Evaluating the use of the consolidated framework for implementation research in primary care settings: a systematic review

Abstract

Background

Previous systematic reviews have examined the use of the 2009 version of the Consolidated Framework for Implementation Research (CFIR) in healthcare settings. However, these reviews primarily focused on studies conducted in secondary and tertiary care, with limited attention to its application in primary care. The use of the CFIR in primary care remains underexplored. Given the unique attributes of primary care—guiding principles such as first-contact care, continuity, comprehensiveness, coordination, and people-centeredness—findings from studies in other healthcare settings may not fully translate to the primary care context. This systematic review aimed to investigate how the CFIR has been applied in primary care, evaluate how it aligns with the guiding principles of primary care, and propose refinements to enhance its future application.

Methods

We searched Scopus and PubMed for publications including the terms CFIR and primary care from 2009 to February 15, 2024. We included studies that addressed clinical, organizational, or service delivery interventions implemented in primary care settings interventions within primary care settings. Data abstraction focused on several variables, including study design and location, participants, health topic, CFIR domains and constructs used, rationale for use, and additional implementation frameworks. We also evaluated how the applied constructs related to the WHO guiding principles of primary care.

Results

Out of 394 studies, 105 met the inclusion criteria. The use of the CFIR in primary care steadily increased between 2015 and 2024. Most studies were qualitative (80.9%), focused on non-communicable diseases (19%), conducted during the post-implementation phase (43.8%), centered on healthcare workers' perceptions (40%), and conducted in high-income country (83%). Most studies (61%) applied the five domains of the CFIR. However, 53.3% of the studies did not reported the rationale for selecting the domains. Some CFIR constructs investigated aligned with the guiding principles of primary care, particularly people-centeredness and comprehensiveness.

Conclusions

Refinements for applying the CFIR in primary care include enhancing community participation throughout the research process, from study design to interpretation and development of practice recommendations; reporting the rationale for selecting CFIR constructs, including their alignment with the guiding principles of primary care; increasing pre-implementation evaluation to support longitudinal, formative implementation research; and strengthening the role of implementation research in healthcare policies.

Systematic Review Registration

osf.io/4yq2f

Supplementary Information

The online version contains supplementary material available at 10.1186/s43058-026-00866-6.

Contributions to the literature.

• Previous systematic reviews have focused on evaluating the use of the CFIR in secondary and tertiary care in Global North countries; however, its application in primary care has been overlooked. This systematic review assesses the use of the CFIR specifically in primary care contexts.

• In prior studies, the rationale for selecting CFIR – particularly its alignment with the guiding principles of primary care constructs—was rarely reported.

• Guidance for refining the use of the CFIR includes better operationalization of interest holders and community participation, as well as alignment with primary care core principles.

Introduction

Primary care has played a pivotal role in improving health-related outcomes, reducing inequalities in access to care, and enhancing the resilience of health systems [1–3]. The impacts of primary care on population health are directly linked to its guiding principles— first-contact care, people-centeredness, continuity, comprehensiveness, intersectoral coordination, and community engagement, and community-driven approach that addresses social determinants of health, and promotes efficient use of resources [4–6]. These principles distinguish primary care from secondary and tertiary care and shape how interventions are implemented, often involving multidisciplinary teams, community-based services, and contextualized patient relationships.

In this review, we adopted Starfield’s widely used definition of primary care as the first level of contact within the health system, characterized by first-contact accessibility, continuity, comprehensiveness, coordination of care, and a people-centered approach that integrates individuals’ and communities’ needs [1]. Primary care actions include health promotion, vaccination, disease prevention, and the treatment of both noncommunicable and communicable diseases. Additionally, primary care supports maternal, newborn, child, mental, sexual, and reproductive health [4]. Therefore, applying strategies to enhance the effectiveness [7] and scalability of primary care is essential to improving health outcomes, particularly in contexts with remarkable inequities and constrained resources, such as in low- and middle- income countries (LMICs) [8].

Implementation research has been conducted to improve the adoption, dissemination, and sustainability of evidence-based health interventions, reducing the evidence-practice gap and improving primary care effectiveness [9, 10]. One of the most widely applied frameworks in implementation science for evaluating healthcare global interventions is the Consolidated Framework for Implementation Research (CFIR) [11]. The CFIR is a determinant framework used to assess contextual factors influencing the implementation of evidence-based interventions in real-world settings. It provides a structured approach to identifying determinants that facilitate or hinder implementation across different contexts and can pragmatically inform implementation planning, strategy selection, monitoring, and adaptive decision-making [12, 13]. The framework supports examination of the implementation process across stages, including adoption, dissemination, integration, and sustainability of interventions in healthcare settings. It comprises five key domains: (1) Intervention Characteristics, referring to features of the intervention such as adaptability; (2) Outer Setting, encompassing external influences including patient needs, and policies; (3) Inner Setting, which includes organizational culture, structure, and available resources; (4) Characteristics of Individuals, addressing the knowledge, beliefs, and readiness of those involved; and (5) Process, referring to planning, stakeholder engagement, execution, and monitoring activities. Together, these domains capture multilevel determinants shaping implementation across interventions, contexts, actors, and processes. By examining these domains, CFIR provides a comprehensive framework for understanding the determinants of implementation success, making it particularly useful for evaluating the adoption, sustainability, and scalability of interventions in primary care settings.

Previous systematic reviews—most notably by Kirk et al. [11] and Means et al. [14]—made significant contributions to the understanding of how the CFIR has been applied across a range of implementation studies. Kirk et al. [11] demonstrated that while the CFIR was widely used, its constructs were often applied post hoc, with limited justification for construct selection and minimal linkage to implementation outcomes. Means et al. [14] examined the application of the CFIR in LMICs and proposed both conceptual and operational refinements to enhance its relevance and measurement in these contexts. Their recommendations included identifying constructs that were less compatible in LMIC settings, integrating sociocultural, team-level, and health system–level determinants, and proposing a new “Characteristics of Systems” domain with additional constructs to capture system architecture, policy alignment, resource continuity, and scalability, thereby strengthening the framework’s applicability beyond organizational-level analyses. These reviews were instrumental in identifying challenges in operationalization and standardization of CFIR-based research. However, they focused predominantly on hospital-based, specialty care, and other secondary or tertiary healthcare settings, where organizational structures, care processes, and patient pathways differ substantially from those of primary care.

