You are viewing the site in preview mode

Skip to main content

Advertisement

A Community Health Worker “logic model”: towards a theory of enhanced performance in low- and middle-income countries

Abstract

Background

There has been a resurgence of interest in national Community Health Worker (CHW) programs in low- and middle-income countries (LMICs). A lack of strong research evidence persists, however, about the most efficient and effective strategies to ensure optimal, sustained performance of CHWs at scale. To facilitate learning and research to address this knowledge gap, the authors developed a generic CHW logic model that proposes a theoretical causal pathway to improved performance. The logic model draws upon available research and expert knowledge on CHWs in LMICs.

Methods

Construction of the model entailed a multi-stage, inductive, two-year process. It began with the planning and implementation of a structured review of the existing research on community and health system support for enhanced CHW performance. It continued with a facilitated discussion of review findings with experts during a two-day consultation. The process culminated with the authors’ review of consultation-generated documentation, additional analysis, and production of multiple iterations of the model.

Results

The generic CHW logic model posits that optimal CHW performance is a function of high quality CHW programming, which is reinforced, sustained, and brought to scale by robust, high-performing health and community systems, both of which mobilize inputs and put in place processes needed to fully achieve performance objectives. Multiple contextual factors can influence CHW programming, system functioning, and CHW performance.

Conclusions

The model is a novel contribution to current thinking about CHWs. It places CHW performance at the center of the discussion about CHW programming, recognizes the strengths and limitations of discrete, targeted programs, and is comprehensive, reflecting the current state of both scientific and tacit knowledge about support for improving CHW performance. The model is also a practical tool that offers guidance for continuous learning about what works. Despite the model’s limitations and several challenges in translating the potential for learning into tangible learning, the CHW generic logic model provides a solid basis for exploring and testing a causal pathway to improved performance.

Background

Community Health Workers in low- and middle-income countries

The positive impact that Community Health Workers (CHWs) can have on people’s health and well-being in low- and middle-income countries (LMICs) is well-documented [14]. The final push toward achieving the Millennium Development Goals by 2015, current post-2015 discussions, and the introduction of Universal Health Coverage [5] have prompted many LMICs to increasingly invest in CHW programming in the hope of creating more accessible, equitable, and people-centered health systems [6]. A core challenge of CHW programming is how to ensure sustained, optimal performance at scale of this important cadre of the health workforce.

Available research evidence on the most efficient and effective strategies to ensure such performance, however, is weak [7]. Nevertheless, CHW programs continue to grow and expand, usually incorporating a variety of strategies to support performance with promising but uncertain effectiveness [8]. Consequently, more research is needed on how best to ensure optimal CHW performance at scale, particularly when LMIC governments are increasing domestic expenditures on health and donor support to the health sector is in a state of transition [9].

Although collective understanding of the definitive causal pathway to improved performance is limited, policy makers, program managers, practitioners, and the academic community can work together to develop theories about how to improve and sustain performance. The paper proposes such a theory, in the form of a generic CHW “logic model,” drawing upon available research and expert knowledge of CHWs in LMICs. Combining a somewhat patchwork collection of evidence (from studies in the published and gray literature) with the tacit knowledge of experts [10], and translating this knowledge into a practical tool for decision makers, is not unique to CHW performance, but is rather a recurrent challenge in on-going efforts to address the knowledge-practice gap in global public health [1113].

Logic models

Policy makers, program planners, project managers, and other analysts use logic models to communicate succinctly and visually the underlying theory of their policies and programs. Funnell and Rogers define a program theory as, “an explicit theory or model of how an intervention, such as a project, a program, a strategy, an initiative, or a policy contributes to a chain of intermediate results and finally to the intended or observed outcomes” [14]. A logic model maps the intended relationships and causal connections between what a program plans to do and what it hopes to achieve [15, 16]. A logic model commonly includes contextual factors that may positively or negatively influence a program’s implementation and the attainment of results [16].

Although the logic model traces its conceptual roots to program evaluation research [1719], today its reach is far broader. It can guide program design, implementation, monitoring, operational research, and evaluation. During the last two decades, interest in the application of causal models has grown among academics, governmental agencies, non-governmental organizations, and practitioners of evaluation, primarily in industrialized countries [17]. The inclusion of this kind of causal thinking in the early stages of policy and program development is now making in-roads in non-industrialized countries [20].

Overview

The purpose of this paper is to promote early and continuous causal thinking as decision makers design, implement, scale up, and evaluate CHW and other programs that are intended to positively affect the public’s health. The methods section describes how the generic CHW logic model was constructed, drawing explicitly on research in LMICs and the informed opinion of CHW experts with experience in these countries. The results section presents a graphic display of the model and detailed explanations of its component parts, in both narrative and tabular form. In the discussion section, the authors examine the value and unique contribution of the model and its potential as a tool to guide continuous learning about what works. They also present challenges of translating potential learning into tangible learning and describe some inherent limitations of the model. The paper concludes that, despite these challenges and limitations, the model offers the global health community greater clarity about how to think about, learn about, and ultimately support improved CHW performance.

