Abstract
Introduction
To (1) summarize the current landscape of implementation science (IS) training in North America, (2) describe participant characteristics from The Center for Implementation (TCI), one of the largest IS training programs, and (3) identify opportunities to inform future capacity-building efforts.
Methods
We conducted a structured environmental scan of North American IS training programs; online surveys were then distributed to all identified programs to collect information on program characteristics and target audience. We then performed descriptive and qualitative analyses of the survey results and de-identified demographic and participant response data from TCI course participants.
Results
Fifty-seven IS training programs were identified across North America. Most programs (59.6%) were located in the United States and were primarily graduate level (54.4%), certificate programs (36.8%), and fellowships or post-doctoral opportunities (29.8% each). Nearly half (47.4%) enrolled ≤10 participants per cohort, and approximately 10% enrolled more than 50 participants. Nearly all programs admitted academic audiences (94.7%), with fewer targeting participants from public health (57.9%) or healthcare providers (43.9%). TCI participants (N = 1,632 for demographic data; N = 436 for qualitative analysis) were a diverse audience representing multiple sectors, most frequently healthcare (26.9%), IS (17.6%), public health (17.0%), and education (15.6%). Organizational affiliations included nonprofit organizations (22.7%), healthcare institutions (18.1%), higher education (17.7%), and government (15.9%). Through qualitative analysis of TCI participant introductions, we identified four major themes reflecting participants' professional contexts and motivations for IS training: (1) system roles, (2) current implementation activities, (3) learning motivations, and (4) application of IS concepts to their work.
Discussion
Current IS trainings in North America are primarily focused on a research audience. Expanding IS implementation training and education to a broader audience, in particular implementation support practitioners who are not engaged in research, but who seek to apply IS to solve problems, is warranted.
Keywords: capacity building, evidence-based practice, implementation practice, implementation science, training programs
1. Introduction
Over the past two decades, implementation science (IS) has advanced substantially. Supported by a growing body of theoretical frameworks, methodologies, and studies, IS aims to improve the effectiveness, pace, and sustainability of implementation efforts (1–3). With this progress has come a rising interest in the field, reflected in increased investment in IS research by funding agencies and the development of training infrastructure within academic institutions (2, 4). Yet, translating research into equitable and sustained practice remains challenging. This underscores the need for capacity-building among researchers and professionals.
Capacity building in IS seeks to strengthen the knowledge and skills needed to design and apply the most rigorous methods of IS in research and also implementation practice (5, 6). This can occur in various educational and training structures such as degree and non-degree programs at academic institutions, certificate programs, training institutes, and workshops or conferences (1, 5, 7). Historically, most IS capacity-building efforts have been geared towards academic audiences (i.e., researchers, faculty, and trainees), such as the Implementation Research Institute (IRI), The Society for Implementation Research Collaboration, and The Dissemination and Implementation Science Training Institute (8–11). These individuals desire to apply implementation theories, models, frameworks, and methods in controlled research projects (4, 8, 9, 12). However, the true benefits of IS are relevant for a much broader range of actors, including health system leaders, policymakers, clinicians, community organizations, those supporting implementation (e.g., through intermediaries or within their organizations), and implementation support practitioners (13–15). It remains unclear whether the current landscape of IS training adequately serves the diverse professionals responsible for implementing evidence in practice. This distinction matters because the competencies required for implementation research aren't always the same as those needed to execute implementation (13, 16). Implementation researchers aim to generate and test theories, models, and methods; implementation support practitioners apply these approaches in real-world settings. As a result, a knowledge-practice gap may be emerging. The field designed to close the evidence-practice gap may itself be producing knowledge that practitioners cannot readily access or apply (17). This creates a broader tension for capacity-building initiatives, which must be responsive to the needs of both groups, as well as individuals who occupy hybrid roles spanning research and practice.
Previous reviews have documented capacity-building programs in IS. However, little is known about how programs differ in target audience (including those outside of academia or research), scope, and structure. These factors may ultimately shape who has access to formal training and how effectively participants can apply the knowledge and skills they acquire (4). Thus, a comprehensive and updated review of existing IS training opportunities is needed to describe what is currently offered, identify areas for improvement, and align future capacity-building strategies to diverse professional settings.
