Skip to main content
Download PDF
Download ePub

Multi-sector determinants of implementation and sustainment for non-specialist treatment of depression and post-traumatic stress disorder in Kenya: a concept mapping study

Implementation Science Communications volume 6, Article number: 55 (2025) Cite this article

Abstract

Background

The global shortage of trained mental health workers disproportionately impacts mental health care access in low- and middle-income countries. In Kenya, effective strategies are needed to scale-up the workforce to meet the demand for depression and post-traumatic stress disorder treatment. Task-shifting – delegating specific tasks to non-specialist workers – is one workforce expansion approach. However, non-specialist workers remain underutilized in Kenya due to a paucity of research on how to scale-up and sustain such service models.

Methods

Purposive sampling was used to recruit experts from policy, healthcare practice, research, and mental health advocacy roles in Kenya (N = 30). Participants completed concept mapping activities to explore factors likely to facilitate or hinder a collaborative Ministry of Health-researcher training of the mental health non-specialist workforce. Participants brainstormed 71 statements describing determinants and implementation strategies, sorted and rated the importance and changeability of each. Multidimensional scaling and hierarchical cluster analysis quantified relationships between statements. The Exploration, Preparation, Implementation, and Sustainment (EPIS) framework guided cluster interpretation activities.

Results

Twelve determinant clusters were identified: 1) Current workforce characteristics, 2) Exploration considerations, 3) Preparation considerations, 4) Sustainment considerations, 5) Inner context implementation processes and tools, 6) Local capacity and partnerships, 7) Financing for community health teams, 8) Outer context resource allocation/policy into action, 9) Workforce characteristics to enhance during implementation, 10) Workforce implementation strategies, 11) Cross-level workforce strategies, and 12) Training and education recommendations. Cluster 8 was rated the most important and changeable.

Conclusion

Concept mapping offers a rapid, community-engaged approach for identifying determinants and implementation strategies to address workforce shortages. Organizing results by EPIS phases can help prioritize strategy deployment to achieve implementation goals. Scale-up and sustainment of the non-specialist workforce in Kenya requires formal partnerships between the Ministry of Health and community health worker teams to distribute financial resources and collaboratively standardize training curriculum.

Peer Review reports

Background

There is a growing and unmet need for mental healthcare globally. The Global Burden of Disease Study 2021 identified substantial increases in rates of disability adjusted life-years and years lived with disability for anxiety and depressive disorders between 2010 and 2021 [1]. Within the first year of the COVID-19 pandemic, globally measured rates of depression increased 25% [2]. Depression is expected to be a leading cause of burden of disease by 2030 [3]. The shortage of trained mental health workers is a pressing global health challenge disproportionately impacting low- and middle-income countries (LMICs) [4]. LMICs have an estimated shortage of 1.18 million mental health workers [5]. Effective strategies to scale up and sustain the mental health workforce are urgently needed to address common conditions including depression and post-traumatic stress disorder (PTSD).

Major depressive disorder and PTSD are two of the most prevalent mental disorders in Kenyan primary care populations [6,7,8]. Prior research investigating access to mental health services across Kenya’s counties found that in some counties, only 1.7% of individuals with a need for treatment received services [9]. To address this substantial treatment gap, in 2021, the Kenyan government launched the Kenya Mental Health Action Plan aimed at improving mental health system leadership and governance, increasing access to mental healthcare by integrating services with primary care, and strengthening mental health systems through investments in the workforce size and available training supports [10]. Mobilizing the local non-specialist mental health workforce can help address the existing significant mental health treatment gap in Kenya. Non-specialist health workers include any type of health workers (e.g., doctors, nurses, community and lay health workers) who are not specialists of mental healthcare, but who may have had some training for common conditions [11]. Non-specialist workforce interventions are effective for increasing access to treatment, reducing symptoms of PTSD, supporting recovery from depression and anxiety [11], and promoting culturally responsive care [12]. Yet the non-specialist workforce remains underutilized, in part due to a paucity of implementation research demonstrating strategies to effectively scale-up such interventions. [8]. Most studies of non-specialist services for depression and PTSD have focused primarily on behavioral interventions [13,14,15]. However, there is a need for both psychotherapeutic and pharmacological treatments for these mental health disorders [16,17,18,19,20,21,22].

This study applied concept mapping methods through an implementation science lens to identify implementation and sustainment determinants (i.e., barriers and facilitators) and cross-level alignment promoting factors to support non-specialist workforce scale up in Kenya’s public sector mental healthcare system to address depression and PTSD. To confront this research-practice knowledge gap, we conducted a concept mapping study [23] with diverse stakeholders from Kenya’s mental health system including policymakers, advocates, and researchers. We aimed to identify critical determinants and mechanisms of ongoing efforts to collaborate with the Ministry of Health, public health facilities and hospitals in training the non-specialist workforce in mental healthcare within Kenya’s public health system.

Methods

Concept mapping activities were conducted within the context of an effectiveness-implementation Hybrid type 1 larger parent grant that is a Sequential, Multiple, Assignment Randomized Trial (SMART) of two evidence-based treatments for depression and PTSD (the SMART-DAPPER study) in Kenya. The SMART-DAPPER parent trial randomized 2,710 patients in outpatient clinics at Kisumu County Hospital and other Kisumu County Ministry of Health facilities to receive either fluoxetine or Interpersonal Therapy (IPT) provided by non- mental health specialists (e.g., nurses, community health workers, teachers, church leaders, women’s group leaders, village chiefs/elders, traditional healers). The parent trial tested the effectiveness of and treatment fidelity to each evidence-based practice. The study team trained nurses and clinical officers to deliver fluoxetine, and non-specialist therapists participated in an IPT training course. Both training courses were delivered over multiple days and included subsequent proctored treatment sessions with multiple practice participants and supportive supervision [8]. Given the focus on non-specialists’ role in delivering IPT and fluoxetine, the study also aimed to investigate potential strategies to scale-up non-specialist mental healthcare delivery in Kenya in the event that non-specialist delivered IPT and fluoxetine was highly effective. The parent trial convened an Implementation Resource Team (IRT) [24, 25] comprised of cross-sector key informants to shepherd implementation activities across study aims. All IRT participants were then asked to participate in the concept mapping study to provide insights about how to leverage and sustain a non-specialist workforce to deliver mental healthcare. Additional details of the SMART-DAPPER parent study are described elsewhere [8]. This research was approved by the University of California San Francisco Institutional Review Board.