Methods: the process of model construction

The generic CHW logic model evolved over a two-year period (April 2011 to April 2013) through a multi-stage, inductive process. The different stages of the process were as follows.

Initial planning/concept development

Model construction began during the initial planning of a structured review of the evidence on community and formal health system support for enhanced CHW performance, a US government-sponsored initiative led by the US Agency for International Developmenta. The organizers of the review adopted the following definition of a CHW: a health worker who receives standardized training outside the formal nursing or medical curricula to deliver a range of basic health, promotional, educational, and outreach services, and who has a defined role within the community system and larger health system. The first step in the planning process comprised formulating alternative definitions of CHW performance; identifying various factors with the potential to affect performance; and classifying common activities to support CHW performance by whether they were provided by health systems alone, by communities alone, or by both. To achieve these objectives in a short period of time, one of the authors [JN] performed a rapid qualitative content analysis of a small, purposive sample of recent (since 2010) key documents on CHWs [2, 3, 2123]. The same author [JN] derived the definitions, factors, and support activities directly and inductively from this sample alone. This review was accompanied by a cursory exploration of gaps in the literature on efforts to improve CHW performance. The product of this formative work was an initial working conceptual framework.

Evidence review

The organizers of the evidence review shared the working framework with approximately 50 CHW experts (representing academic institutions, bi-lateral and multi-lateral development agencies, and non-governmental organizations) who were invited to review the evidence on how best to support CHWs. The organizers subsequently assigned the experts to three evidence review teams and charged each with investigating a different question related to the three different sources of support for CHWs (community, health system, both). The preliminary conceptual framework was intended as a working template to guide the teams in their preparations for reviewing the literature and summarizing their findings. Each review team independently refined the template. The teams applied a variety of methods in doing so, including group discussion, literature review, analyses of CHW program case studies, and a modified theory of change exercise. Not all teams adopted all these methods, but most combined at least two of them. The teams incorporated visual displays of their respective frameworks into three draft evidence synthesis papersb.

Expert consultation

A two-day expert consultation, which included more than one hundred professionals from around the world, provided each of the three review teams with an opportunity to share the findings of their independent reviews, including their frameworks. Although the organizers of the event did not explicitly ask the experts to comment on the three different frameworks, the experts’ oral and written feedback to the review teams about the review findings provided additional insights into the relevance and utility of the original working framework and each of the modified versions. A series of presentations from representatives of African and Asian countries that addressed the challenges of implementing and sustaining CHW programs at scale provided additional insights. At the conclusion of the consultation, the review teams revised their frameworks and draft synthesis papers, as necessary.

Development of a synthesis model from the results of the evidence review, expert consultation, and supplementary analytical work

Once the review teams had submitted their final synthesis papers [2426], two of the authors [JN and DF] completed a thorough content analysis of each with the intention of consolidating the diversity of conceptual thinking and findings toward the development of a single, cohesive, representative draft generic logic model. The three synthesis papers did not adequately address, however, how community systems can influence CHW performance. Consequently, the same two authors [JN and DF] consulted several key papers from the literature on building community capacity [2729]. This cursory review produced a working outline on community systems, which all the authors examined for its relevance to CHW performance. The authors incorporated the information from this outline into a second draft of the generic logic model and continued to meet to refine the full model through discussion and further clustering and aggregating of information, which led to a series of additional iterations until the authors reached a consensus.

Ethical approval was not required as this study was based on reviewing published and unpublished literature and consulting with experts. A comprehensive description of all phases of the structured evidence review process, including its strengths and limitations, has been reported elsewhere [7].

Results: the generic CHW logic model

The generic CHW logic model (Figure 1) posits that optimal CHW performance (“Results”) is a function of high quality CHW programming (“Activities at the program level”), which is reinforced, sustained, and brought to scale by robust, high-performing health and community systems (“Activities at the system level”), both of which mobilize essential inputs and put in place processes needed to fully achieve the objectives of improved performance (“Inputs”). A range of contextual factors can also influence programming, system functioning, and performance. The component parts of the model are described briefly below, beginning with the intended results and working backward.

Figure 1
figure1

Community Health Worker generic logic model.