Among capacity-building programs, The Center for Implementation (TCI) is one of the largest providers of IS training (18), reaching a global audience of both researchers and implementation support practitioners through online courses and a certificate program. To date, there is limited understanding of non-academic capacity-building efforts such as TCI, though their participant sample is often similar or larger than academic trainings. As one of the largest training programs in North America, TCI serves as a compelling case example for examining characteristics and needs of a cross-sector implementation support workforce. To contribute to the knowledge base of IS capacity-building programs, we aim to (1) summarize the current landscape of training programs in North America and their key characteristics; (2) describe TCI's participant audience; and, (3) identify opportunities to inform future capacity-building efforts, particularly for audiences beyond academia who remain underrepresented but essential to advancing the impact of IS.
2. Methods
This study employed a three-part approach: (1) a structured environmental scan of current IS training programs in North America, (2) a survey administered to identified programs, and (3) descriptive and qualitative analyses of internal participant data from The Center for Implementation.
2.1. Identification of IS training programs
A structured environmental scan was conducted to identify all current IS training programs in North America. Searches were carried out between May and September 2025 using multiple sources including Stanford University Library databases, Google, Google Scholar, and institutional websites.
Search terms combined core IS terminology with synonymous or related concepts. Primary terms included “Implementation Science/Practice” and “Dissemination and Implementation” paired with descriptors including program, course, certificate, training, degree, and workshop. Degree variations (e.g., undergraduate, master's, PhD, graduate, certificate) and regional or state-specific modifiers were also tested. Supplementary terms (e.g., webinar, training program, modules, seminar, implementation practice, tools) were used to ensure broad capture. Search results were screened for relevance to IS education, training, and capacity building. Inclusion criteria were: located in North America, explicit IS focus, and recurring training opportunities. Exclusion criteria were: non-English and employer-specific programs, one-time events, and consultation or mentorship only opportunities. A total of N = 54 programs were initially identified. Each website was reviewed independently by two research team members to confirm eligibility and identify duplicates. Any discrepancies were resolved through discussion and consensus before finalizing inclusion.
2.2. Survey of identified programs
Following initial verification, an online survey was distributed via email to the primary contacts for all eligible programs. The survey collected structured information on: program country and institutional affiliation, training type and level, admission frequency, duration, cohort size, cost, intended audience, course content, and topic specificity. Course content categories were developed a priori through a structured synthesis of existing IS competency frameworks (15, 19–23), yielding eleven major topics spanning both implementation research and implementation support practice. Programs were asked to indicate which topics their training addressed.
Responses were reviewed for completeness and accuracy. Four discontinued programs were removed and seven new programs identified through follow-up survey responses were added, resulting in a final sample of N = 57 programs.
2.3. Quantitative analysis
Descriptive analyses were conducted to summarize program- and participant-level characteristics. Program-level variables were summarized as frequencies and percentages. Internal participant data provided by TCI were analyzed to describe participant characteristics overall and across five courses. Variables included demographic information, professional sector, organizational affiliation, geographic location, and years of experience.
2.4. Qualitative analysis
To further examine participant engagement in IS training, a rapid qualitative analysis was conducted on de-identified course introductions submitted by TCI participants in the most popular course during the introductory module. Participant introductions were an optional course activity, and therefore not all StrategEase participants submitted an introduction. The qualitative analysis included the subset of participants who provided an introduction. Consistent with established rapid approaches, this method was selected to generate timely insights from participant data while maintaining methodological rigor (24, 25).
We first developed a summary template to guide data synthesis and ensure systematic documentation. Two research team members independently reviewed a sample of introductions to develop an initial codebook grounded in pre-defined content themes. A combined inductive and deductive strategy was used as consistent with existing rapid qualitative methods (24, 25). Two members of the research team independently coded the data. Coding discrepancies were resolved through discussion and consensus until agreement was reached. Through iterative discussion, code definitions were refined to ensure consistency and reliability. The finalized codebook was systematically applied to the full dataset. Coders compiled structured summaries after independent review and calibrated their approaches through jointly reviewing and comparing previous versions to enhance rigor and dependability (24, 25). Emergent themes were then analyzed across participants, using matrices organized around key domains from the summary template. Representative quotes were selected to illustrate each theme.