Concept mapping is a mixed methods (qualitative and quantitative) approach to meaningfully engage invested actors, collaborators, and end users in an implementation effort in the research process [23, 26,27,28]. Concept mapping can be used to foster communication about implementation efforts between actors across contexts, and incorporate their diverse perspectives, knowledge, and positionalities into implementation and sustainment planning. First, individual and group brainstorming takes place to enumerate potential determinants (i.e., implementation barriers and facilitators). Next, sorting activities and analyses are used to generate a conceptual framework and shared understanding relevant for the implementation context and clinical interventions. These initial steps use participant language to identify relevant determinants and potential implementation strategies [23]. Next, each statement is rated on its importance and changeability. Then quantitative methods are used to identify key determinants likely to affect scale-up depression and PTSD treatment.

Participants

The parent trial (SMART DAPPER) employed purposive sampling [29] to recruit participants with professional or lived experience roles in diverse sectors (i.e., policy, healthcare practice, research, and mental health advocacy) to inform the implementation of non-specialist delivered care for depression and PTSD in Kenya. Policy actors included Kenyan officials from national and county health entities. Healthcare practice perspectives included healthcare workers and leaders from health facilities and non-governmental organizations. University-affiliated researchers and consultants were included in the research perspective, and mental health advocates were recruited from non-governmental and other organizations involved in mental healthcare promotion including some individuals who were receiving IPT and fluoxetine. This group was identified as the IRT – a resource to provide ongoing key informant insights and feedback to the parent trial. The research team invited all IRT members to participate in concept mapping activities.

Theory

The Exploration, Preparation, Implementation, Sustainment (EPIS) framework guided data collection and interpretation activities. The EPIS framework is both a process and determinant framework that describes how determinants and relationships between entities in the local implementation context (inner context) and the multilevel sociopolitical and service delivery environments (outer context) can change across the four phases of implementation and influence implementation outcomes and sustainment of evidence-based practices [30, 31]. EPIS “bridging factors” represent relational ties, formal arrangements, and other processes for supporting outer and inner context alignment for implementation success and long-term sustainment [32]. Bridging factors can also include structures or ways of exchanging financial or political capital to promote policy transfer, ensuring national health priorities are pursued by local governments and adhered to in public sector clinics [33]. The present study documented exploration and preparation phase concept mapping activities that occurred, and plans for future implementation and sustainment activities. During the exploration phase, invested stakeholders across outer and inner contexts defined the need to improve mental health services, and agreed to focus on enhancing the non-specialty mental health workforce to augment depression and post-traumatic stress disorder service delivery. During the preparation phase, cross-context representing participants (i.e., IRT members) considered determinants (i.e., barriers, facilitators) and potential implementation strategies to expand the non-specialty mental health workforce to provide treatment for depression and PTSD. The implementation and sustainment phases were not observed, but concept mapping activities revealed key determinants and implementation strategies relevant to progress into these phases.

Data collection for concept mapping

Concept mapping activities occurred in six stages: (1) preparing participants, (2) generating ideas, (3) structuring ideas, (4) representation, (5) interpretation, and (6) utilization (Fig. 1) [23, 28]. During the preparation stage, IRT members met over a video call to discuss the unmet need for depression and PTSD care in Kisumu, Kenya. A member of the research team introduced the concept mapping process to IRT members and facilitated the group in generating a focal statement for concept mapping process: “What factors will facilitate or hinder a collaborative Ministry of Health-SMART DAPPER training of Kenyan mental health non-specialist workforce for scale up and sustainment of public sector mental healthcare?” IRT members were instructed to respond to the focal statement by generating concise statements regarding potential barriers and facilitators to training a non-specialist workforce to scale up and sustain public sector mental healthcare. Participants generated 40 unique statements during the initial call.

Fig. 1
figure 1

Concept mapping stages

Full size image

IRT members were asked to engage in additional individual idea generation (i.e., brainstorming) and structuring stage activities (i.e., sorting statements) via the web-based concept mapping platform, GroupWisdom.34 Participants received an emailed invitation to the platform where they encountered web-based prompts to guide them through the process. The study team used a web-based platform to conduct concept mapping activities (instead of in-person meetings) given the geographic dispersion of participants, the desire to promote asynchronous and anonymous responses to the focal statement, and the need for social distancing during the COVID-19 pandemic. IRT members were asked to respond to five questions about their knowledge of evidence-based practices for mental health treatment, educational experiences regarding depression and PTSD topics, their amount of previous experience participating in collaborative efforts to improve mental healthcare or advocating for the rights of people living will mental illness, and their perceived ability to enact changes to Kenya’s public-sector mental healthcare delivery system. Participants were then instructed to review the list of 40 brainstorming statements and contribute any additional relevant ideas in response to the focal statement. Participants could enter as many statements as they desired, and view statements generated by other participants. All statements were anonymously viewable to participants. The research team reviewed the list to remove duplicative ideas, combine similar statements, and ensure that only one concise idea was represented in each statement. A total of 71 unique statements were generated.

During the structuring the GroupWisdom platform enabled participants to electronically sort all statements into different ‘piles’ based on their perceived similarity, yielding as many piles as each participant deemed meaningful. Participants could name the piles if desired. Participants also rated each statement separately to indicate its degree of importance and changeability (e.g., how feasible or possible it is address or alter a barrier to implementation) using a scale of one to five. Participants first rated how important each determinant statement was to the implementation of training the Kenyan mental health non-specialist workforce for scale up and sustainment of public sector mental healthcare. Then participants rated how changeable each determinant was to address during implementation efforts.