In this review, the CFIR is used as an analytical lens to examine how its domains and constructs have been applied in primary care implementation research. We sought to fill this gap by examining how the CFIR has been used in primary care settings. In addition, we aimed to (i) describe how CFIR constructs have been selected and operationalized in primary care implementation studies, and (ii) assess the extent to which the constructs addressed in these studies are consistent with the WHO guiding principles of primary care [9, 10]. We hypothesize, for example, that constructs such as "Networks and Communications," "Available Resources," and "Patient Needs and Resources" may exert a more pronounced influence in primary care settings, where informal communication, resource constraints, and contextual patient factors are deeply embedded in care delivery. Conversely, constructs such as "External Policy and Incentives" may manifest differently due to the less centralized governance of many primary care systems, especially in LMICs. To the best of our knowledge, no previous review has examined the use of the CFIR in primary care settings or addressed whether implementation framework applications align with the guiding principles of primary care.

Method

We used CFIR constructs to categorize and synthesize determinants of implementation identified in the selected studies. Given that the CFIR underwent an update in 2022, we prespecified that this review would use the original 2009 version of the framework. The CFIR 2.0 update introduced major modifications, including restructuring of domains, expansion and renaming of constructs, and incorporation of multilevel causal pathways, that limit its direct comparability with studies grounded in the 2009 framework. This systematic review protocol is registered in the Open Science Framework (registration: osf.io/4yq2f) and follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines [15] (PRISMA checklist – additional file 1).

We searched PubMed and Scopus from 2009 to February 15, 2024 to identify original peer-reviewed research in any language that cited the original CFIR and primary care. The search strategy was intentionally broad to maximize sensitivity, given the heterogeneous reporting indexing of CFIR across databases, as well as the variability in how “primary care” is described in the international literature. Accordingly, the final search equations were:

PubMed “(("consolidated framework for implementation research"[All Fields] OR "CFIR"[All Fields]) AND "primary health care"[MeSH Terms])“

Scopus(TITLE-ABS-KEY("Consolidated Framework for Implementation Research") OR TITLE-ABS-KEY(cfir)) AND TITLE-ABS-KEY("primary care")

In addition to the electronic database search, we conducted a manual search to identify potentially relevant studies not captured through automated strategies. This process involved screening the reference lists of all studies included after full-text review, as well as the reference lists of key systematic reviews examining the use of CFIR in implementation research. The same inclusion and exclusion criteria applied in the primary search were used to assess eligibility.

Study inclusion and exclusion criteria

We included only studies focusing on the CFIR as an implementation framework in primary care settings. Eligible studies included empirical qualitative, quantitative, mixed-methods, or hybrid effectiveness–implementation designs that used CFIR, fully or partially, to examine the implementation of healthcare delivery interventions. Complex or multicomponent interventions were eligible, and no minimum number of CFIR constructs was required for inclusion.

We focused on peer-reviewed literature and not included dissertations, and book chapters. Studies relying on secondary data (e.g., reviews, editorials, and commentaries) or study protocols were excluded. These criteria were applied consistently during title/abstract and full-text screening and are reflected in the PRISMA flow diagram.

Data collection and extraction strategy

After removing duplicates, the following steps were performed using the Rayyan software. Initially, two reviewers (ATCS and LYTU) screened the titles and abstracts. Subsequently, the group of eight reviewers was divided into pairs to assess the full-text articles. Each reviewer independently evaluated the assigned articles. Disagreements were resolved by consensus, and when necessary, a third reviewer was consulted to make the final decision. Reviewers’ experience included roles such as healthcare practitioners, health system managers, and researchers involved in the evaluation and implementation of health services. They were familiar with the dynamics of healthcare settings, including the challenges and nuances of health interventions across different levels of care. To ensure reliability, the reviewers underwent targeted training on the CFIR framework and its constructs, followed by a calibration exercise to validate the spreadsheet and ensure alignment in data collection. We created an extraction spreadsheet based on the elements of the research question, including the following information: year of publication, country, study design, methodology (quantitative, qualitative, or mixed-methods), participants (healthcare workers [HCWs], managers, and patients), health topic investigated, phase of implementation (pre-, during-, and post-implementation), specific CFIR domains and constructs used, and the rationale for selecting the constructs. Disagreements were resolved through consensus discussion. The classification of countries as high-, middle-, or low-income was based on the World Bank classification criteria [16].

Data synthesis and analysis

The coding process was conducted manually by the reviewers. Data analysis was conducted using a descriptive approach guided by the standardized data extraction form. Frequencies and percentages were employed to summarize the characteristics of the included studies. To assess the application of the CFIR, we analyzed the frequency of CFIR use, the specific CFIR domains—intervention characteristics, outer setting, inner setting, characteristics of individuals, and process—and constructs reported within each domain [13], the rationale provided by the studies for choosing CFIR domains and constructs; and the utilization of CFIR domains and constructs across different domains to identify patterns in reporting and usage.

We evaluated the relationships between the CFIR constructs applied and the guiding principles of primary care outlined by the WHO and emphasized in the Declaration of Astana [4]: first-contact care, people-centeredness, continuity, comprehensiveness, intersectoral coordination, and community engagement. The mapping was conducted using a structured approach: (1) a deductive coding process in which CFIR constructs were aligned with primary care principles based on theoretical and conceptual similarities; (2) an independent review by two authors to ensure consistency in the mapping; and (3) resolution of discrepancies through discussion with a third reviewer. This process ensured a systematic and reproducible alignment between the CFIR and primary care principles. By prioritizing equity and community engagement, the Declaration of Astana advocates for a strengthened healthcare system that empowers individuals and communities. It underscores the importance of primary care in addressing the growing burden of noncommunicable diseases, reducing premature deaths caused by tobacco and alcohol use, and promoting healthy lifestyles and behaviors.

In Table 1, we summarized the mapping between CFIR constructs and the WHO guiding principles of primary care, illustrating how each construct was deductively aligned with one or more principles.

Table 1.