CHW performance (“Results”)

The model depicts CHW performance in relation to the specific roles and responsibilities of CHWs in a given context in three ways: outputs, outcomes, and impact (Table 1). Outputs are proximate measures of performance that occur at the level of the individual CHW. Some are indirect measures, such as cognitive/psycho-motor (e.g., knowledge and skills acquisition) or affective (e.g., self-efficacy/self-esteem, confidence, or personal satisfaction) CHW-level changes, while others are direct behavioral measures that occur at the interface of CHWs and clients, such as absenteeism, the quantity and quality of service delivery, responsiveness to clients, and productivity. Attrition and advancement are measures of CHW developmental changes over time. Outcomes are intermediate measures, defined as CHW-attributable changes that occur among individual clients (e.g., health care-seeking behavior or health-promoting behavior in the home), as well as effects on communities and health systems (e.g., changes in social cohesion or cost savings to the health system, respectively). Impact refers to more distal measures, defined as CHW-attributable changes in health (e.g., morbidity and mortality) at the population level.

Table 1 Measures and definitions of Community Health Worker performance in the generic logic model

CHW programming (“Activities at the program level”)

Quality CHW programming comprises a wide range of support activities that explicitly target CHWs, seek to enhance their performance, and are undertaken by a range of actors in the health sector and the community (Table 2). These support activities are subsumed under three rubrics: technical support, social support, and incentives. Although these rubrics are common to both health sector actors (e.g., health workers, district health managers, etc.) and community actors (e.g., village health committees, local religious leaders, etc.) engaged in programming, some actors tend to carry out some of these activities more frequently than others, while other activities seem to be undertaken more equally by both. Of the three rubrics, technical support functions are those mostly commonly shared by health sector and community actors and thus are listed together in Table 2. For social support and incentives, however, the differences are more discernible; consequently, they are listed separately for health sector and community actors in Table 2.

Table 2 The role of community and health sector actors in Community Health Worker (CHW) programming

Technical support includes efforts by health sector and community actors to design good CHW programs, ensure sound program implementation and management, monitor adequacy of effort, and evaluate effectiveness. Sound programming also takes into consideration the characteristics of both CHWs (e.g., age, sex, ethnicity, education, experience) and their clients (e.g., socio-economic status, cultural belief system, education, age, sex).

Social support includes various activities that health sector and community actors undertake with non-health and health sector representatives to enhance CHW programming. For example, these activities may include health sector and community actors fostering new partnerships with non-health sector structures and representatives such as political, administrative, and government officials, journalists, non-governmental organizations (NGOs), and social action community-based groups. Other activities are intended to strengthen the linkages with various groups that have a history of working on health promotion and disease prevention activities, such as faith-based organizations, NGOs, social and civic clubs, savings groups and loan associations, women’s groups, schools, health care delivery teams, district health offices, and health oversight bodies. Continuously promoting and developing the professional and personal networks of CHWs, such as CHW associations and peer groups, is another area of targeted social support. As Table 2 demonstrates, health sector and community actors often perform these social support activities in different ways.

Incentives encompass non-financial (e.g., gestures reflecting community appreciation of and trust in CHWs), in-kind (e.g., specific privileges, goods, and services), and financial inducements (e.g., fees for service, salary, stipend, or allowances/benefits) that are commonly used to motivate CHWs to enhance and sustain their performance. Although there is some overlap between incentives, particularly the non-financial ones, and social support, they are described separately to adequately capture the many dimensions and nuances of inducements as an important source of support for CHW performance. Again, community and health sector actors vary in their performance of these functions.

The model assumes that all these program-level activities, when adequately implemented in terms of both intensity and quality of effort, will contribute to improved CHW performance.

Systems support (“Activities at the system level”)

In contrast to the targeted nature of CHW programming, performance-enhancing activities at the level of health and community systems have multiple and synergistic direct and indirect effects across numerous health sector programs and providers (i.e., not limited to CHWs). Consequently, in contrast to the common rubrics of CHW program support activities, there is greater variation in how health system and community system support activities manifest themselves and influence CHW programming and ultimately CHW performance (Table 3).

Table 3 The role of community and health systems in reinforcing Community Health Worker programming

Robust-performing health systems can reinforce CHW programming, sustain results, and take efforts and effectiveness to scale through sound governance of the sector; timely and adequate sector financing; well-organized service delivery; ensuring a capable and well-deployed health workforce; the systematic collection, analysis, and use of information; and ensuring access to a broad range of medical products and commodities [30]. For their part, communities contribute to quality CHW programming through sound governance of community resources; ensuring social belonging and cohesion; and resource mobilization [27]. As noted previously, the model also assumes adequacy of effort at the system level: activities are timely, comprehensive, and of sufficient quantity and quality.

Inputs

For each of the many activities at the program and system levels in the model there is an input implication. For the sake of parsimony, the authors have included only the main rubrics in the model—written policies and programs, people, funding, organizations and facilities, material and equipment, and time (Figure 1). Again, the model assumes adequate levels and timely availability of these resources (quantity and quality) at program and system levels to achieve improvements in CHW performance.