2.5. Quality assurance and ethical considerations
Throughout all phases, multiple steps were taken to enhance rigor and consistency. Screening and coding procedures were double-checked by independent reviewers with discrepancies resolved through consensus meetings. Survey data were cross validated against publicly available program information. Qualitative coding reliability was verified through periodic discussions, and all analytic decisions were documented to ensure reproducibility.
This study was determined to be exempt by Stanford University's institutional review board, as it involved the analysis of publicly available program information and de-identified internal data from The Center for Implementation. Although de-identified program data were provided directly by The Center for Implementation, all analyses were conducted independently by the research team to reduce potential bias and strengthen internal validity. No personally identifiable information was collected or analyzed.
3. Results
3.1. Environmental scan and survey
A total of 57 IS training programs were identified and surveyed across North America (Table 1). The majority of North American programs (59.6%) were located in the United States and delivered in-person only; 35.1% of programs were offered as hybrid or remote. Most were offered at the graduate level (54.4%), followed by professional development workshops (42.1%), certificate programs (36.8%), and fellowships or post-doctoral opportunities (29.8% each). Nearly half (47.4%) of trainings admitted small cohorts of ≤10 participants per cycle, with approximately 10% enrolling more than 50 participants. While most training opportunities were $0 (42.1%)—no cost to participants, due to external funding from federal agencies such as the National Institutes of Health—over one third had tuition costs exceeding $7,000 USD (35.1%). Among programs with tuition, mean cost was $23,650 ($31,598); median cost was $8,900. Regarding the intended audience, nearly all programs (94.7%) offered admission to those in academia (i.e., faculty, researchers, students, and postdoctoral scholars). Several programs also offered or targeted training to those in public health (57.9%) and healthcare providers (43.9%); fewer programs welcomed other audiences from the education sector (24%), government (24%), and policy (26%). Training content covered a wide range of IS topics. Nearly all programs addressed implementation strategies (96.5%), frameworks and theories (94.7%), and foundations of IS (93.1%). Fewer programs incorporated capacity building and team composition (50.9%), economic or policy considerations (47.4%), or de-implementation (40.4%). Approximately one-third of training (31.6%) were topic- or sector-specific, while most remained general in focus (68.4%). Additional detail from survey responses can be found in Supplementary Table S1.
Table 1.
Characteristics of current implementation science trainings in North America.
| Characteristic | Category | N (Total = 57) | % |
|---|---|---|---|