Generating the concept map

Once data collection was completed, concept mapping procedures progressed to the representation stage. A similarity matrix for each participant was generated to indicate where each statement was, or was not, sorted together with other statements. Next, we conducted multidimensional scaling using the matrix data for all participants to place statements on a two-dimensional point map. Hierarchical cluster analysis was used to develop a cluster map depicting which statements cluster together into relatively mutually exclusive conceptual clusters of statements [34]. Hierarchical cluster analysis is an algorithm that is useful for identifying patterns in data, such as statements that are conceptually similar to each other and meaningfully distinct from other statements generated by participants. We used a hierarchical cluster analysis algorithm to identify patterns of statements that participants frequently sorted together, and then represented those clustered statements as visually closer together on the point and cluster maps. To conduct the hierarchical cluster analysis, the research team reviewed the 71 participant generated statements and reviewed the point and cluster maps to estimate the optimal number of clusters that would practically make sense and represent the statement data generated by participants. We estimated a series of models, each with an increasing number of potential clusters ranging from 10 to 16 clusters. To select the final model, the research team considered how well the number of clusters could be interpreted as meaningfully distinct themes and considered the model’s compatibility with our initial estimate. Average bridging values and spanning values for statements were calculated to describe the relationship between statements across clusters and to confirm that clusters were conceptually distinct. The research team interpreted cluster themes to assign cluster labels. Cluster labels were cross-walked to EPIS constructs [30, 31] (i.e., outer context, inner context, bridging factors, innovation factors) to enhance interpretability and use in guiding future global mental health implementation efforts.

Finally, we conducted pattern matching analysis to compare average ratings for the importance and changeability of each statement aggregated to the cluster level and measured the average correlation between these dimensions. Additionally, each statement’s importance and changeability score was plotted on a graph and divided into four quadrants using the mean of importance and changeability scores to model a ‘Go-Zones’ plot [23, 28]. Statements in quadrant 4 (i.e., upper right Go-Zone) have above average values for both importance and changeability and represent the focal statements that participants prioritized for addressing in future implementation efforts. A presentation summarizing the concept mapping methods and process was shared with IRT members.

Results

Sample characteristics

Thirty IRT members participated in concept mapping activities including: responding to participant experience questions (n = 30), brainstorming (n = 30 and sorting statements (n = 16), and rating importance and changeability (n = 15; Table 1). Most IRT members (70%) rated themselves as knowledgeable to very knowledgeable about evidence-based practices for mental health treatment. IRT members self-reported having formal (60.0%) and informal (56.7%) educational experiences with depression and PTSD, and 73.4% had “moderate” to “a lot” of experience advocating for the rights of people living with mental illness or advocating for mental healthcare services. Almost all members (93.3%) had prior experience participating in other collaborative, multisector efforts to improve mental healthcare. Most members (63.4%) said they had a “great extent” of ability to enact change in the delivery of evidence-based mental healthcare in Kenya’s public sector. Notably, none of the participants reported having no ability to enact change.

Table 1 Implementation Resource Team participant characteristics
Full size table

Final concept map

A 12-cluster model was selected as the final model. Figure 2 displays the final point and cluster map (i.e., ‘concept map’) illustrating relationships between the 71 statements grouped into 12 clusters. The clusters represented 1) Current workforce characteristics, 2) Exploration considerations, 3) Preparation considerations, 4) Sustainment considerations, 5) Inner context implementation processes and tools, 6) Local capacity and partnerships, 7) Financing for community health teams (referred to as ‘village health teams’ in the statement list), 8) Outer context resource allocation/policy into action, 9) Workforce characteristics to enhance during implementation, 10) Workforce implementation strategies, 11) Cross-level workforce strategies, and 12) Training and Education. Proximity of mapped clusters, and bridging values (i.e., the degree to which statements within one cluster could “bridge” or be similar to statements in nearby clusters) revealed some thematic similarities between the clusters. For example, strong bridging values were measured between clusters 4 (i.e., Sustainment considerations) and 7 (i.e., Financing for community health teams) (0.56–0.69), indicating strong conceptual linkage between the clusters. Statements in cluster 4 describe low reimbursement rates and staff turnover during implementation as factors affecting sustainment of non-specialist workforce training efforts. Neighboring cluster 7 describes the need to specifically finance community health worker teams for implementation success.

Fig. 2
figure 2

Cluster and point map of factors expected to influence training of Kenyan mental health non-specialist workforce. Note: Colored shapes and labels represent thematically distinct clusters. Numbered points in grey represent the 72 discrete statements that participants generated and sorted during the concept mapping process (numbered statements are listed in Appendix 1). Spatial distance between statements illustrates how frequently statements were sorted into the same pile as other statements

Full size image

Clusters 1–4, and 8 represent EPIS outer context determinants expected to impact the training efforts for the Kenyan mental health non-specialist workforce. Participants identified current workforce characteristics (cluster 1) including turnover rates, a lack of clear roles and responsibilities, and resistance to change as general concerns. Participants generated ideas for supporting training efforts across implementation phases. EPIS Exploration considerations (cluster 2) include clarifying Ministry of Health responsibilities and assessing current mental health service quality, and client treatment seeking behaviors. EPIS Preparation considerations (cluster 3) focus on generating awareness and support for SMART DAPPER and considering potential barriers to scaling up the workforce. EPIS Sustainment considerations (cluster 4) describe ongoing factors likely to influence the size and quality of the non-specialist workforce in public sector mental healthcare such as insufficient service reimbursement rates. Clusters 5, 9, and 12 address inner context considerations. Cluster 5 statements focus on inner context implementation processes and tools including timely communication, effective supervision for non-specialist providers, patient follow-up and navigation services. Cluster 9 describes specific workforce characteristics to enhance during implementation (i.e., outcomes of implementation strategies), while cluster 12 proposes workforce training and education activities.

Themes from clusters 6, 7, 10, 11 represent potential EPIS bridging factors to align outer and inner context entities in a common goal of scaling up and sustaining the non-specialist mental health workforce. Cluster 6 focuses on how community health worker teams (inner context) and Ministry of Health (outer context) entities can support local capacity and partnerships for implementation success. Cluster 7 highlights the need for dedicated financing for community health worker teams. While clusters 1, 9, and 12 described workforce contextual determinants likely to impact training efforts, clusters 10 and 11 describe potential strategies for non-specialist workforce training success. Cluster 10 describes implementation strategies related to capacity building across levels of the health system and continuous medical education. Cluster 11 describes ways to integrate non-specialists into existing systems including the development of a training curriculum and certification process.

Table 2 displays all statements, grouped by cluster labels, and with individual importance and changeability ratings, and Go-Zone quadrant positions. There was a strong correlation (r = 0.77) between importance and changeability ratings indicating that most statements were mapped in Go-Zone quadrant 1 (high importance and changeability; Fig. 3, Supplemental Material). Similarly, the Pearson product-moment correlation (r = 0.93) measured a high level of predictable alignment between average cluster ratings of importance and changeability ratings. Thirty-five statements (49.3%) were mapped in the Go-Zone for their high importance and changeability ratings. Average cluster importance and changeable ratings revealed that IRT members perceived outer context resource allocation (cluster 8) to be the most important (4.46) and changeable (4.37) area of focus.