Contextual factors

Many contextual factors can influence CHW programming, systems functioning, and CHW performance. Some of these factors are associated with the larger political economy of a country, whereas others relate to the characteristics of communities that CHWs serve. Some examples of political economy include the structure, rules, dynamics, and balance of power in society; the role and influence of interest groups in national decision making; the role of foreign aid in development; the degree of tolerance of corruption; the extent of transparency in governance; frequency of political elections, violence, and coercion; and the level of ethnic fragmentation in society. Examples of community characteristics are cultural values, geography (urban/rural), economic status (poverty level), social status (isolation, discrimination, gender norms), stability (nomadic, transitional, permanent), health belief system (preference for traditional medicines), and history of and experience with volunteerism.

Discussion

Value of the model

This multi-level model is a unique contribution to current thinking about CHWs and their performance for several reasons. It places performance and how to improve it at the center of the discussion about CHW programming, which is increasingly expected to make important contributions to the achievement of results-oriented Millennium Development Goals- and Universal Health Coverage-related initiatives at global and country levels. Additionally, it recognizes that the different contributions of both health sector and community actors to high quality CHW programming are necessary but not sufficient to attain and sustain CHW performance at scale. CHWs find themselves at the intersection of two overlapping and dynamic systems, both of which have a critical and mutually supportive role to play in enabling and reinforcing CHW programming. If today’s CHW programs are to overcome the weaknesses of CHW programs of the past [31], strong health and community systems will be needed to ensure the sustainability of health gains achieved through discrete targeted programs.

Furthermore, the model is integrative in that it draws on a broad range of the available literature, both published and unpublished, first-hand stakeholder experience, and the informed opinions and perspectives of experts. Finally, the authors adopted an innovative, pragmatic approach to logic model construction that combined certain elements of traditional approaches (single analyst and collaborative group process) [17] that were adapted to the unique circumstances under which this work was to be carried out. The approach was largely opportunistic, iterative, drew on a range of sources, and was embedded in a larger evidence synthesis exercise carried out in a compressed timeframe. The process began with convergent thinking toward the development of a preliminary working conceptual framework, subsequently evolved into divergent thinking as reflected in review teams’ multiple variations of the original framework, and ultimately returned to convergent thinking in pursuit of a single, comprehensive, framework. Although this convergence-divergence-convergence cycle was a time-intensive process, the benefit was a more robust identification and rendering of the many determinants of CHW performance.

Utility of the model

The generic CHW logic model not only represents a novel approach for how to think about improving CHW performance, but also offers a framework that decision-makers, including policymakers, planners, researchers, and communities, can use to learn about what does and does not work in practice. The generic CHW logic model can serve multiple, pragmatic purposes. First, it can be an aid to planning. Decision makers can consult and adapt the model when developing or modifying local CHW policies and programs. The value of a generic, comprehensive model is that it can draw decision-makers attention to certain elements of sound design that are sometimes overlooked. For example, this model highlights the important role of community systems, which is inadequately addressed by the published literature, yet repeatedly mentioned in the gray literature and by experts as a significant determinant of good CHW performance. Hopefully, the model will stimulate planners to take into account the contribution of community systems in optimizing programming and sustaining CHW performance.

Second, use of the model can contribute to consensus-building. The use of local program theories, which allow decision makers to describe, in explicit terms, how they expect their CHW programs to work, can facilitate communication among program developers, researchers, policy makers, community representatives, and funders. Improved communication can help foster a shared understanding of the depth and breadth of what is needed to improve and sustain CHW performance [16, 32, 33], and promote a common understanding among country staff and community representatives of their shared mission, a critical first step in developing a mutual accountability framework. This shared understanding can inform the current dialogue about the need to address fragmented stewardship of CHWs at both country and global levels [34, 35].

Third, as decision-makers adapt the model to local conditions and available resources, they can use it as a guide for improving program implementation. The assumed linkages and relationships represented in country-specific causal models can generate a series of questions that can be explored through routine monitoring, systematic documentation of implementation (preferably prospective), and other forms of systematic inquiry around implementation such as operational or action research. Observation and documentation of the actual intensity and adequacy of intended implementation – an explicit assumption of the generic model – as well as short- and medium-term intended and unintended effects of implementation, can improve management and contribute to continuous learning about why a CHW program does or does not work. The answers to these questions may result not only in improvements to programming, but also in modifications to the assumptions explicit and implicit in local and generic models.

Fourth, a local theory of the program adapted from the generic logic model, if used to guide summative evaluation, can contribute to collective learning about program effectiveness [18, 19]. Summative evaluation responds to senior policy makers’, funders’, and community leaders’ end-game concerns about whether the program worked, what it was about the program that led to success, and whether they should invest further to extend its reach or take it to scale. Documentation of the extent to which a local program theory has been implemented as intended, when incorporated into evaluation designs, can provide some support for attributing the observed outcomes to the program even in the absence of a counterfactual [17] – a common limitation in the literature on support strategies for CHW performance.