| Country | Canada | 3 | 5.3 |
| Flexible/Hybrid | 10 | 17.5 | |
| Remote (online) | 10 | 17.5 | |
| United States | 34 | 59.6 | |
| Training level | Certificate program | 21 | 36.8 |
| Fellowship | 17 | 29.8 | |
| Graduate level | 31 | 54.4 | |
| Post-doctoral | 17 | 29.8 | |
| Professional development workshop/training | 24 | 42.1 | |
| Undergraduate level | 6 | 10.5 | |
| Other (webinar series, reading course, etc.) | 5 | 8.8 | |
| Admission frequency | Annually | 38 | 66.7 |
| Biannually (twice per year) | 4 | 7.0 | |
| Monthly | 1 | 1.8 | |
| Multiple offerings per year | 4 | 7.0 | |
| Rolling enrollment | 10 | 17.5 | |
| Cost | Funded | 24 | 42.1 |
| <$3,000 | 8 | 14.0 | |
| $3,000—$7,000 | 5 | 8.8 | |
| $7,001—$25,000 | 11 | 19.3 | |
| >$25,000 | 9 | 15.8 | |
| Cohort size | 1–10 | 27 | 47.4 |
| 11–20 | 15 | 26.3 | |
| 21–50 | 9 | 15.8 | |
| 51–100 | 4 | 7.0 | |
| 100+ | 2 | 3.5 | |
| Duration | 1–7 days | 12 | 21.1 |
| A few months | 4 | 7.0 | |
| Six months—1 year | 10 | 17.5 | |
| More than 1 year | 30 | 52.6 | |
| Self-paced | 1 | 1.8 | |
| Audience | Academia (faculty, researchers, students, postdocs) | 54 | 94.7 |
| Community organizations | 20 | 35.1 | |
| Education sector professionals | 14 | 24.6 | |
| Funding agencies/Government institutions | 14 | 24.6 | |
| Healthcare institutions | 17 | 29.8 | |
| Healthcare providers | 25 | 43.9 | |
| Human services professionals | 20 | 35.1 | |
| Policy makers | 15 | 26.3 | |
| Public health professionals | 33 | 57.9 | |
| Other | 6 | 10.5 | |
| Competencies | Adaptation and Fidelity | 44 | 77.2 |
| Assessing Context and Readiness | 48 | 84.2 | |
| Capacity Building and Team Composition | 29 | 50.9 | |
| De-implementation | 23 | 40.4 | |
| Economic and Policy Considerations | 27 | 47.4 | |
| Foundations of Implementation Science | 53 | 93.0 | |
| Frameworks and Theories | 54 | 94.7 | |
| Implementation Strategies | 55 | 96.5 | |
| Measurement and Outcomes | 50 | 87.7 | |
| Stakeholder and Community Engagement | 52 | 91.2 | |
| Study Design and Methods | 49 | 86.0 | |
| Other | 5 | 8.8 | |
| Topic specificity | No (generalist/agnostic) | 39 | 68.4 |
| Yes (topic/sector specific) | 18 | 31.6 |
3.2. TCI participant data
Participant data from TCI revealed a diverse and international audience (Table 2). Participants represented a broad range of age groups, mostly aged 35–44 years (36.2%) and 45–54 years (27.3%). Across all courses, 85.6% of participants identified as women and 67.1% as White. Participants most commonly worked in healthcare (26.9%), IS (17.6%), public health (17%), and education (15.6%), with many reporting multiple disciplinary affiliations (56.4%). Representation included diverse organizations, including nonprofit organizations (22.7%), healthcare institutions (18.1%), higher education (17.7%), government agencies (15.9%), and research organizations (17.4%), with substantial overlap across sectors (36.6%). Professional experience varied widely, spanning early- to late-career stages (1–5 years: 28.1%; 6–10 years: 19.8%; 11–15 years: 18.8%; 16+ years: 28.1%).