Table 2 Summary of 71 factors expected to influence training of Kenyan mental health non-specialist workforce, organized by cluster with mean importance and changeability ratings and Go-Zone quadrant
Full size table
Fig. 3
figure 3

Go-Zone map. The Go-Zone map shows the mean participant rating for each statement brainstormed and rated in the concept mapping study. The X-axis represents ratings of how important each item is, and the Y-axis represents ratings of how changeable each item is as perceived by concept mapping participants. The colors of each point illustrate which cluster the statements belong to (see Fig. 2)

Full size image

Discussion

This study used concept mapping activities to address a persistent knowledge gap about how to effectively plan for and engage multi-sector stakeholders in efforts to scale-up the non-specialist mental health workforce.8 Concept mapping activities represented a rapid, virtual and community engaged approach to leverage the combined knowledge and expertise of stakeholders from policy, healthcare practice, research, and mental health advocacy perspectives. Results specifically revealed factors that facilitate or hinder (i.e., determinants) a collaborative effort between researchers and the Kenya Ministry of Health to train, employ, and sustain the mental health non-specialist workforce’s role in delivering public sector mental health services for depression and PTSD.

The main determinants and mechanisms identified in the concept mapping results represent key domains in the EPIS framework (i.e., outer context, inner context, bridging factors). Outer context (e.g., Ministry of Health and country-wide efforts) and inner context (e.g., local healthcare delivery settings) determinants highlighted different aspects of how workforce characteristics, turnover rates, current skill level and training and education needs are important targets for implementation strategies. Participants also emphasized the need for bridging factor type implementation strategies and supports that can align outer and inner contexts32 including formal partnerships between the Ministry of Health and local community health worker teams to distribute necessary financial resources (e.g., government finances to pay the local workforce) and collaboratively refine and standardize the mental health training curriculum. Such implementation strategies may be realized as the Kenyan government recently announced plans to hire 103,000 community health workers that were expected to be trained by the United States Agency for International Development [35]. Concurrent workforce enhancement efforts should coordinate implementation strategies and lessons learned to maximize impact. Future research is needed to independently evaluate government sponsored workforce initiatives.

The outer context emphasis on enhancing government’s formal participation in workforce scale-up efforts through greater resource allocation (e.g., increased availability of medicines, equipment) and policy into action efforts (e.g., leadership engagement throughout planning activities, maintaining a national commitment to improving healthcare, designing policies with sustainment in mind) was identified as the most necessary cluster of implementation strategies to promote widespread scale up of the non-specialists workforce for depression and PTSD service delivery. Prior systematic reviews highlight the importance of obtaining formal engagement from government entities to enhance acceptability and credibility of a non-specialist workforce [36, 37], but have not clarified the specific types of supports a Ministry of Health or other entity should offer. By including policymaker perspectives in the concept mapping exercise, the current study offers insights about what specific outer context government agency supports are needed and realistically available. Importantly, this project included policymaker perspectives while retaining the insights of nonspecialist providers, specialists, and individuals with lived experience. Including a diverse perspectives is critical to ensuring the feasibility and appropriateness of factors and implementation strategies identified in the concept map.

Participants rated all of the statements for their perceived importance and changeability which helps stakeholders to prioritize which cross-context implementation strategies will be needed over time to achieve implementation goals. Viewed through the lens of our guiding EPIS framework [31], a determinants’ importance and potential for changeability informs whether to pursue that factor during Exploration (e.g., identifying determinants and mechanisms related to potential evidence-based practice needs; cluster 2), Preparation (e.g., matching implementation strategies to determinants; cluster 3), Implementation (i.e., testing the implementation strategies and evaluating implementation outcomes; clusters 1, 5–12), and Sustainment (cluster 4). Successful planning for Sustainment is critical to institutionalizing evidence-based interventions that yield continued positive impacts on provider behaviors, patient engagement, patient outcomes (inner context), and ideally population health, policies and funding to support widespread access to evidenced-based depression and PTSD care (i.e., outer context).

Notably, Exploration, Preparation, and Sustainment Consideration focused clusters had lower importance and changeability ratings compared to clusters that focused on Implementation phase issues. Sustainment Considerations was ranked as the least important and least changeable cluster. This is consistent with prior research noting that sustainment strategies are undervalued during active implementation [38,39,40]. However, planning for sustainment from the beginning can reduce time and financial waste while increasing the public health impact of an implementation effort [41]. Ongoing workforce enhancement efforts will need to identify ways to address workforce reimbursement rates, turnover, and other sustainment challenges identified by participants. For example, compensation can include clear salary scales for non-specialist workers and compensation for travel and participation in workforce training activities [36, 42]. Compensating supervisors of non-specialists may also be an important consideration [42]. Additionally, worker turnover can be significantly reduced when providers perceive the care being delivered as meeting the needs of patients (i.e., strong when there is a strong fit between providers’ values and the service model), and when providers are supported with ongoing training and coaching implementation strategies [43].

Finally, concept mapping activities revealed several existing workforce characteristics that will need to be addressed by Implementation phase activities to scale up the mental health non-specialist workforce to deliver depression and PTSD services. For example, participants highlighted lack of clarity regarding non-specialist roles and responsibilities as a highly important and changeable factor for successful scale up. Poor role clarity is common amongst non-specialist workers [44,45,46], and can promote competition amongst peers and burnout.47 Supervisor mentoring and training programs tailored to non-specialist provider responsibilities can promote a clear mission and job expectations and increase job satisfaction [47, 48]. In recognition of these challenges, participants described the need for workforce implementation strategies including training supervisors to oversee non-specialists, involving educational institutions in training efforts, developing modular training manuals and certification programs for non-specialist workers, and developing public–private partnerships to build-up workforce capacity. Naslund and colleagues (2019) suggest that digital technologies can be tailored and used to support workforce development for non-specialist mental health workforce [49]. Digital platforms can support provider training, diagnostic tools, treatment guidance, and clinical or practice supervision. However, there is some evidence that combined digital and in-person training can have positive synergistic effects [50]. Additionally, future research is needed to investigate the optimal pedagogical training approaches required to support the non-specialist workforce, such as competency-based versus traditional knowledge acquisition approaches.