Mapping of CFIR Constructs to Primary Care Guiding Principles

CFIR Domain	CFIR Construct	Primary Care Guiding Principles	Rationale for Alignment
Intervention Characteristics	Complexity	Comprehensiveness; Cultural competence	Complex interventions require tailoring to diverse patient needs and service scope typically delivered in primary care
	Adaptability	Comprehensiveness; Cultural competence	Adaptation is essential for addressing varied clinical, social, and cultural contexts
	Cost	Continuity; Comprehensiveness	Affordability influences patients’ ability to maintain continuous and comprehensive care
Outer Setting	Patient Needs & Resources	People-centeredness; Comprehensiveness	Directly addresses how care responds to patient priorities, contexts, and social determinants
	External Policies & Incentives	Intersectoral coordination; Community engagement	Reflects how policies shape collaboration across sectors and community-level action
	Cosmopolitanism	Intersectoral coordination; Community engagement	Emphasizes collaboration with other services, community organizations, and sectors
Characteristics of Individuals	Knowledge & Beliefs	Comprehensiveness; Cultural competence	Providers’ understanding influences delivery across diverse patient needs and contexts
Process	Engaging	Community engagement; Cultural competence	Engagement strategies rely on communication, trust-building, and involvement of diverse community actors

Results

Our database search yielded 678 articles. After removing 284 duplicates, 394 studies remained to be assessed. We reviewed the titles and abstracts of these articles and excluded 55 articles for the following reasons: not conducted in primary care, not primary research, or being protocol papers. A full-text review was conducted on the remaining 339 articles, of which 234 were excluded for the following reasons: not based on primary care, not primary research (e.g., systematic or narrative reviews), being protocol papers, or not investigating healthcare interventions (e.g., administrative issues) (Fig. 1). The final sample included 105 studies. Table 2 shows the selected studies. Most of them were conducted in high-income countries, particularly in the US and Canada.

Fig. 1.

PRISMA flowchat of systematic review

Table 2.

Summary of included studies (n = 105)