Table 2.
Participant characteristics of the center for implementation's implementation science certificate program, overall and by course.
| N (%) | All courses (N = 1,632) | StrategEase: The HOW of creating sustainable change (N = 652) | Implementation, spread, and scale (N = 324) | Cultivating trust and navigating power (N = 275) | Embracing complexity (N = 341) | Igniting change from the middle (N = 40) |
|---|---|---|---|---|---|---|
| Age group | ||||||
| 18–24 | 31 (2%) | 17 (2.8%) | 7 (2.3%) | 3 (1.1%) | 2 (0.6%) | 2 (5.6%) |
| 25–34 | 329 (21.3%) | 146 (24%) | 73 (23.5%) | 34 (12.7%) | 63 (19.5%) | 13 (36.1%) |
| 35–44 | 560 (36.2%) | 233 (38.3%) | 109 (35.2%) | 110 (41%) | 96 (29.7%) | 12 (33.3%) |
| 45–54 | 422 (27.3%) | 144 (23.6%) | 91 (29.4%) | 83 (31%) | 98 (30.3%) | 6 (16.7%) |
| 55–64 | 164 (10.6%) | 56 (9.2%) | 20 (6.5%) | 34 (12.7%) | 51 (15.8%) | 3 (8.3%) |
| 65–75 | 31 (2%) | 9 (1.5%) | 5 (1.6%) | 4 (1.5%) | 13 (4%) | NA |
| Over 75 | 1 (0.1%) | 1 (0.2%) | NA | NA | NA | NA |
| Prefer not to disclose | 8 (0.5%) | 3 (0.5%) | 5 (1.6%) | NA | NA | NA |
| Country | ||||||
| Australia | 134 (8.6%) | 63 (10.1%) | 19 (6.1%) | 19 (7.2%) | 30 (9.1%) | 3 (7.9%) |
| Canada | 653 (41.8%) | 302 (48.4%) | 138 (44.7%) | 77 (29.3%) | 124 (37.7%) | 12 (31.6%) |
| United States | 535 (34.2%) | 202 (32.4%) | 103 (33.3%) | 114 (43.3%) | 100 (30.4%) | 16 (42.1%) |
| Other | 241 (15.4%) | 57 (9.1%) | 49 (15.9%) | 53 (20.2%) | 75 (22.8%) | 7 (18.4%) |
| English as First Language | 1,149 (77.8%) | 457 (79.2%) | 230 (76.4%) | 193 (77.2%) | 244 (77.7%) | 25 (71.4%) |
| Gender | ||||||
| Man | 200 (12.5%) | 83 (13.1%) | 37 (11.6%) | 37 (13.6%) | 38 (11.3%) | 5 (12.5%) |
| Non-binary | 16 (1.0%) | 3 (0.5%) | 6 (1.9%) | 3 (1.1%) | 3 (0.9%) | 1 (2.5%) |
| Woman | 1,370 (85.6%) | 540 (85.4%) | 270 (84.9%) | 231 (84.6%) | 293 (87.5%) | 34 (85.0%) |
| Prefer not to disclose | 14 (0.9%) | 6 (1.0%) | 5 (1.6%) | 2 (0.7%) | 1 (0.3%) | 0 (0.0%) |
| Race/Ethnicity | ||||||
| Asian | 163 (10.4%) | 66 (10.6%) | 37 (11.9%) | 19 (7.1%) | 38 (11.8%) | 3 (7.5%) |
| Black | 141 (9%) | 45 (7.2%) | 27 (8.7%) | 41 (15.3%) | 25 (7.7%) | 3 (7.5%) |
| Hispanic or Latinx | 86 (5.5%) | 24 (3.8%) | 21 (6.7%) | 21 (7.8%) | 13 (4%) | 7 (17.5%) |
| Indigenous, First Nations, or Metis | 9 (0.6%) | 2 (0.3%) | NA | 3 (1.1%) | 4 (1.2%) | NA |
| Middle Eastern | 27 (1.7%) | 11 (1.8%) | 8 (2.6%) | 3 (1.1%) | 5 (1.5%) | NA |
| More than one race | 46 (2.9%) | 20 (3.2%) | 5 (1.6%) | 7 (2.6%) | 12 (3.7%) | 2 (5%) |
| Prefer not to disclose | 44 (2.8%) | 15 (2.4%) | 14 (4.5%) | 6 (2.2%) | 8 (2.5%) | 1 (2.5%) |
| White | 1,051 (67.1%) | 441 (70.7%) | 200 (64.1%) | 168 (62.7%) | 218 (67.5%) | 24 (60%) |
| Field | ||||||
| Education | 503 (15.6%) | 168 (13.2%) | 96 (14.3%) | 128 (23.3%) | 98 (15.1%) | 13 (14.4%) |
| Healthcare | 868 (26.9%) | 380 (30%) | 180 (26.9%) | 116 (21.1%) | 176 (27.1%) | 16 (17.8%) |
| Implementation Science | 568 (17.6%) | 201 (15.9%) | 126 (18.8%) | 96 (17.5%) | 128 (19.7%) | 17 (18.9%) |