Limitations

Findings should be interpreted in consideration of limitations. First, the concept mapping activity was introduced and the focal statement and initial brainstorming were generated during an IRT video call. Subsequent concept mapping activities were conducted asynchronously using an online platform and resulted in reduced participation. Several outreach attempts were made to solicit IRT member participation, but the study team learned that IRT members were fatigued by the platform and activities. It is possible that additional important and potentially changeable barriers and facilitators to non-specialist workforce training may have been identified if concept mapping activities continued in virtual or in-person meetings with greater participation. However, additional meetings would also likely have increased participant response burden. Second, almost 50% of statements were mapped to the Go-Zone, which could indicate that participants differed in their understanding of the rating process. All concept mapping instructions were reviewed by multiple team members working in the US and Kenya to ensure ease of interpretation, but language and technology barriers could have influenced participant behavior.

Conclusion

The present study illustrates that training, development, and sustaining a non-specialist workforce for mental health care can’t be done in isolation, but rather will require the collaboration and engagement of policymakers, funders, mental health experts, the best research evidence, and the will to improve the lives of those living with depression and PTSD. In this study concept mapping activities provided a community-engaged approach for investigating how to impactfully grow and sustain the non-specialist mental health workforce for depression and PTSD service delivery. Participants rated many of the outer and inner context determinants and needed implementation strategies as both important and changeable, but structured concept mapping activities revealed that government action and resource allocation was perceived as the most critical and changeable approach to developing a sustainable mental health workforce. Integrating concept mapping with the EPIS framework aided in organizing results in a way that also advances implementation science. Combining this widely used framework with the mixed-method concept mapping approach helped reveal which factors and implementation strategies were perceived as optimal to address over implementation phases and which key stakeholders should be included in implementation efforts.

Data availability

De-identified concept mapping data may be available from the corresponding author on reasonable request.

Abbreviations

EPIS:

Exploration, Preparation, Implementation, Sustainment framework

IRT:

Implementation resource team

LMICs:

Low- and middle-income countries

PTSD:

Post-traumatic stress disorder

SMART:

Sequential, multiple, assignment randomized trial

SMART-DAPPER:

Sequential, multiple, assignment randomized trial of two evidence-based treatments for depression and PTSD (the SMART-DAPPER study)

References

  1. GBD 2021 Diseases and Injuries Collaborators. Global incidence, prevalence, years lived with disability (YLDs), disability-adjusted life-years (DALYs), and healthy life expectancy (HALE) for 371 diseases and injuries in 204 countries and territories and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021. The Lancet. 2024;403(10440):2133–2161. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/S0140-6736(24)00757-8.

  2. World Health Organization. World mental health report: transforming mental health for all. Geneva: World Health Organization. 2022. CC BY-NC-SA 3.0 IGO.

  3. Mathers CD, Loncar D. Projections of global mortality and burden of disease from 2002 to 2030. PLoS Med. 2006;3(11): e442. https://doiorg.publicaciones.saludcastillayleon.es/10.1371/journal.pmed.0030442.

    Article  PubMed  PubMed Central  Google Scholar 

  4. Rathod S, Pinninti N, Irfan M, Gorczynski P, Rathod P, Gega L, Naeem F. Mental health service provision in low- and middle-income countries. Health Serv Insights. 2017;10:1178632917694350. https://doiorg.publicaciones.saludcastillayleon.es/10.1177/1178632917694350.

    Article  PubMed  PubMed Central  Google Scholar 

  5. Kakuma R, Minas H, van Ginneken N, Dal Poz MR, Desiraju K, Morris JE, Saxena S, Scheffler RM. Human resources for mental health care: current situation and strategies for action. The Lancet. 2011;378(9803):1654–63. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/S0140-6736(11)61093-3.

    Article  Google Scholar 

  6. Jenkins R, Njenga F, Okonji M, Kigamwa P, Baraza M, Ayuyo J, Singleton N, McManus S, Kiima D. Prevalence of common mental disorders in a rural district of kenya, and socio-demographic risk factors. Int J Environ Res Public Health. 2012;9(5):1810–9. https://doiorg.publicaciones.saludcastillayleon.es/10.3390/ijerph9051810.

    Article  PubMed  PubMed Central  Google Scholar 

  7. Jenkins R, Othieno C, Omollo R, Ongeri L, Sifuna P, Kingora Mboroki J, Kiima D, Ogutu B. Probable Post Traumatic Stress Disorder in Kenya and Its Associated Risk Factors: A Cross-Sectional Household Survey. Int J Environ Res Public Health. 2015;12(10):13494–509. https://doiorg.publicaciones.saludcastillayleon.es/10.3390/ijerph121013494.

    Article  PubMed  PubMed Central  Google Scholar 

  8. Levy R, Mathai M, Chatterjee P, Ongeri L, Njuguna S, Onyango D, Akena D, Rota G, Otieno A, Neylan TC, Lukwata H, Kahn JG, Cohen CR, Bukusi D, Aarons GA, Burger R, Blum K, Nahum-Shani I, McCulloch CE, Meffert SM. Implementation research for public sector mental health care scale-up (SMART-DAPPER): a sequential multiple, assignment randomized trial (SMART) of non-specialist-delivered psychotherapy and/or medication for major depressive disorder and posttraumatic stress disorder (DAPPER) integrated with outpatient care clinics at a county hospital in Kenya. BMC Psychiatry. 2019;19(1):424. https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12888-019-2395-x.

    Article  PubMed  PubMed Central  Google Scholar 

  9. Kwobah E, Epstein S, Mwangi A, Litzelman D, Atwoli L. Prevalence of psychiatric morbidity in a community sample in Western Kenya. BMC Psychiatry. 2017;17(1):30. https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12888-017-1202-9.

    Article  PubMed  PubMed Central  Google Scholar 

  10. Ministry of Health. Kenya mental health action plan 2021 - 2025. Nairobi: Ministry of Health; 2021. https://mental.health.go.ke/download/kenya-mental-health-action-plan-2021-2025/.

  11. van Ginneken N, Tharyan P, Lewin S, Roa GN, Meera SM, Pian J, Chandrashekar S, Patel V. Non‐specialist health worker interventions for the care of mental, neurological and substance‐abuse disorders in low‐ and middle‐income countries. Cochrane Database of Systematic Reviews. 2023;11:CD009149. https://doiorg.publicaciones.saludcastillayleon.es/10.1002/14651858.CD009149.pub2.