Future challenges

How can this potential for learning be translated into tangible learning? There are several practical challenges in moving forward. The most pressing is to find an opportunity to test the assumptions of the generic CHW logic model under real-life conditions. To that end, the authors have commissioned a retrospective study, using the logic model as a framework for analysis, of the factors associated with the success of a growth monitoring and promotion program implemented by female village volunteers in Honduras. The authors also have commissioned field research in a sub-Saharan African country to determine the content and face validity of the model to ascertain its relevance as a framework for identifying strengths and weaknesses of current efforts to improve CHW performance, as well as for identifying new opportunities for doing so. These activities should provide much-needed insights on the validity of the model and its generalizability to different settings.

A second challenge is determining the feasibility of local adaptations of the generic model in routine policy making and programming. Although the model in Figure 1 appears simple and straightforward, CHW policy makers and program managers may perceive the underlying number and variety of elements and relationships as depicted in the tables to be intimidating. An inter-country, continuous learning exchange, whereby CHW stakeholders periodically share experiences about selected causal links in their respective models at different points in time can provide ideas and support for innovation that may partially address this challenge. One benefit of this kind of south-to-south knowledge management initiative would be a more systematic and user-friendly gray literature derived from documentation of program implementation framed by explicit logic models, which could significantly contribute to the global community’s collective understanding of what works under different conditions. Furthermore, documentation of the costs and benefits of holding these adapted models accountable for real-world results can inform global efforts to ensure adequate stewardship of donor support to national efforts and in building national capacity.

A third challenge is ensuring the generic model is adapted to local conditions, and that local adaptations and the generic model are updated in response to changing realities. The factors influencing CHW performance are complex; any generic logic model will be an imperfect and oversimplified reflection of reality and at best a snapshot of that reality at a single point in time. For example, a generic model may not offer the precision needed to differentiate the performance determinants of volunteer CHWs working on HIV and AIDS from those of paid CHWs working on improving access to curative care. No single, generic CHW logic model will be equally relevant to all countries: the genesis, purpose, evolution, and complexity of CHW programs around the world can differ substantially from one country to another. Furthermore, the inputs and activities required to sustain a fully functioning program will vary in type, mix, intensity, and sequence across countries. Therefore, generic models must be adapted to local conditions and evolve as circumstances change. For instance, the use of mobile technology may alter our current understanding of what is needed to adequately support CHWs. Continuously building better generic and adapted models based on feedback from multiple information sources is both a challenge and an opportunity for understanding better what works in practice in different settings.

Limitations

The generic CHW logic model has several limitations. First, all the elements in the model appear equally weighted. At this time, there is no strong evidence base to support weighting, yet weighting will likely vary from context to context. The Honduras study should shed some light on the relative contributions of different support strategies to improved performance in this particular setting and with this particular cadre of CHWs. Further research of this kind, however, is urgently needed.

Second, the model should not be interpreted as normative guidance for how to improve CHW performance. It is a working theory in the absence of strong scientific evidence on the definitive causal pathway to improved performance. This does not mean that the different activities reviewed and included in the model do not work or do not merit support. It means that continuing implementation of promising activities expected to influence performance should be accompanied by prospective monitoring and documentation of the adequacy of effort expended, the influence of many factors, and any intended and unintended effects. More rigorous research should be conducted when and where possible.

Third, although use of the model can inform programming, by itself it is not an adequate program planning tool. A “logical framework” or “log frame” is a natural extension of the logic model: it magnifies the logic model by integrating indicators and targets and the means for measuring progress [36, 37]. The additive components of a log frame make it an indispensable complementary management tool for program planning, monitoring, and evaluation of specific country CHW programs. Likewise, the logic model is primarily descriptive, not explanatory. For the expected linkages it does propose, the logic model does not unpack the underlying assumptions in the causal results chain. The “theory of change” approach is better suited to this kind of explanatory work [38]. It provides a thorough analysis of ‘why’ program activities are expected to produce intended results and creates a more in-depth understanding of ‘how’ change can occur [39].

Finally, the logic model concept is based upon a general systems theory approach to management [40], which is consistent with the literature and expert opinion on CHW performance that suggest that support activities across and within health and community systems are not linear, but rather interdependent, and may interact to achieve the intended results [26]. The two-dimensional graphic representation of the CHW logic model (Figure 1) is not capable of capturing this dynamism adequately [38].

Conclusions

A robust evidence base for developing a definitive causal pathway to improved CHW performance does not yet exist. Although such a pathway may remain aspirational, the importance of CHW performance in today’s results-oriented environment should spur further development of such pathways. This generic CHW logic model partially addresses this knowledge gap by proposing a theoretical causal pathway to enhanced performance.