| Justice | 117 (3.6%) | 60 (4.7%) | 25 (3.7%) | 14 (2.6%) | 15 (2.3%) | 3 (3.3%) |
| Mental Health | 354 (11%) | 153 (12.1%) | 75 (11.2%) | 56 (10.2%) | 60 (9.2%) | 10 (11.1%) |
| Public Health | 547 (17%) | 199 (15.7%) | 109 (16.3%) | 102 (18.6%) | 121 (18.6%) | 16 (17.8%) |
| Social Services | 267 (8.3%) | 106 (8.4%) | 58 (8.7%) | 37 (6.7%) | 51 (7.9%) | 15 (16.7%) |
| Prefer not to disclose | 2 (0.1%) | 1 (0.1%) | 1 (0.1%) | NA | NA | NA |
| More than one (double counted) | 892 (56.4%) | 346 (54.5%) | 197 (61.9%) | 148 (54.6%) | 176 (55%) | 25 (65.8%) |
| Organization | ||||||
| Community Organization | 144 (6%) | 58 (6.2%) | 24 (4.9%) | 28 (7.1%) | 31 (6%) | 3 (5.3%) |
| For Profit Organization | 52 (2.2%) | 14 (1.5%) | 12 (2.5%) | 9 (2.3%) | 13 (2.5%) | 4 (7%) |
| Government | 381 (15.9%) | 162 (17.2%) | 72 (14.8%) | 66 (16.8%) | 75 (14.5%) | 6 (10.5%) |
| Healthcare Institution | 434 (18.1%) | 178 (18.9%) | 92 (18.9%) | 60 (15.3%) | 100 (19.3%) | 4 (7%) |
| Higher Education | 425 (17.7%) | 152 (16.1%) | 93 (19.1%) | 75 (19.1%) | 95 (18.4%) | 10 (17.5%) |
| Nonprofit Organization | 544 (22.7%) | 217 (23%) | 112 (23%) | 100 (25.5%) | 102 (19.7%) | 13 (22.8%) |
| Research Organization | 416 (17.4%) | 162 (17.2%) | 82 (16.8%) | 54 (13.8%) | 101 (19.5%) | 17 (29.8%) |
| More than one (double counted) | 573 (36.6%) | 225 (36.1%) | 116 (36.6%) | 89 (33.7%) | 129 (39.9%) | 14 (35.9%) |
| Years of experience | ||||||
| <1 year | 82 (5.2%) | 43 (6.9%) | 15 (4.8%) | 10 (3.8%) | 13 (3.9%) | 1 (2.6%) |
| 1–5 years | 439 (28.1%) | 184 (29.7%) | 103 (33.2%) | 63 (23.8%) | 73 (22.1%) | 16 (41%) |
| 6–10 years | 310 (19.8%) | 123 (19.8%) | 52 (16.8%) | 46 (17.4%) | 78 (23.6%) | 11 (28.2%) |
| 11–15 years | 294 (18.8%) | 122 (19.7%) | 61 (19.7%) | 53 (20%) | 53 (16.1%) | 5 (12.8%) |
| 16+ years | 439 (28.1%) | 148 (23.9%) | 79 (25.5%) | 93 (35.1%) | 113 (34.2%) | 6 (15.4%) |
Qualitative analysis of de-identified participant data from TCI's StrategEase course (N = 436), the first course in the Level 1 Implementation Support Specialist Certificate Program revealed four major themes: (1) System roles, (2) Current implementation activities, (3) Learning motivations, and (4) Applying IS course content (Table 3). Learners represented a wide range of professional system roles, reflecting the cross-sector and interdisciplinary reach of IS training audiences and their varying implementation-related activities. Participants' learning motivations included deepening foundational knowledge and acquiring practical tools to strengthen implementation and sustainment processes, while others emphasized professional growth or bridging research and practice. Learners also described applying course content to push organizational change, scale programs and services, improve patient-centered care and access, and strengthen implementation capacity.
Table 3.
System roles, implementation activities, and learning motivations of participants in an implementation science certificate program.