  12. Barnett ML, Puffer ES, Ng LC, Jaguga F. Effective training practices for non-specialist providers to promote high-quality mental health intervention delivery: A narrative review with four case studies from Kenya, Ethiopia, and the United States. Camb Prisms Glob Ment Health. 2023;10: e26. https://doiorg.publicaciones.saludcastillayleon.es/10.1017/gmh.2023.19.

    Article  Google Scholar 

  13. van Ginneken N, Chin WY, Lim YC, Ussif A, Singh R, Shahmalak U, Purgato M, Rojas-García A, Uphoff E, McMullen S, Foss HS, Thapa Pachya A, Rashidian L, Borghesani A, Henschke N, Chong L-Y, Lewin S. Primary‐level worker interventions for the care of people living with mental disorders and distress in low‐ and middle‐income countries. Cochrane Database of Systematic Reviews 2021; 8:CD009149. https://doiorg.publicaciones.saludcastillayleon.es/10.1002/14651858.CD009149.pub3.

  14. Connolly SM, Vanchu-Orosco M, Warner J, Seidi Pa, Edwards J, Boath E, Irgens AC. Mental health interventions by lay counsellors: a systematic review and meta-analysis. Bull World Health Organ. 2021;99(8):572–582. https://doiorg.publicaciones.saludcastillayleon.es/10.2471/BLT.20.269050.

  15. Nakimuli-Mpungu E, Musisi S, Wamala K, Okello J, Ndyanabangi S, Birungi J, Nanfuka M, Etukoit M, Mayora C, Ssengooba F, Mojtabai R, Nachega JB, Harari O, Mills EJ. Effectiveness and cost-effectiveness of group support psychotherapy delivered by trained lay health workers for depression treatment among people with HIV in Uganda: a cluster-randomised trial. Lancet Glob Health. 2020;8(3):e387–98. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/S2214-109X(19)30548-0.

    Article  PubMed  Google Scholar 

  16. Wagner GJ, Ngo V, Goutam P, Glick P, Musisi S, Akena D. A structured protocol model of depression care versus clinical acumen: a cluster randomized trial of the effects on depression screening, diagnostic evaluation, and treatment uptake in Ugandan HIV clinics. PLoS ONE. 2016;11(5): e0153132. https://doiorg.publicaciones.saludcastillayleon.es/10.1371/journal.pone.0153132.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Wagner GJ, Gwokyalya V, Faherty L, Akena D, Nakigudde J, Ngo V, McBain R, Ghosh-Dastidar B, Beyeza-Kashesya J, Nakku J, Kyohangirwe L, Nabitaka LK, Lukwata H, Mukasa B, Wanyenze RK. Effects of M-DEPTH model of depression care on maternal HIV viral suppression and adherence to the PMTCT care continuum among HIV-infected pregnant women in Uganda: results from a cluster randomized controlled trial at pregnancy completion. AIDS Behav. 2023;27(9):2902–14. https://doiorg.publicaciones.saludcastillayleon.es/10.1007/s10461-023-04014-2.

    Article  PubMed  PubMed Central  Google Scholar 

  18. Jordans MJD, Kohrt BA. Scaling up mental health care and psychosocial support in low-resource settings: a roadmap to impact. Epidemiol Psychiatr Sci. 2020;29: e189. https://doiorg.publicaciones.saludcastillayleon.es/10.1017/S2045796020001018.

    Article  PubMed  PubMed Central  Google Scholar 

  19. Davies T, Lund C. Integrating mental health care into primary care systems in low- and middle-income countries: lessons from PRIME and AFFIRM. Glob Ment Health Camb Engl. 2017;4: e7. https://doiorg.publicaciones.saludcastillayleon.es/10.1017/gmh.2017.3.

    Article  CAS  Google Scholar 

  20. Petersen I, van Rensburg A, Kigozi F, Semrau M, Hanlon C, Abdulmalik J, Kola L, Fekadu A, Gureje O, Gurung D, Jordans M, Mntambo N, Mugisha J, Muke S, Petrus R, Shidhaye R, Ssebunnya J, Tekola B, Upadhaya N, Patel V, Lund C, Thornicroft G. Scaling up integrated primary mental health in six low- and middle-income countries: obstacles, synergies and implications for systems reform. BJ Psych Open. 2019;5(5):e69. https://doiorg.publicaciones.saludcastillayleon.es/10.1192/bjo.2019.7.

    Article  Google Scholar 

  21. Adams JL, Gaynes BN, McGuinness T, Modi R, Willig J, Pence BW. Treating depression within the HIV “medical home”: a guided algorithm for antidepressant management by HIV clinicians. AIDS Patient Care STDS. 2012;26(11):647–54. https://doiorg.publicaciones.saludcastillayleon.es/10.1089/apc.2012.0113.

    Article  PubMed  PubMed Central  Google Scholar 

  22. Ngo VK, Wagner GJ, Nakasujja N, Dickens A, Aunon F, Musisi S. Effectiveness of antidepressants and predictors of treatment response for depressed HIV patients in Uganda. International Journal of STD & AIDS. 2014;26(14). https://doiorg.publicaciones.saludcastillayleon.es/10.1177/0956462414564606.

  23. Green AE, Fettes DL, Aarons GA. A concept mapping approach to guide and understand dissemination and implementation. J Behav Health Serv Res. 2012;39(4):362–73. https://doiorg.publicaciones.saludcastillayleon.es/10.1007/s11414-012-9291-1.

    Article  PubMed  Google Scholar 

  24. Hurlburt M, Aarons GA, Fettes D, Willging C, Gunderson L, Chaffin MJ. Interagency collaborative team model for capacity building to scale-up evidence-based practice. Child Youth Serv Rev. 2014;39:160–8. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.childyouth.2013.10.005.

    Article  PubMed  PubMed Central  Google Scholar 

  25. Aarons GA, Green AE, Palinkas LA, Self-Brown S, Whitaker DJ, Lutzker JR, Silovsky JF, Hecht DB, Chaffin MJ. Dynamic adaptation process to implement an evidence-based child maltreatment intervention. Implement Sci. 2012;7:32. https://doiorg.publicaciones.saludcastillayleon.es/10.1186/1748-5908-7-32.

