 

  54. Kilbourne AM, Geng E, Eshun-Wilson I, Sweeney S, Shelley D, Cohen DJ, et al. How does facilitation in healthcare work? Using mechanism mapping to illuminate the black box of a meta-implementation strategy. Implement Sci Commun. 2023;4(1):53.

    Article  PubMed  PubMed Central  Google Scholar 

  55. Elnitsky C, Powell-Cope G, Besterman-Dahan K, Rugs D, Ullrich P. Implementation of safe patient handling in the US Veterans Health System: a qualitative study of internal facilitators’ perceptions. Worldviews Evidence-Based Nurs. 2015;12(4):208–16.

    Article  CAS  Google Scholar 

  56. Matthews K, Morgan I, Davis K, Estriplet T, Perez S, Crear-Perry JA. Pathways To equitable and antiracist maternal mental health care: insights from black women stakeholders: study examines pathways to equitable and antiracist maternal mental health care. Health Aff. 2021;40(10):1597–604.

    Article  Google Scholar 

  57. Vindrola-Padros C. Rapid ethnographies: A practical guide. Cambridge: Cambridge University Press; 2021.

  58. Vindrola-Padros C, Vindrola-Padros B. Quick and dirty? A systematic review of the use of rapid ethnographies in healthcare organisation and delivery. BMJ Qual Saf. 2018;27(4):321–30.

    Article  PubMed  Google Scholar 

  59. Kuckartz U, Rädiker S. Analyzing qualitative data with MAXQDA. Cham: Springer; 2019.

  60. DAISEY IFS. DAISEY: Quality Home Visiting Initiatives: University of Kansas Center for Public Partnerships and Research; 2016. Available from: https://daiseyiowa.daiseysolutions.org/.

  61. Lee C. A game changer in the making? Lessons from states advancing environmental justice through mapping and cumulative impact strategies. Envtl L Rep. 2020;50:10203.

    Google Scholar 

  62. U.S. Environmental Protection Agency. Environmental Justice (EJ) SCREEN Indexes 2021. Available from: https://edg.epa.gov/metadata/catalog/main/home.page.

  63. Van Horne YO, Alcala CS, Peltier RE, Quintana PJ, Seto E, Gonzales M, et al. An applied environmental justice framework for exposure science. J Eposure Sci Environ Epidemiol. 2023;33(1):1–11.

    Article  Google Scholar 

  64. McCue K, DeNicola N. Environmental exposures in reproductive health. Obstet Gynecol Clin North Am. 2019;46(3):455–68.

    Article  PubMed  Google Scholar 

  65. Krause H. The Data Equity Framework. In: Count WA, editor. 2021.

  66. Fletcher AJ, MacPhee M, Dickson G. Doing participatory action research in a multicase study: a methodological example. Int J Qual Methods. 2015;14(5):1609406915621405.

    Article  Google Scholar 

Download references

Acknowledgements

We thank our collaborators at the Iowa Department of Health and Human Services and the Indiana Department of Health for their contributions to the conceptualization of the study and their partnership in this work.

Funding

The study is funded by the National Institute of Mental Health grant R01MH134474 (MPIs Elissa Faro and Kelli Ryckman). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Author information

Authors and Affiliations

  1. Department of Internal Medicine, University of Iowa Carver College of Medicine, 200 Hawkins Dr, Iowa City, IA, 52242, USA

    Elissa Z. Faro & Grace Swartzendruber

  2. Institute for Clinical and Translational Science, University of Iowa, Iowa City, IA, USA

    DeShauna Jones & Morolake Adeagbo

  3. Department of Biostatistics, University of Iowa College of Public Health, Iowa CIty, USA

    Hyunkeun Cho

  4. School of Social Work, University of Illinois at Urbana-Champaign, Urbana, IL, USA

    Karen M. Tabb

  5. Department of Medical Social Sciences, Northwestern University Feinberg School of Medicine, Chicago, IL, USA

    S. Darius Tandon

  6. Indiana University School of Public Health, Bloomington, IN, USA

    Kelli Ryckman

Authors
  1. Elissa Z. Faro
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. DeShauna Jones
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Morolake Adeagbo
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Hyunkeun Cho
    View author publications

    You can also search for this author inPubMed Google Scholar

  5. Grace Swartzendruber
    View author publications

    You can also search for this author inPubMed Google Scholar

  6. Karen M. Tabb
    View author publications

    You can also search for this author inPubMed Google Scholar

  7. S. Darius Tandon
    View author publications

    You can also search for this author inPubMed Google Scholar

  8. Kelli Ryckman
    View author publications

    You can also search for this author inPubMed Google Scholar

Contributions

EZF and KR revised the grant proposal and wrote the current protocol manuscript. DJ and MA contributed to the qualitative methodology. HC provided the statistical methods and calculated the power estimates. SDT and KMT provided content expertise on the intervention and the HVP context. GS contributed expertise as a community full-spectrum doula. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Elissa Z. Faro.

Ethics declarations

Ethics approval and consent to participate

The University of Iowa Institutional Review Board has reviewed and approved this study IRB#202312148.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Faro, E.Z., Jones, D., Adeagbo, M. et al. Can an evidence-based mental health intervention be implemented into preexisting home visiting programs using implementation facilitation? Study protocol for a three variable implementation effectiveness context hybrid trial. Implementation Sci 19, 76 (2024). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s13012-024-01402-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s13012-024-01402-7

Keywords

Implementation Science

ISSN: 1748-5908