The model posits that robust, highly functioning health and community systems enable and reinforce CHW programming, which offers the prospects of sustaining CHW performance at scale beyond the life of discrete CHW-targeted programs and projects. By examining both formal health and community system support for CHWs in an integrated manner, the model highlights the multi-level and multi-dimensional challenges and complexity of enhancing CHW performance. The model is a novel contribution to current thinking about CHWs. It places CHW performance at the center of the discussion about CHW programming. It highlights the strengths and limits of targeted programming, and is comprehensive, reflecting the current state of both scientific and tacit knowledge about support for improving CHW performance. The authors adopted an innovative, opportunistic approach to model construction, combining specific elements of traditional approaches and embedding them in a larger evidence synthesis exercise.

The model also offers a comprehensive framework to stimulate continuous learning about what works. It is a cause for concern that CHW programs continue to proliferate, go to scale, and grow in the absence of routine monitoring information and strong research evidence on what support activities, or combination of activities, work best. It is critical to pay more attention to answering the question of how best to enhance CHW performance at scale and to guide investment decisions of governments, communities, and donors alike in this time of transition in development assistance for health. The CHW generic logic model can make an important contribution to this learning agenda despite several challenges in translating the potential for learning into actual learning, and some inherent limitations in the model. It suggests a way forward, yet leaves ample space for continuous modification, creativity, and innovation.

Endnotes

aAlthough it can be argued that communities are important actors in producing health, and therefore could be considered as part of the health system, the organizers of the evidence review chose to separate the community’s contribution to improving CHW performance precisely to highlight the important and often overlooked role communities play in this process.

bIn all, the three teams reviewed 147 documents and summarized the evidence in terms of what was known, what remained to be investigated, and what recommendations for action could be drawn. A complete bibliography is available from the authors upon request.

Authors’ information

JN is the Health Systems Research Advisor in the Office of Health Systems, Global Health Bureau, USAID, Washington, DC, on assignment from the U.S. Centers for Disease Control and Prevention (CDC), Atlanta, Georgia. DF is a Health Systems Strengthening Advisor in the Office of HIV/AIDS, United States Agency for International Development (USAID), Washington, DC. TW is a Senior Improvement Advisor for Health Workforce Development for the USAID Applying Sciences to Strengthen and Improve Systems Project (ASSIST) at University Research Co., LLC (URC), in Bethesda, Maryland. LMF is Vice-President for Technical Assistance and Evaluation for EnCompass LLC, in Rockville, Maryland. MN is Vice-President, Global Health and WASH, World Vision International, in Washington, DC.

Abbreviations

CHW:

Community Health Worker

LMICs:

Low- and middle-income countries.

References

  1. 1.

    Bhutta ZA, Das JK, Walker N, Rizvi A, Campbell H, Rudan I, Black RE, The Lancet Diarrhoea and Pneumonia Interventions Study Group: Interventions to address deaths from childhood pneumonia and diarrhea equitably: what works and at what cost?. Lancet. 2013, 381: 1417-1429. 10.1016/S0140-6736(13)60648-0.

  2. 2.

    Kane SS, Gerretsen B, Scherpbier R, Dal Poz M, Dieleman M: A realist synthesis of randomized control trials involving use of community health workers for delivering child health interventions in low- and middle-income countries. BMC Health Serv Res. 2010, 10: 286-10.1186/1472-6963-10-286.

  3. 3.

    Bhutta ZA, Lassi ZS, Pariyo G, Huicho L: Global Experience of Community Health Workers for Delivery of Health-Related Millennium Development Goals: A Systematic Review, Country Case Studies, and Recommendations for Integration into National Health Systems. 2010, Geneva: Global Health Workforce Alliance and WHO

  4. 4.

    Haines A, Sanders D, Lehmann U, Rowe AK, Lawn JE, Jan S, Walker DG, Bhutta Z: Achieving child survival goals: potential contribution of community health workers. Lancet. 2007, 369: 2121-2131. 10.1016/S0140-6736(07)60325-0.

  5. 5.

    World Health Organization: The World Health Report 2013: Research for Universal Health Coverage. 2013, Geneva: WHO

  6. 6.

    Frenk J, Chen L, Bhutta ZA, Cohen J, Crisp N, Evans T, Fineberg H, Garcia P, Ke Y, Kelley P, Kistnasamy B, Meleis A, Naylor D, Pablos-Mendez A, Reddy S, Scrimshaw S, Sepulveda J, Serwadda D, Zurayk H: Health professionals for a new century: transforming education to strengthen health systems in an interdependent world. Lancet. 2010, 376: 1923-1958. 10.1016/S0140-6736(10)61854-5.

  7. 7.

    Naimoli JF, Frymus DE, Quain EE, Roseman EL: Community and Formal Health System Support for Enhanced Community Health Worker Performance: A U.S. Government Evidence Summit. 2012, Washington, DC: USAID,http://www.usaid.gov/sites/default/files/documents/1864/CHW-Evidence-Summit-Final-Report.pdf,

  8. 8.