    Article  PubMed  PubMed Central  Google Scholar 

  26. Trochim WMK. Concept mapping: soft science or hard art? Eval Program Plann. 1989;12(1):87–110. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/0149-7189(89)90027-X.

    Article  Google Scholar 

  27. Kane M, Trochim WMK. Concept mapping for planning and evaluation. J Mix Methods Res. 2009;3(1):87–9. https://doiorg.publicaciones.saludcastillayleon.es/10.1177/1558689808326121.

    Article  Google Scholar 

  28. Trochim W, Kane M. Concept mapping: an introduction to structured conceptualization in health care. Int J Qual Health Care J Int Soc Qual Health Care. 2005;17(3):187–91. https://doiorg.publicaciones.saludcastillayleon.es/10.1093/intqhc/mzi038.

    Article  Google Scholar 

  29. Palinkas LA, Horwitz SM, Green CA, Wisdom JP, Duan N, Hoagwood K. Purposeful sampling for qualitative data collection and analysis in mixed method implementation research. Adm Policy Ment Health. 2015;42(5):533–44. https://doiorg.publicaciones.saludcastillayleon.es/10.1007/s10488-013-0528-y.

    Article  PubMed  PubMed Central  Google Scholar 

  30. Aarons GA, Hurlburt M, Horwitz SM. Advancing a conceptual model of evidence-based practice implementation in public service sectors. Adm Policy Ment Health. 2011;38(1):4–23. https://doiorg.publicaciones.saludcastillayleon.es/10.1007/s10488-010-0327-7.

    Article  PubMed  Google Scholar 

  31. Moullin JC, Dickson KS, Stadnick NA, Rabin B, Aarons GA. Systematic review of the Exploration, Preparation, Implementation, Sustainment (EPIS) framework. Implement Sci. 2019;14(1):1. https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s13012-018-0842-6.

    Article  PubMed  PubMed Central  Google Scholar 

  32. Lengnick-Hall R, Stadnick NA, Dickson KS, Moullin JC, Aarons GA. Forms and functions of bridging factors: specifying the dynamic links between outer and inner contexts during implementation and sustainment. Implement Sci. 2021;16(1):34. https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s13012-021-01099-y.

    Article  PubMed  PubMed Central  Google Scholar 

  33. Crable EL, Lengnick-Hall R, Stadnick NA, Moullin JC, Aarons GA. Where is “policy” in dissemination and implementation science? Recommendations to advance theories, models, and frameworks: EPIS as a case example. Implement Sci. 2022;17(1):80. https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s13012-022-01256-x.

    Article  PubMed  PubMed Central  Google Scholar 

  34. The Concept System® groupwisdomTM. Build 2022.30.10 [Web-based Platform]. 2022. Ithaca, NY. Available from https://www.groupwisdom.tech.

  35. The Official Website of the President of the Republic of Kenya. Government to hire community health workers in new approach [Internet]. Republic of Kenya; 2023 June 23. Available from: https://www.president.go.ke/government-to-hire-community-health-workers-in-new-approach/.

  36. Bunn M, Gonzalez N, Falek I, Weine S, Acri M. Supporting and sustaining nonspecialists to deliver mental health interventions in low- and middle-income countries: an umbrella review. Interv J Ment Health Psychosoc Support Confl Affect Areas. 2021;19(2):155. https://doiorg.publicaciones.saludcastillayleon.es/10.4103/INTV.INTV_47_20.

    Article  Google Scholar 

  37. Huang W, Long H, Li J, Tao S, Zheng P, Tang S, Abdullah AS. Delivery of public health services by community health workers (CHWs) in primary health care settings in China: a systematic review (1996–2016). Glob Health Res Policy. 2018;3(1):18. https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s41256-018-0072-0.

    Article  PubMed  PubMed Central  Google Scholar 

  38. Berta WB, Wagg A, Cranley L, Doupe MB, Ginsburg L, Hoben M, MacEachern L, Chamberlain S, Clement F, Esterbrook A, Keefe JM, Knopp-Sihota J, Rappon T, Reid C, Song Y, Estabrooks CA. Sustainment, Sustainability, and Spread Study (SSaSSy): protocol for a study of factors that contribute to the sustainment, sustainability, and spread of practice changes introduced through an evidence-based quality-improvement intervention in Canadian nursing homes. Implement Sci. 2019;14(1):109. https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s13012-019-0959-2.

    Article  PubMed  PubMed Central  Google Scholar 

  39. Shelton RC, Cooper BR, Stirman SW. The sustainability of evidence-based interventions and practices in public health and health care. Annu Rev Public Health. 2018;39:55–76. https://doiorg.publicaciones.saludcastillayleon.es/10.1146/annurev-publhealth-040617-014731.

    Article  PubMed  Google Scholar 

  40. Chambers DA, Glasgow RE, Stange KC. The dynamic sustainability framework: addressing the paradox of sustainment amid ongoing change. Implement Sci. 2013;8(1):117. https://doiorg.publicaciones.saludcastillayleon.es/10.1186/1748-5908-8-117.

    Article  PubMed  PubMed Central  Google Scholar 

  41. Aarons GA, Green AE, Willging CE, Ehrhart Mg, Roesch SC, Hecht DB, Chaffin MJ. Mixed-method study of a conceptual model of evidence-based intervention sustainment across multiple public-sector service settings. Implement Sci. 2014;9(1):183. https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s13012-014-0183-z.

  42. Mendenhall E, De Silva MJ, Hanlon C, Petersen I, Shidhaye R, Jordans M, Luitel N, Ssebunnya J, Fekadu A, Patel V, Tomlinson M, Lund C. Acceptability and feasibility of using non-specialist health workers to deliver mental health care: stakeholder perceptions from the PRIME district sites in Ethiopia, India, Nepal, South Africa, and Uganda. Soc Sci Med. 2014;118:33–42. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.socscimed.2014.07.057.

    Article  PubMed  PubMed Central  Google Scholar 

  43. Aarons GA, Sommerfeld DH, Hecht DB, Silovsky JF, Chaffin MJ. The impact of evidence-based practice implementation and fidelity monitoring on staff turnover: evidence for a protective effect. J Consult Clin Psychol. 2009;77(2):270–80. https://doiorg.publicaciones.saludcastillayleon.es/10.1037/a0013223.