| Themes | Sub-themes | Example quotes |
|---|---|---|
| System role | Researchers (implementation and topic-specific) Clinicians & healthcare providers Implementation support practitioners Organizational leaders Policy & government Human services and education professionals Evaluators Students & trainees Hybrid roles |
“Hi, — from University of —, in —, UK. I am a clinical exercise physiologist and full time researcher with an interest in implementation of evidence-based strength training guidelines/programmes for public health.” (244) “In my role as policy analyst, I provide training and technical assistance…implementing reentry programs…many of these programs are new and complex, which is where implementation science could really help guide the work and ensure that programs are human-centered and can achieve successful outcomes.” (71) “I am currently an evaluator at the—, a non-profit think tank, where I use my research skills mixed with implementation science to evaluate programs, products, and services aimed to reduce the risk for biased policing against Black and Brown communities and to redesign public safety.” (327) |
| Current implementation activities | Capacity building, training, and facilitation Coordination and partnership development Collaboration and networking Project evaluation Implementation leadership Knowledge translation and dissemination Organizational change Embedded research Implementation research |
“My role is to work with researchers, community partners and the government to embed knowledge translation and implementation into a federally funded initiative examining how to better support seniors and promote healthy aging.” (417) “My role includes patient- and healthcare provider oriented research to inform organizational strategy related to pain management, leading development of clinical standards and practice supports and related implementation supports, and education of providers, patients, and healthcare learners.” (283) |
| Learning motivations | IS foundations Applied implementation Professional identity and career development Equity and community, patient, and end-user centered implementation Bridging research and practice |
“I am excited to see how these strategies and processes will equip me and others to become better ‘Implementation Practitioners’ and support our systems on a larger scale.” (61) “Our work involves conducting needs assessments/root cause analyses in schools to determine a pathway for change. Implementation science is necessary to positively affect systems change in schools to assure students with disabilities are afforded the opportunities and access to a free and appropriate education.” (120) “I'm an aspiring implementation scientist that hopes to one day bridge research and practice!” (372) |
| Applying implementation science course content | Implementation within organizations Scaling programs, practices, and services Patient-centered care and access Organizational and system capacity building Organizational change |
“Our organization is expanding its implementation support nationally. This certification will assist us in our efforts to build implementation capacity and identify, contextualize, and improve evidence-based implementation strategies in various settings.” (384) “Part of my role within the—within this initiative is to build capacity and support local government areas to use systems thinking processes to engage with their community (especially children and young people) to understand and map a specific complex problem systems (including factors and relationships that impacts on that complex problem) to then develop community led action that could influence and/or create change within that system. The reason I'm taking this course is to find ways to strengthen the implementation of community led action/initiatives over time and long term.” (366) |
4. Conclusions
This environmental scan shows that, although training opportunities in IS have increased across North America, they remain largely focused on an academic research audience and on researchers. This may further widen the gap between IS research and practice (17). As a result, IS training programs may not be adequately reaching the wider audience seeking to implement and sustain evidence-based practices across various systems. While numerous programs exist, most are tailored for researchers and graduate students, offering a few spots for non-academic participants. Some programs have fairly strict inclusion criteria, specifically having a “thing” backed by extensive evidence; for many people working to address significant challenges in the system, they are often looking to implementation guidelines, policies, and new practices in topic areas that do not have available evidence-based programs. There is a limited outreach to the sectors and roles where implementation challenges are most significant, such as healthcare delivery, community and social services, and government organizations.
One of the findings that emerged from the environmental scan was the overall reach of current IS training programs. Almost half of the identified programs (47.4%) enroll relatively small cohorts of fewer than 10 participants per cohort. Excluding the two programs that train more than 100 participants annually, the combined maximum estimated enrollment across the remaining 55 programs is approximately 1,420 individuals per year—even when assuming the highest possible enrollment within each cohort's reported range. Considering the number of implementation support practitioners, organizational leaders, intermediaries, and system actors responsible for implementing, spreading, and sustaining evidence-based programs and practices across healthcare, public health, education, social services, and other human service sectors, this level of reach remains limited. The mismatch between the supply of capacity building initiatives and implementation practice needs and demand underscores a critical gap: while training offerings have grown, their overall scale and accessibility remain insufficient to meaningfully support system-wide implementation efforts. Internationally, IS capacity building efforts may also offer useful lessons for expanding training beyond academic audiences. Notably, Viglione and colleagues identified a lack of programs in low- and middle-income countries, underscoring that access gaps are not unique to non-academic audiences in North America but reflect a broader global equity challenge in IS capacity building (3).