Authors	Year of publication	Country	Healthcare topic	Intervention	Study design	Effectiveness-implementation hybrid studies	Phase of implementation	Application	Rationale for construct selection	Additional framework
Robins et al. [17]	2013	US	Hypertension	electronic communications and home blood pressure monitoring	Qualitative	No	Pre-implementation	data analysis	Yes	Yes (PRISM)
Cole et al. [18]	2015	US	Colorectal cancer	colorectal cancer screening	Qualitative	No	Pre-implementation	data collection, data analysis	Yes	No
Liang et al. [19]	2016	US	Cancer screening	safety net system for cancers screening	Qualitative	No	During implementation	data collection, data analysis	No	No
Sopcak et al. [20]	2016	Canada	Chronic disease	chronic disease prevention and screening	Qualitative	No	During implementation	data analysis	No	No
Palacio et al. [21]	2016	US	Chronic disease	medication delivery	Mixed Methods	No	Post-implementation	data analysis	No	Yes (RE-AIM)
Martinez et al. [22]	2017	Spain	Health promotion	multiple healthy habits	Qualitative	No	During implementation	data analysis	No	No
Kowalski et al. [23]	2018	England	Diabetes	diabetes control programs	Qualitative	No	Pre-, during and Post-implementation	data analysis	No	No
Warner et al. [24]	2018	Canada	Frailty	Frailty assessment and care planning	Qualitative	No	Pre-implementation	data collection, data analysis	Yes	No
Garbutt et al. [25]	2018	US	HPV vaccination	HPV vaccination	Mixed Methods	No	Post-implementation	data collection, data analysis	Yes	No
Garbutt et al. [26]	2018	US	HPV vaccination	HPV vaccination	Mixed Methods	No	During implementation	data collection, data analysis	Yes	Yes (BCW; TDF)
Stephan et al. [27]	2018	Germany	Vertigo	Vertigo treatment	Qualitative	No	Pre-implementation	Data collection	Yes	Yes (COM-B; TDF; EPOC)
Holloway et al. [28]	2018	Australia	Depression	telephone-based problem-solving treatment	Qualitative	No	During implementation	data collection, data analysis	No	Yes (TDF)
Adamu et al. [29]	2019	Nigeria	Vaccination	Reducing vaccination hesitancy program	Mixed Methods	No	Pre-implementation, During implementation	data collection, data analysis	No	No
Hagedorn et al. [30]	2019	US	Alcohol use disorder	alcohol use disorders pharmacotherapy	Qualitative	No	Pre-implementation	data analysis	No	No
Shade et al. [31]	2019	US	Asthma	asthma shared decision-making intervention	Quantitative	No	During implementation	Data collection, data analysis	No	No
Harry et al. [32]	2019	US	Cancer	clinical decision support program	Qualitative	No	Pre-implementation	data collection, data analysis	Yes	No
Varley et al. [33]	2019	US	Chronic pain and opioid use disorder	practices for co-occurring chronic pain and opioid use disorder	Qualitative	No	Post-implementation	data collection, data analysis	Yes	No
Hahn et al. [34]	2019	US	Cancer	cancer survivorship care models	Qualitative	No	Pre-implementation	data analysis	Yes	No
Morgan et al. [35]	2019	Canada	Dementia	Interdisciplinary care	Qualitative	No	Pre-, during and Post-implementation	data collection, data analysis	Yes	No
Pannebakker et al. [36]	2019	England	Cancer	electronic clinical decision support for melanoma	Qualitative	No	Post-implementation	data analysis	Yes	No
Escoffery et al. [37]	2019	US	Vaccination	HPV—Vaccinate Adolescents Against Cancers program	Qualitative	No	Post-implementation	data collection, data analysis	Yes	No
Harry et al. [38]	2020	US	Cancer	clinical decision support program	Qualitative	No	Pre-implementation	data analysis	No	No
Koffel & Hagedorn [39]	2020	US	Insomnia	Cognitive behavioral therapy	Qualitative	No	Pre-implementation	data analysis	No	No
Ndejjo et al. [40]	2020	Uganda	Cardiovascular disease	community cardiovascular disease program	Qualitative	Yes—Type II	During implementation	data collection, data analysis	No	No
Godbee et al. [41]	2020	Australia	Dementia	dementia risk reduction program	Qualitative	No	Post-implementation	data analysis	No	No
Radovic et al. [42]	2020	US	Depression and anxiety	web-based intervention to increase depression and anxiety treatment	Qualitative	Yes—Type II	Pre-, during and Post-implementation	data analysis	No	No
Muddu et al. [43]	2020	Uganda	Hypertension and HIV	Hypertension and HIV integrated care program	Qualitative	No	Pre-implementation	data collection, data analysis	Yes	No
Simione et al. [44]	2020	US	Obesity	weight management program	Mixed Methods	No	During implementation	data collection, data analysis	Yes	No
Vest et al. [45]	2020	US	Mental health	Pharmacogenetic testing	Qualitative	No	Pre-implementation	data analysis	No	No
VanDevanter et al. [46]	2020	Vietnam	Smoking cessation	tobacco dependence treatment	Qualitative	No	Pre-implementation	data collection, data analysis	Yes	No
Socias et al. [47]	2021	Spain	Benzodiazepine-use reduction strategy	General practitioners training and follow up	Qualitative	Yes—Type I	Post-implementation	data collection, data analysis	No	No
Nelson-Brantley et al. [48]	2021	US	Cancer screening	Cancer screening in rural areas	Qualitative	No	During implementation	data collection, data analysis	Yes	No
Hahn et al. [49]	2021	US	Cancer screening	HPV test	Qualitative	No	During implementation	data collection, data analysis	No	No
Soukup et al. [50]	2021	US	Cholesterol screening	pediatric cholesterol screening	Qualitative	No	Post-implementation	data analysis	Yes	No
Ahmed et al. [51]	2021	US	Chronic pain	electronic patient-reported outcome measures	Qualitative	No	Pre-implementation	data analysis	No	No
Holden et al. [52]	2021	Australia	Colorectal cancer	Colorectal cancer screening	Qualitative	No	During implementation	data collection, data analysis	No	No
Paciocco et al. [53]	2021	Canada	COPD	COPD management program	Qualitative	No	Post-implementation	data collection, data analysis	Yes	No
Okoli et al. [54]	2021	Nigeria	Hypertension	Hypertension treatment program	Qualitative	No	Pre-implementation	data collection, data analysis	No	No
Rogers et al. [55]	2021	Spain	Health Promotion	health promotion intervention program	Qualitative	No	Post-implementation	data collection and analysis	Yes	No
Teeter et al. [56]	2021	US	HPV vaccination	pharmacist-physician collaboration models to improve HPV vaccination rates	Qualitative	No	Post-implementation	data collection	No	No
Kemp et al. [57]	2021	South Africa	Mental Health, depression	Integrated mental health service	Mixed Methods	No	Post-implementation	data collection, data analysis	No	No
Ngangue et al. [58]	2021	Canada	Multimorbidity	interdisciplinary patient-centered care intervention	Qualitative	No	Post-implementation	data collection, data analysis	Yes	No
Reckrey et al. [59]	2021	US	Aging	intervention to reduce serious fall injuries in older adults	Qualitative	Yes—Type I	Post-implementation	data collection, data analysis	Yes	No
Zabaleta-del-Olmo et al. [60]	2021	Spain	Smoking cessation, physical activity, and healthy diet	Multiple health behavior change intervention	Mixed Methods	Yes—Type II	Post-implementation	data analysis	No	No
Piper et al. [61]	2021	US	Mental health, and HIV	trauma-informed care	Qualitative	No	Pre-implementation	data collection, data analysis	Yes	No
Block et al. [62]	2021	Mexico	Diabetes	Chronic Disease Preventive Model	Mixed Methods	No	Post-implementation	data collection	No	No
Thompson et al. [63]	2021	US	Violence	Interpersonal Violence Screening Program	Qualitative	No	Post-implementation	data collection, data analysis	No	No
Warner et al. [64]	2021	Canada	Palliative care	community-based intervention	Qualitative	No	During implementation, post-implementation	data collection, data analysis	Yes	No
Fiechtner et al. [65]	2021	US	Childhood obesity	Weight Management Intervention	Qualitative	No	Pre-implementation	data collection	Yes	Yes (RE-AIM)
Kola et al. [66]	2021	Nigeria	Perinatal depression	mobile phone supported intervention	Qualitative	No	Post-implementation	data collection	No	Yes (TDF, COM-B)
Aerts et al. [67]	2022	Belgium	Cardiovascular disease	multicomponent intervention for the primary prevention of CVD	Qualitative	No	Pre-implementation	data collection, data analysis	No	No