    Article  PubMed  PubMed Central  Google Scholar 

  44. Adams WE, Duquette R, de Wet A, Rogers ES. Competing allegiance in an unclear role: peer and non-peer understanding of peer support in Massachusetts. United States SSM - Ment Health. 2023;4: 100245. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.ssmmh.2023.100245.

    Article  PubMed  Google Scholar 

  45. Collins R, Firth L, Shakespeare T. “Very much evolving”: a qualitative study of the views of psychiatrists about peer support workers. J Ment Health. 2016;25(3):278–83. https://doiorg.publicaciones.saludcastillayleon.es/10.3109/09638237.2016.1167858.

    Article  PubMed  Google Scholar 

  46. Moran GS, Russinova Z, Gidugu V, Gagne C. Challenges experienced by paid peer providers in mental health recovery: a qualitative study. Community Ment Health J. 2013;49(3):281–91. https://doiorg.publicaciones.saludcastillayleon.es/10.1007/s10597-012-9541-y.

    Article  PubMed  Google Scholar 

  47. Abraham KM, Erickson PS, Sata MJ, Lewis SB. Job satisfaction and burnout among peer support specialists: the contributions of supervisory mentorship, recovery-oriented workplaces, and role clarity. Adv Ment Health. 2022;20(1):38–50. https://doiorg.publicaciones.saludcastillayleon.es/10.1080/18387357.2021.1977667.

    Article  Google Scholar 

  48. Dishop CR, Green AE, Torres E, Aarons GA. Predicting turnover: the moderating effect of functional climates on emotional exhaustion and work attitudes. Community Ment Health J. 2019;55(5):733–41. https://doiorg.publicaciones.saludcastillayleon.es/10.1007/s10597-019-00407-7.

    Article  PubMed  PubMed Central  Google Scholar 

  49. Naslund JA, Shidhaye R, Patel V. Digital technology for building capacity of nonspecialist health workers for task sharing and scaling up mental health care globally. Harv Rev Psychiatry. 2019;27(3):181–92. https://doiorg.publicaciones.saludcastillayleon.es/10.1097/HRP.0000000000000217.

    Article  PubMed  PubMed Central  Google Scholar 

  50. Muke SS, Tugnawat D, Joshi U, Anand A, Khan A, Shrivastava R, Singh A, Restivo JL, Bhan A, Patel V, Naslund JA. Digital training for non-specialist health workers to deliver a brief psychological treatment for depression in primary care in India: findings from a randomized pilot study. Int J Environ Res Public Health. 2020;17(17):6368. https://doiorg.publicaciones.saludcastillayleon.es/10.3390/ijerph17176368.

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

The authors would like to thank the members of the Implementation Resource Team, without whom this concept mapping study would not have been possible.

Funding

This study was funded by grants from the National Institute of Health, National Institute of Mental Health (R01MH115512, R01MH113722). Dr. Crable is also supported by a grant from the National Institute of Health, National Institute on Drug Abuse (K01DA056838).

Author information

Authors and Affiliations

  1. Department of Psychiatry, University of California San Diego, La Jolla, CA, USA

    Erika L. Crable, Ryan G. Kenneally & Gregory A. Aarons

  2. Herbert Wertheim School of Public Health and Human Longevity Science, University of California San Diego, La Jolla, CA, USA

    Erika L. Crable

  3. ACTRI Dissemination and Implementation Science Center, University of California San Diego, La Jolla, CA, USA

    Erika L. Crable & Gregory A. Aarons

  4. Child and Adolescent Services Research Center, San Diego, CA, USA

    Erika L. Crable, Ryan G. Kenneally & Gregory A. Aarons

  5. Department of Psychiatry, University of California San Francisco, San Francisco, CA, USA

    Susan M. Meffert

  6. Centre for Clinical Research, Kenya Medical Research Institute (KEMRI), Nairobi, Kenya

    Linnet Ongeri

  7. Department of Psychiatry, University of Nairobi, Nairobi, Kenya

    David Bukusi

  8. Department of Obstetrics, Gynecology and Reproductive Sciences, University of California San Francisco, San Francisco, CA, USA

    Rachel L. Burger

  9. University of Nairobi, Nairobi, Kenya

    Grace Rota, Ammon Otieno, Raymond Rotai & Muthoni Mathai

Authors
  1. Erika L. Crable
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Susan M. Meffert
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Ryan G. Kenneally
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Linnet Ongeri
    View author publications

    You can also search for this author inPubMed Google Scholar

  5. David Bukusi
    View author publications

    You can also search for this author inPubMed Google Scholar

  6. Rachel L. Burger
    View author publications

    You can also search for this author inPubMed Google Scholar

  7. Grace Rota
    View author publications

    You can also search for this author inPubMed Google Scholar

  8. Ammon Otieno
    View author publications

    You can also search for this author inPubMed Google Scholar

  9. Raymond Rotai
    View author publications

    You can also search for this author inPubMed Google Scholar

  10. Muthoni Mathai
    View author publications

    You can also search for this author inPubMed Google Scholar

  11. Gregory A. Aarons
    View author publications

    You can also search for this author inPubMed Google Scholar

Contributions

SM and MM designed the parent study. GAA, SM, and MM conceptualized the concept mapping study. GAA and ELC developed concept mapping questions and analytic plan with feedback from SM, MM, LO, and RB. SM, LO, DB, RB, GR, AO, RR, and MM supported recruitment efforts. ELC and GAA conducted the analyses. RGK conducted relevant literature reviews. ELC drafted and revised the manuscript based on feedback from all coauthors. All authors reviewed and approved the final manuscript.

Corresponding author

Correspondence to Erika L. Crable.

Ethics declarations

Ethics approval and consent to participate

Study procedures were reviewed and approved by the University of California San Francisco Institutional Review Board. Consent to participate was obtained from all individuals who participated in interviews.

Consent for publication

Consent for publication was obtained from all individuals who participated in concept mapping activities.

Competing interests

GAA is a Co-Editor in Chief of Implementation Science and on the Editorial Board of Implementation Science Communications. All decisions on this paper were made by other editors. The other 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.

Supplementary Information

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

Crable, E.L., Meffert, S.M., Kenneally, R.G. et al. Multi-sector determinants of implementation and sustainment for non-specialist treatment of depression and post-traumatic stress disorder in Kenya: a concept mapping study. Implement Sci Commun 6, 55 (2025). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s43058-025-00744-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s43058-025-00744-7

Keywords

Implementation Science Communications

ISSN: 2662-2211