    Crigler L, Hill K, Furth R, Bjerregaard D: Community Health Worker Assessment and Improvement Matrix (CHW AIM): A Toolkit for Improving CHW Programs and Services. 2011, Bethesda, MD: University Research Corporation

  9. 9.

    Institute of Health Metrics and Evaluation/University of Washington: Financing Global Health 2013—Transition in an Age of Austerity. 2014, Seattle, WA: Institute of Health Metrics and Evaluation

  10. 10.

    Bosch-Capblanch X, Lavis JN, Lewin S, Atun R, Rottingen J-A, Droschel D, Beck L, Abalos E, El-Jardali F, Gilson L, Oliver S, Wyss K, Tugwell P, Kulier R, Pang T, Haines A: Guidance for evidence-informed policies about health systems: rationale for and challenges of guidance development. PLoS Med. 2012, 9 (3): e1001185-10.1371/journal.pmed.1001185.

  11. 11.

    Holmes B, Scarrow G, Schellenberg M: Translating evidence into practice: the role of health research funders. Implement Sci. 2012, 7: 39-10.1186/1748-5908-7-39.

  12. 12.

    Damschroder LJ, Aron DC, Keith RE, Kirsh SR, Alexander JA, Lowery JC: Fostering implementation of health services research findings into practice: a consolidated framework for advancing implementation science. Implement Sci. 2009, 4: 50-10.1186/1748-5908-4-50.

  13. 13.

    Landry R, Amara N, Pablos-Mendez A, Shademani R, Gold I: The knowledge-value chain: a conceptual framework for knowledge translation in health. Bull World Health Organ. 2006, 84: 597-602. 10.2471/BLT.06.031724.

  14. 14.

    Funnell SC, Rogers PJ: Purposeful Program Theory: Effective Use of Theories of Change and Logic Models. 2011, San Francisco, CA: Jossey-Bass

  15. 15.

    Kellogg Foundation WK: Logic Model Development Guide: Using Logic Models to Bring Together Planning, Evaluation and Action. 2004, W.K. Kellogg Foundation: Battle Creek, MI

  16. 16.

    Wholey JS, Hatry HP, Newcomer KE: Handbook of Practical Program Evaluation. 2010, San Francisco, CA: Jossey-Bass

  17. 17.

    Rogers P, Petrosino A, Huebner TA, Hacsi TA: Program theory evaluation: practice, promise, and problems. Program Theory in Evaluation: Challenges and Opportunities, New Directions for Evaluation. 2000, San Francisco, CA: Jossey-Bass, 87

  18. 18.

    Suchman EA: Evaluative Research: Principles and Practice in Public Service and Social Action Programs. 1967, Russell Sage Foundation: New York, NY

  19. 19.

    Weiss CH: Evaluation Research: Methods of Assessing Program Effectiveness. 1972, Prentice Hall: Englewood Cliffs, NJ

  20. 20.

    The World Bank: Monitoring and Evaluation: Some Tools, Methods and Approaches. 2002, Washington, DC: The World Bank

  21. 21.

    LeBan K: How Social Capital in Community Systems Strengthens Health Systems: People, Structures, Processes. 2011, Washington, DC: USAID Core Group,http://www.coregroup.org/storage/Program_Learning/Community_Health_Workers/Components_of_a_Community_Health_System_final10-12-2011.pdf,

  22. 22.

    USAID Health Care Improvement Project: Strengthening Community Health Systems to Improve Health Care at the Community Level. 2011, Bethesda, MD: University Research Corporation,http://www.urc-chs.com/uploads/resourceFiles/Live/CommunityHealthSystems_Oct11.pdf,

  23. 23.

    Shakir FK: Community Health Worker Programs: A Review of Recent Literature. 2010, Bethesda, MD: University Research Corporation,http://chwcentral.org/sites/default/files/Community%20Health%20Worker%20Programs-%20A%20Review%20of%20Recent%20Literature_0.pdf,

  24. 24.

    Perry H, Townsend J, McQueen S, Adana U, D’Harcourt E, Newsome M, Jones E, Roth R, Shreshta R, Swedberg E, Ngatia P, Altobelli L: Government Evidence Summit on Community and Formal health System Support for Enhanced Community Health Worker Performance. Evidence Synthesis Paper: Community, Prepared for the U.S. 2012, Washington, DC: USAID,http://www.usaid.gov/sites/default/files/documents/1864/ERT-1-Synthesis-Paper.pdf,

  25. 25.

    Berman P, Miller Franco L, Askew I, Boezwinkle J, Brown C, Cherian D, Crigler L, Fleisher L, Franco C, Higgs E, Kraushaar D, Meadowcroft L, Middleberg M, Naimoli J, Pacca J, Palen J, Peterson S, Scott V, Tharaney M, Williams T: Prepared for the U.S. Government Evidence Summit on Community and Formal Health System Support for Enhanced Community Health Worker Performance. Evidence Synthesis Paper: Formal Health System. 2012, Washington, DC: USAID,http://www.usaid.gov/sites/default/files/documents/1864/ERT-2-Synthesis-Paper.pdf,

  26. 26.