Radovic et al. [68]	2022	US	Depression and suicidality	digital behavioral health applications	Mixed Methods	No	Pre-, during and Post-implementation	data analysis	Yes	No
Al-Arkee et al. [69]	2022	UK	Atrial fibrillation	Pharmacist management program	Qualitative	No	Post-implementation	data collection	No	No
Davis et al. [70]	2022	US	Colorectal cancer	Mailed screening test	Qualitative	No	Post-implementation	data collection	No	No
Milton et al. [71]	2022	Australia	Colorectal cancer	Screening test	Qualitative	No	During implementation	data collection, data analysis	No	No
Seidel et al. [72]	2022	German	Dementia	Dementia Care Management Intervention	Qualitative	No	Post-implementation	data collection, data analysis	Yes	No
Smith et al. [73]	2022	US	Dementia	Multicomponent Intervention to Improve Communication	Qualitative	No	Post-implementation	data analysis	No	No
Fu et al. [74]	2022	US	Depression and anxiety	Collaborative Care for Depression and Anxiety	Mixed Methods	No	Pre-implementation	data collection, data analysis	No	No
Rasooly et al. [75]	2022	China	Diabetes	Diabetes care management	Qualitative	No	Post-implementation	data analysis	No	No
Ware et al. [76]	2022	US	Diabetic retinopathy	telemedicine diabetic retinopathy screening	Mixed Methods	No	Post-implementation	data analysis	No	No
Hennein et al. [77]	2022	Uganda	Diabetes and TB	Screening for diabetes	Qualitative	No	Pre-implementation	data collection, data analysis	No	No
Pratt et al. [78]	2022	US	Diabetes	clinical decision support tool	Qualitative	No	During implementation	data analysis	Yes	No
Taher et al. [79]	2022	US	Food security	food security screening	Qualitative	No	Post-implementation	data collection, data analysis	Yes	No
Montena et al. [80]	2022	US	Mental health	Peer-supported mobile applications	Qualitative	No	Post-implementation	data collection, data analysis	Yes	No
Endris et al. [81]	2022	Ethiopia	Nutrition	nutrition interventions	Qualitative	No	Post-implementation	data collection, data analysis	No	No
Espel-Huynh et al. [82]	2022	US	Obesity	online behavioral obesity treatment	Qualitative	Yes—Type II	Post-implementation	data collection, data analysis	Yes	No
Persaud et al. [83]	2022	US	Obesity	pediatric weight management	Qualitative	No	During implementation	data collection	No	No
Ferketa et al. [84]	2022	US	Pregnancy Intention	Pregnancy Intention Screening Tool	Qualitative	No	Post-implementation	data collection, data analysis	Yes	No
Ahmad et al. [85]	2022	US	Sexually Transmitted Infection	Sexually Transmitted Infection Screening	Qualitative	No	Post-implementation	data analysis	No	No
Ridenour et al. [86]	2022	US	Risky Health Behaviors	Prevention for Youth Risky Health Behaviors	Quantitative	No	Post-implementation	data collection, data analysis	Yes	No
Huang et al. [87]	2022	China	Vaccination	Pneumococcal vaccination intervention	Qualitative	No	Post-implementation	data analysis	Yes	Yes (RE-AIM)
Ludden et al. [88]	2022	US	Asthma	collaborative web-based application	Mixed Methods	Yes—Type I	Pre-implementation, Post-implementation	data collection	No	Yes (RE-AIM)
Toto et al. [89]	2023	US	Aging	person-centered intervention	Qualitative	Yes—Type I	Pre-implementation	data analysis	No	No
Sopcak et al. [90]	2023	Canada	Cancer and chronic disease	cancer and chronic disease prevention and screening program	Qualitative	No	Post-implementation	data analysis	Yes	No
Brenner et al. [91]	2023	US	Colorectal cancer	colorectal cancer screening	Qualitative	No	Pre-implementation	data collection, data analysis	No	No
Schlueter et al. [92]	2023	US	Colorectal Cancer	colorectal cancer screening	Qualitative	No	During implementation	data collection, data analysis	Yes	No
Smoorenburg et al. [93]	2023	Netherlands	Prenatal	integrated screening program (hypertension or diabetes)	Qualitative	No	During implementation	data collection, data analysis	No	No
Badacho & Mahomed [94]	2023	Ethiopia	Diabetes, hypertension, and HIV	Integrated care program	Qualitative	No	Post-implementation	data collection, data analysis	Yes	No
Freeland et al. [95]	2023	Nigeria	Hepatitis B vaccination	Hepatitis B vaccination	Qualitative	No	Post-implementation	data collection and analysis	No	No
Maciel et al. [96]	2023	Brazil	Health Promotion	Physical activity and nutrition intervention	Qualitative	No	Post-implementation	data analysis	Yes	No
Strid, Wallin & Nilsagard [97]	2023	Sweden	Health Promotion	healthy lifestyle-promoting practice	Qualitative	No	Pre-implementation	data analysis	Yes	No
Selohilwe et al. [98]	2023	South Africa	Mental health	task- sharing counselling	Qualitative	No	During implementation	data collection, data analysis	No	No
Ho et al. [99]	2023	US	Peripheral arterial disease	Screening for peripheral arterial disease	Qualitative	No	Pre-implementation	data collection, data analysis	No	No
Faija et al. [100]	2023	UK	Mental health	psychological interventions delivered by telephone	Qualitative	No	Post-implementation	data analysis	Yes	No
Shin et al. [101]	2023	US	Multimorbidity	multiple chronic conditions care	Qualitative	No	During implementation	data collection, data analysis	No	No
Siösteen-Holmblad et al. [102]	2023	Sweden	Post-partum contraception	contraceptive program in postpartum care	Qualitative	No	Post-implementation	data analysis	Yes	No
Le et al. [103]	2023	US	HPV	self-collection for HPV testing	Qualitative	No	Pre-implementation	data collection, data analysis	Yes	No
van Westen‐Lagerweij et al. [104]	2023	Netherlands	Smoking cessation	ask-advise-connect approach	Mixed Methods	No	Pre-implementation, Post-implementation	data collection, data analysis	No	No
Wong et al. [105]	2023	Malaysia	Tuberculosis	tuberculosis-directly observed treatment	Mixed Methods	No	Pre-implementation	data collection, data analysis	Yes	No
Raffa et al. [106]	2023	US	COVID-19	telemedicine for COVID-19 care	Qualitative	No	Post-implementation	data analysis	No	No
van de Water et al. [107]	2023	South Africa	Tuberculosis	tuberculosis preventive therapy program	Qualitative	No	Pre-implementation	data analysis	No	No
Kirkland et al. [108]	2023	US	Diabetes	diabetes remote monitoring program	Mixed Methods	No	Post-implementation	data collection, data analysis	No	No
Onakomaiya et al. [109]	2023	US	COVID-19, Diabetes	community-clinic linkage model	Qualitative	No	Post-implementation	data collection, data analysis	Yes	Yes (MADI)
Lee et al. [110]	2023	US	Cancer	primary cancer prevention	Mixed Methods	No	During implementation	data collection, data analysis	No	Yes (Proctor)
Aerts et al. [111]	2023	Belgium	Cardiovascular disease	prevention program for CVD	Qualitative	No	Pre-, during and Post-implementation	data collection, data analysis	Yes	Yes (RE-AIM)
Sudore et al. [112]	2023	US	Palliative care	advance care planning	Quantitative	No	Pre-, during and Post-implementation	data collection	No	Yes (RE-AIM)
Philpot et al. [113]	2023	US	Chronic pain	digital support patient care	Mixed Methods	No	Pre-, during and Post-implementation	data collection	No	Yes (RE-AIM)
Talrich et al. [114]	2023	Belgium	Antenatal care	antenatal care program	Qualitative	No	During implementation	data collection, data analysis	No	Yes (TDF)
Arena et al. [115]	2023	US	Colorectal Cancer	colorectal Cancer Screening	Qualitative	No	Post-implementation	data collection	No	Yes (FRAME-IS)
Goff et al. [116]	2024	US	Fluoride varnish	fluoride varnish application	Qualitative	No	During implementation	data collection, data analysis	No	No
Langley et al. [117]	2024	Canada	Palliative care	earlier access to palliative care program	Mixed Methods	No	Pre-implementation	data collection, data analysis	Yes	No
Nishimura et al. [118]	2024	Japan	Benzodiazepine	benzodiazepine deprescribing	Qualitative	No	Post-implementation	data analysis	Yes	No
Minian et al. [119]	2024	Canada	Suicide	suicide prevention protocol	Mixed Methods	No	Pre-implementation	data collection, data analysis	Yes	No
Parks et al. [120]	2024	Malawi	COVID-19	telemedicine	Qualitative	No	Post-implementation	data collection, data analysis	Yes	No
van der Laag et al. [121]	2024	Netherlands	Health Promotion	nutrition and exercise intervention	Qualitative	No	Pre-implementation	data analysis	Yes	Proctor

We found that the use of the CFIR in primary care progressively increased from 2015 to 2024 (Table 3). Most studies were qualitative and applied the CFIR during the post-implementation phase (43.8%) and pre-implementation phase (27.6%) of healthcare interventions. Only 7% were effectiveness-implementation hybrid studies. The CFIR was most commonly used for both data collection and analysis. For data collection, the framework was frequently used to structure interview guides and extract determinants of implementation, such as barriers, facilitators, and contextual conditions. For data analysis, in some cases, CFIR constructs guided the evaluation of how contextual factors shaped implementation success and supported decisions related to selecting or adapting implementation strategies. The rationale for selecting specific CFIR constructs was not explained in 53.3% of the studies. Among these studies, a recurrent pattern involved the use of multiple constructs from different CFIR domains without clarifying their relevance to the intervention or setting, which hindered interpretability. In contrast, a smaller subset of studies offered explicit justifications for construct selection, typically grounded in theoretical alignment or contextual considerations, resulting in greater methodological coherence. Associations with one or more implementation research frameworks were reported in 17 studies (16.2%), with the Reach, Effectiveness, Adoption, Implementation, and Maintenance (RE-AIM) framework being the most frequently cited additional framework.

Table 3.

Characteristics of selected studies that uses of CFIR in primary care settings (N = 105)

	N	%
Setting
High-income country	87	83.0
Middle-income country	12	11.4
Low-income country	6	5.6
Publication year
Before 2015	1	0.1
2015—2017	5	4.7
2018—2020	24	22.8
2021—Feb 2024	75	71.4
Study design
Qualitative	85	80.9
Quantitative	3	2.8
Mixed Methods	19	21.3
Phase of CFIR application
Post-implementation	46	43.8
Pre-implementation	29	27.6
During implementation	21	20.0
Pre-. during and post-implementation	7	6.5
Pre-implementation, post-implementation	2	1.9
Pre-implementation, during implementation	1	0.1
During implementation, post-implementation	1	0.1
Effectiveness-implementation hybrid study
No	98	93.0
Yes—Type I	4	3.5
Yes—Type II	4	3.5
Number of CFIR domains used
One	2	1.9
Two	9	8.2
Three	8	7.2
Four	20	18.9
All 5	66	61.0
Not reported	3	2.8
Phase of CFIR application
Data collection and data analysis	60	57.2
Data analysis	33	31.4
Data collection	12	11.4
Rationale for domains selection
No	56	53.3
Yes	49	46.7
CFIR associated with another framework*
No	88	83.8
Yes	17	16.2

^*Reach, Effectiveness, Adoption, Implementation, and Maintenance (RE-AIM): n = 7

Proctor's Model for Implementation Outcomes (PROCTOR): n = 2

Theoretical Domains Framework (TDF) n = 2

TDF and Capacity, opportunity, and motivation for behavior change (COM-B) n = 1

TDF, Cochrane Effective Practice and Organization of Care Group (EPOC), and COM-B n = 1

Behavior Change Wheel (BCW) n = 1

Practical Implementation Sustainability Model (PRISM) n = 1

Model for Adaptation design and impact (MADI) n = 1

Framework for Reporting Adaptations and Modifications to Evidence-based Implementation Strategies (FRAME-IS) n = 1

There were 21 different health topics addressed in the included studies. Noncommunicable diseases (such as hypertension and diabetes), cancer, and mental health conditions (including depression and anxiety) were the most investigated topics (Fig. 2). Across the included studies, innovations encompassed a range of intervention types implemented in primary care, including clinical decision support tools for chronic disease management, screening and prevention programs (e.g., cancer screening, vaccination initiatives), task-shifting and team-based care models, digital health and telehealth interventions, and community- or pharmacy-integrated care approaches. These innovations were primarily designed to improve the delivery of disease-specific services within primary care settings rather than to modify core primary care structures or processes.

Fig. 2.

Healthcare topics addressed in the selected studies. *Sopcak et al. investigated cancer and chronic diseases; Varley et al. investigated chronic pain and opioid use disorder; Hennein et al. investigated diabetes in patients with tuberculosis; Muddu et al. investigated diabetes and HIV; Zableta-del-Olmo et al. investigated smoking cessation, physical activity, and healthy diet. Those studies were included into categories, respectively, cancer and non-transmissible disease, chronic pain and addiction, non-transmissible disease and infectious disease, non-transmissible disease and infectious disease, and smoking cessation, and health promotion

Figure 3 shows the participants included in the studies. We found that 40% of the studies focused solely on HCWs, and 46% addressed only one type of participant. While the inclusion of multiple interest holders is strongly recommended in implementation research, only 15.2% of the studies involved HCWs, managers, and patients, who are essential for evaluating the implementation process and the delivery of innovations within primary care settings. The 2009 version of the CFIR is primarily intended to guide data collection from individuals who influence implementation effectiveness, such as healthcare providers and managers. However, some studies also included patients’ perspectives, which may reflect an adaptation or extension of the CFIR to incorporate patients’ voices.

Fig. 3.

Group of participants included in the selected studies (N = 105)

Figure 4 displays the CFIR constructs investigated in the selected studies, categorized by Domain. The most frequently reported constructs were: in Domain 1 (Intervention Characteristics) Complexity (45%) and Adaptability (38%); in Domain 2 (Outer Setting), Patient Needs and Resources (52%), and External Policy and Incentives (42%); in Domain 3 (Inner Setting), Networks and Communication (41%), and Implementation Climate (39%); in Domain 4 (Characteristics of Individuals), Knowledge and Beliefs about the Intervention (46%); and in Domain 5 (Process), Engaging and Planning (32%). Conversely, the least investigated CFIR constructs were Trialability, Intervention Source, and Cost (Domain 1), respectively, 9%, 10%, and 11%; Peer Pressure (18%) and Cosmopolitanism (20%) (Domain 2); Learning Climate (19%), and Goals and Feedback (16%) (Domain 3); Individual Identification with the Organization (10%) and Individual Stage of Change (18%) (Domain 4); and Formally Appointed Internal Implementation Leaders (12%) and Opinion Leaders (14%) (Domain 5).

Fig. 4.

Distribution of the constructs of CFIR by dimension in the selected studies

With respect to the alignment of CFIR constructs with the guiding principles of primary care, Complexity and Adaptability align with comprehensiveness and cultural competence; Patients Needs and Resources are linked to people-centeredness and comprehensiveness; and External Policy and Incentives are related to intersectoral coordination and community engagement. In Domain 4, the construct Knowledge and Beliefs about the Intervention is related to comprehensiveness, and cultural competency. In Domain 5, the construct Engaging refers to the combination of strategies such as social marketing, education, and role modeling to improve individuals’ involvement in implementing the intervention, which is connected to cultural competency, and community engagement.