    Frehywot S, Wuliji T, Carnell M, Collins P, Cooney K, Dini HSF, Frymus D, Germanow J, Greer G, Herrington J, Hodgins S, Huicho L, Jacobs T, McCaffery J, Quain E, Stanback J, Starbuck E: Prepared for the U.S. Government Evidence Summit on Community and Formal Health System Support for Enhanced Community Health Worker Performance. Evidence Synthesis Paper: Combined Community and Formal Health System. 2012, Washington, DC: USAID,http://www.usaid.gov/sites/default/files/documents/1864/ERT-3-Synthesis-Paper.pdf,

  27. 27.

    Liberato SC, Brimblecombe J, Ritchie J, Ferguson M, Coveney J: Measuring capacity building in communities: a review of the literature. BMC Public Health. 2011, 11: 850-10.1186/1471-2458-11-850.

  28. 28.

    Raeburn J, Akerman M, Chuengsatiansup K, Mejia F, Oladepo O: Community capacity building and health promotion in a globalized world. Health Promot Int. 2007, 21 (Suppl 1): 84-90.

  29. 29.

    Goodman R, Speers MA, McLeroy K, Fawcett S, Kegler M, Parker E, Rathgeb Smith S, Sterling TD, Wallerstein N: Identifying and defining the dimensions of community capacity to provide a basis for measurement. Health Educ Behav. 1998, 258: 258-278.

  30. 30.

    WHO: Everybody’s Business: Strengthening Health Systems to Improve Health Outcomes – WHO’s Framework for Action. 2007, Geneva: World Health Organization

  31. 31.

    Berman P, Gwatkin D, Burger S: Community-based health workers: head start or false start towards health for all?. Soc Sci Med. 1987, 25 (5): 443-459. 10.1016/0277-9536(87)90168-7.

  32. 32.

    McLaughlin JA, Jordan GB: Logic models: a tool for telling your program’s performance story. Eval Program Plann. 1999, 22: 65-72. 10.1016/S0149-7189(98)00042-1.

  33. 33.

    Millar A, Simeone RS, John CT: Logic models: a systems tool for performance management. Eval Program Plann. 2001, 24: 73-81. 10.1016/S0149-7189(00)00048-3.

  34. 34.

    GHWA: Community Health Workers and other Frontline Health Workers: Moving from Fragmentation to Synergy to Achieve Universal Health Coverage. 2013, ᅟ: Side session at the Third Global Forum on Human Resources for Health,http://www.who.int/workforcealliance/knowledge/resources/chw_outcomedocument/en/,

  35. 35.

    Tulenko K, Mogedal S, Afzal MM, Frymus D, Oshin A, Pate M, Quain E, Pinel A, Wynd S, Zodpey S: Community health workers for universal health-care coverage: from fragmentation to synergy. Bull World Health Organ. 2013, 91: 847-852. 10.2471/BLT.13.118745.

  36. 36.

    Cummings FH: Logic models, logical frameworks and results-based management: contrasts and comparisons. Canadian J Dev Stud. 1997, 3: 587-596.

  37. 37.

    Bakewell O, Garbutt A: The Use and Abuse of the Logical Framework Approach. 2005, SIDA: Stockholm

  38. 38.

    Mackinnon A, Amott N: Mapping Change: Using a Theory of Change to Guide Planning and Evaluation. 2006, GrantCraft: New York and Brussels

  39. 39.

    James C: Theory of Change Review: A Report Commissioned by Comic Relief. 2011, ᅟ: Comic Relief,http://dmeforpeace.org/sites/default/files/James_ToC.pdf,

  40. 40.

    von Bertalanffy L: General Systems Theory: Foundations, Development, Applications. 1969, New York: George Braziller, Inc.

Download references

Acknowledgements

We thank James Heiby and Estelle Quain of USAID and four independent referees for their critical reviews of an earlier version of this manuscript. Funding for this paper was provided by the U.S. Agency for International Development.

The views expressed in this paper are solely those of the authors and do not necessarily reflect the views of the United States Agency for International Development or the United States Government.

Author information

Correspondence to Joseph F Naimoli.

Additional information

Competing interests

The authors declare that they have no competing interests.

Authors’ contributions

JN and DF conceived, designed, drafted, and finalized the manuscript. TW, LMF, and MN made substantial contributions to the development of all sections of the manuscript and were involved in critical reviews of multiple drafts. All authors read and approved the final manuscript.

Authors’ original submitted files for images

Below are the links to the authors’ original submitted files for images.

Authors’ original file for figure 1

Rights and permissions

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Keywords

  • Causal thinking
  • Community health workers
  • Logic model
  • Performance
  • Policy
  • Programming