On the other hand, constructs that were less frequently investigated in the selected studies are also linked to the guiding principles of primary care. In Domain 1 (Intervention Characteristics), the construct of Cost, which includes both the costs of the intervention and its implementation, relates to comprehensiveness and continuity, as affordability may hamper these principles. In Domain 2 (Outer Setting), the construct of Cosmopolitanism, which examines networking with external organizations, is linked to intersectoral coordination, community engagement, and comprehensiveness. The constructs explored in the selected studies were less aligned with continuity and intersectoral coordination.

Discussion

This systematic review demonstrates that the Consolidated Framework for Implementation Research (CFIR) has been increasingly applied in implementation research conducted in primary care settings over the past decade. Our findings indicate a rapid growth in CFIR use, particularly in the last five years, reflecting a broader expansion of implementation science within primary care. However, CFIR applications in this setting were predominantly post-implementation, frequently focused on healthcare workers’ perspectives, and often lacked explicit justification for construct selection. Additionally, while many CFIR constructs aligned with core characteristics of primary care, others—particularly those related to continuity of care, intersectoral coordination, and community engagement—were less frequently examined.

Consistent with both reviews, we observed a predominant use of the CFIR in post-implementation phases and a limited reporting of the rationale guiding construct selection. However, our review highlights important distinctions specific to primary care. Unlike hospital-based settings, primary care implementation research is inherently embedded within communities, characterized by longitudinal relationships, coordination across levels of care, and responsiveness to social, cultural, and economic determinants of health. Despite these features, many studies included in our review did not fully leverage CFIR constructs that could capture these dimensions, nor did they consistently integrate patient and community perspectives.

These similarities and differences suggest that challenges identified in earlier CFIR reviews—such as selective and retrospective application—persist in primary care, but their implications may be more consequential given the broader scope and relational nature of primary care practice.

Most studies were conducted in the HICs which helps explain why the most frequently examined health topics were related to leading causes of death in HICs, such as noncommunicable diseases and cancer [122]. Most studies focused on healthcare workers’ perceptions of factors that influenced implementation; 31% also included patients’ perspectives. Increasing the inclusion of multiple stakeholders, particularly by listening to and analyzing the perspectives of patients and communities, would better align with the principles of implementation research and the primary care guiding principle of community engagement [4, 123]. In this direction, the updated CFIR, launched in 2022, introduced new constructs and subconstructs. Within the Inner Setting domain, subconstructs such as recipient-centeredness and human equality-centeredness were added under the construct of Culture. Also, in the Outer Setting domain, new constructs were included to capture the influence of Local Attitudes, such as sociocultural beliefs, on the implementation of the innovation. This is particularly relevant for innovations that require support from community entities [13]. However, the updated CFIR did not highlight the need to include patients and communities as participants in studies applying the framework. We believe that patient and community participation should be priorized in implementation research. Their roles within health systems can contribute not only to identify barriers and facilitators to implementation but also to co-constructing strategies to overcome these barriers. Encouraging community participation aligns with the guinding principles of primary care, particularly people-centeredness, and community engagement.

In line with the updated CFIR guidance, it is important to clarify how we interpreted the role of patients and community members in the studies included in this review. Although the 2022 CFIR revision and the 2025 user guide introduce constructs that encourage attention to the needs, experiences, and engagement of innovation recipients, they do not mandate their participation in all implementation studies. Accordingly, in our analysis, we considered how primary care studies addressed, or overlooked, the involvement of these groups, and how this reflects the flexible, context-dependent nature of the CFIR.

Although the five CFIR domains were assessed in most studies, the rationale for selecting specific constructs was rarely reported. Regarding the alignment between the guiding principles of primary care and the CFIR constructs examined in the included studies, we found that some constructs directly aligned with continuity and intersectoral coordination were infrequently investigated. Our analysis reinforces the usefulness of the CFIR in identifying determinants of implementation in primary care, helping researchers and decision-makers understand contextual factors that impact implementation.

Research funding agencies, such as the National Institutes of Health, have supported implementation research globally [124]. However, most studies conducted in primary care settings focus on HICs. This is reflected in the health topics most frequently investigated in the selected studies, which focused on the leading causes of death in these countries, such as noncommunicable diseases (hypertension, diabetes) and cancer. In contrast, transmissible diseases that contribute to the leading causes of death in LMICs [122], such as lower respiratory tract infections, tuberculosis, maternal and preterm birth complications, and food insecurity, were less investigated. Our findings also highlight important opportunities to strengthen CFIR applications in LMICs. Given that primary care systems in these settings often operate under resource constraints, greater emphasis on constructs such as Available Resources, Cosmopolitanism, and Engaging may help capture critical contextual determinants. Tailoring CFIR-based studies to explicitly consider sociocultural dynamics, community partnerships, and health system fragmentation could improve the applicability and usefulness of implementation research in LMICs. In line with recent decolonial critiques of implementation science [125], adapting CFIR use in LMICs may also require greater attention to locally grounded knowledge, recognition of power asymmetries, and engagement with community-led priorities, elements that can strengthen the framework’s global relevance and equity orientation. Initiatives and research policies aimed at increasing primary care research in the Global South could contribute to enhanced implementation research in regions with higher inequality rates and healthcare systems that face limited resources.

Regarding its application, the CFIR was predominantly used in the post-implementation phase. That pattern was also observed in two previous systematic reviews that evaluated CFIR applications. The use of the CFIR in the pre-implementation phase could provide a better understanding of the multicomponent complexity of implementation, identify factors that may hinder adoption and sustainability, and help reshape the design before the implementation process begins [126, 127]. This is particularly important in primary care settings, where healthcare actions extend beyond the healthcare unit. Primary care teams act more closely to the population contexts than their hospital counterparts. They must consider communities’ cultural, political, social, and economic characteristics, determinants of health, and vulnerable groups [128]. Including a pre-implementation investigation seems valuable, better aligns with primary care guiding principles, and can potentially impact implementation outcomes. Kowalski et al. [23] highlighted the importance of pre-implementation evaluation in improving the effectiveness of implementing a clinical program. It was conceptualized as a formative evaluation that identifies key points in the implementation context, providing elements for redesigning the intervention and increasing the chances of successful implementation.

We found that the CFIR was rarely used along with other implementation research frameworks. However, given the multicomponent complexity of healthcare implementation in primary care settings, the association with other frameworks—such as RE-AIM, PRISM, or Proctor´s framework—should be considered. This may improve understanding of the multi-level nature of implementation [129] and provide a more comprehensive appreciation of the factors influencing implementation success.

The predominant use of the CFIR in post-implementation phases across the included studies may reflect a tendency toward effectiveness-implementation hybrid studies, in which implementation frameworks are applied primarily for exploratory, retrospective evaluation [130]. While this approach provides valuable insights into implementation barriers and facilitators after an intervention has been tested, it limits the potential for using the CFIR as a formative guide to adapt strategies during earlier phases. There is a compelling rationale for shifting toward effectiveness-implementation hybrid studies type 2 and type 3 —or full implementation trials—especially in primary care settings. These studies support the concurrent testing of clinical and implementation strategies, enabling real-time responsiveness to contextual dynamics. Employing the CFIR earlier in the implementation process could enhance the adaptability, sustainability, and scalability of interventions, particularly in the complex, community-embedded nature of primary care.

We observed that many studies did not provide a rationale for selecting specific CFIR domains for data collection, which is consistent with findings from previous systematic reviews [11, 14]. In addition to the study's specific aims, the healthcare setting in which the implementation research is conducted should be considered when selecting constructs. The primary care context involves working within primary care centers, engaging with communities, coordinating care, and integrating with secondary and tertiary levels. These key elements contribute to a multifactorial complexity that should inform the rationale for choosing CFIR constructs.

Our findings indicate that several CFIR domains could be refined to better reflect the realities of primary care. Within the Inner Setting, constructs that capture practice fragmentation, variable staffing models, and coordination demands would strengthen contextual specificity. In the Outer Setting, greater granularity regarding policy, financing, and regulatory pressures—central drivers of primary care implementation—would enhance applicability. Additionally, incorporating features of interdisciplinary teamwork, task-shifting, and relational continuity within the Characteristics of Individuals domain may improve the framework’s sensitivity to primary care workflows.

These observations align with our evidence synthesis: many primary care studies in primary care continue to apply the CFIR in a limited manner, echoing prior concerns about construct selection and depth of application. At the same time, we also observed efforts to tailor the use of the CFIR to the dynamics of primary care, such as leveraging constructs related to communication, contextual adaptation, and patient-centeredness. These patterns support our hypothesis that the determinants influencing implementation in primary care are not only distinct but may also require a re-prioritization of CFIR domains to better reflect the realities of this setting. By focusing on the contextual alignment between CFIR applications and primary care characteristics, our review provides actionable insights for researchers and practitioners aiming to tailor implementation strategies in these settings. Using the CFIR without accounting for these primary care guiding principles, we risk overlooking key contextual determinants that are critical to implementation success in these settings. We hope this contribution will inform more precise, context-sensitive use of the CFIR and support future research and policy efforts to strengthen implementation in primary care globally.

Strengths and limitations

Previous systematic reviews have evaluated the use of the CFIR, but none have specifically focused on its application in primary care settings. Kirk et al. [11] assessed its global use, primarily in high-income countries (HICs), while Means et al. [14] explored its application in LMICs. Both reviews highlighted the CFIR use in secondary and tertiary care, particularly in hospital settings. Our study offers three key innovations: a detailed characterization of how the CFIR has been applied in primary care settings, an analysis of whether these applications align with the guiding principles of primary care, and proposals for refining future implementation research tailored to primary care.

Our systematic review has limitations that should be considered when interpreting the results. The rapid evolution of primary care implementation research may render the findings outdated over time. Additionally, the lack of information in the studies regarding the rationale or criteria for selecting CFIR constructs may have impacted the analysis of their relationship with the guiding principles of primary care. Another limitation is that we did not systematically assess the extent to which included studies adhered strictly to the CFIR's intended role as a determinant framework, versus adapting it for other purposes such as process guidance or innovation development. Future research could examine how such adaptations influence the conceptual clarity and practical utility of the CFIR in implementation science. Finally, since 83% of the selected studies were conducted in HICs, the applicability of the results to primary care in LMICs may be limited.

Conclusion

By explicitly focusing on primary care settings, this review extends previous systematic reviews of CFIR use and highlights both persistent challenges and emerging opportunities for more context-sensitive application. While CFIR use in primary care is increasing, its application often remains limited in scope and depth, with inconsistent alignment to the core principles that define this level of care.

Our findings emphasize the need for more deliberate, theory-driven, and contextually grounded use of the CFIR in primary care implementation research. Without explicit attention to primary care characteristics, there is a risk of overlooking key determinants critical to implementation success. We hope that this review will inform more precise application of the CFIR and support future research and policy efforts aimed at strengthening implementation in primary care globally.

Implications for practice and future research

The predominance of post-implementation CFIR applications observed in this review underscores a missed opportunity to use the framework as a formative tool during earlier phases of implementation. Applying the CFIR during pre-implementation could facilitate a deeper understanding of contextual complexity, anticipate barriers to adoption and sustainability, and inform intervention design before large-scale rollout. This is particularly relevant in primary care, where healthcare actions extend beyond clinical encounters and are closely intertwined with community contexts.

Our findings directly inform several recommendations. The limited inclusion of patient and community perspectives supports the need to enhance community participation throughout the research process. The frequent absence of explicit justification for construct selection reinforces the importance of transparently linking CFIR constructs to both study aims and primary care characteristics. Additionally, the underuse of pre-implementation approaches highlights the value of longitudinal and formative evaluations to improve implementation outcomes.

We recommend the following refinements for applying the CFIR in primary care settings: (1) enhancing community participation from study design to interpretation and development of practice recommendations; (2) reporting the rationale for selecting CFIR constructs, integrating with primary care principles, such as centeredness in communities, social accountability, and reducing inequities; (3) increasing CFIR application during the pre-implementation phase, supporting longitudinal studies to track formative implementation processes; and (4) implementing research policies and programs committed to offering funding for the development of implementation research in primary care settings in LMICs.

Abbreviations

CFIR

Consolidated Framework for Implementation Research

LMICs

Low middle-income countries

HICs

High-income countries

WHO

World Health Organization

HCWs

Healthcare workers

Authors’ contributions

ATCS and PCS designed the manuscript. AC, KA, LA, AB, and LU conducted the searches, reviewed articles for inclusion, and extracted data. ATCS, LU, PCS, GK, RL, and ACC analyzed and interpreted data. ATCS, LYTU, AC, and KA drafted the manuscript. PSC, ACC, GK, and RL critically revised the manuscript. PCS supervised data analysis and interpretation. All authors have read and given final approval of the version of the manuscript submitted for publication.

Funding

This manuscript was not funded.

Data availability

Data and materials are available at osf.io/4yq2f.

Declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.