Data from The Center for Implementation's courses further reinforces the diversity of professionals across sectors who are interested in building IS capacity. These professionals hold a variety of roles, including organizational leadership, system navigation, clinical service delivery, evaluation, and applied research. These participants described engaging in a wide spectrum of implementation activities and expressed clear motivation to apply IS approaches to ongoing change efforts. These findings highlight that the interest in IS extends beyond academia and that professionals in service delivery, community organizations, and government are seeking out IS training that they can apply to their work and contexts.
Taken together, these findings highlight opportunities to enhance IS capacity building efforts. As the goal of IS is to expand the use of evidence in practice, if we want to scale evidence-based programs, policies, and practices, there is a need for scalable IS training programs to equip a workforce with competencies to implement evidence (26–29). Related to competencies, there is a need to expand on which competencies are covered in IS trainings to have more training programs address competencies related to building implementation infrastructure, navigating system-level complexity, de-implementation, and supporting collaborations across organizations in the implementation system (27). The training programs were only asked about IS topics and were not asked to identify which implementation support competencies were being addressed. More work is needed to understand which implementation support competencies are being addressed (20, 28) and likely to build more training programs that will address those competencies.
There is also a growing interest among non-IS researchers, i.e., implementation support practitioners to learn how to apply IS to their work. These are academics who are not interested in being implementation researchers but applying IS to their work—similar to how implementation support practitioners might apply IS. There is a need to unpack the similarities and differences between this audience and aspiring implementation researchers and implementation support practitioners.
Finally, the field may benefit from greater intentional alignment between academic and applied practice-oriented training. It is not necessarily in the best interest of all participants for everyone to receive the same training, since there are different competencies for implementation research and practice, but there are opportunities for elements of shared curricula, co-designed resources, and practice-based collaboration and mentorship. Another question is whether practice-focused education continues to be delivered through these same institutions; or, to truly meet people's needs alternate training spaces may need to be carved out. For example, alternative programs can focus on competencies for synthesis and translation, support, and implementation delivery functions. Currently, trainings are often too specific or must be broad to bridge gaps, with little consideration of the role people play in implementation.
This study includes several limitations. Survey data were self-reported and may not fully capture the programs or evolution of those programs. We focused exclusively on English training opportunities in North America; therefore, the findings do not reflect what is happening globally and there is the possibility that non-English programs (e.g., French programs in Canada and Spanish programs in Mexico) were excluded; the number of excluded programs was not reported. Programs were categorized by host country; however, this may not reflect the geographic reach of participants, particularly for virtual and hybrid programs. Only currently available publicly posted training programs were surveyed; programs that have been terminated, one-time programs, and employer-specific training programs were excluded. Qualitative data underwent rapid coding and were based on written reflections rather than interviews. Although this study did not directly assess workforce demand or competency needs, the data highlight opportunities for future research to examine how well existing trainings meets the needs of those in implementation practice. Whereas many training programs focus on the needs of the implementation research community, there may be a large audience of people who are less interested in research and more interested in solving public health or health care implementation challenges.
The field has made substantial progress in building IS training programs, yet critical gaps remain. There is a need to broaden access by developing and investing in training models that can prepare a larger and more diverse implementation workforce, expanding the predominant models to reach implementation support practitioners, intermediaries, and applied researchers across sectors. At the same time, training efforts should differentiate and intentionally teach both implementation research competencies and implementation support/practice competencies (for facilitating change in practice), ensuring that learning is relevant and actionable for varied roles. Future research should explore validated approaches to identify implementation support practitioners' status as a functional role rather than a job title, since existing demographic categorizations (e.g., professional sector) may not fully capture individuals performing this work. Strengthening access, aligning training content with role-specific competencies, and scaling delivery models will be essential for supporting the widespread and sustained use of evidence in real-world systems and advancing the core goals of IS.
Funding Statement
The author(s) declared that financial support was received for this work and/or its publication. This work was supported by the National Institute on Drug Abuse (NIDA), National Institutes of Health (NIH) under Award Number P50DA054072 [Center for Dissemination & Implementation At Stanford (C-DIAS); PI: McGovern].
Footnotes
Edited by: Alexandra Ziemann, University of Bath, United Kingdom
Reviewed by: Samantha M. Harden, Virginia Tech, United States
Samiksha Garse, DY Patil Deemed to be University, India
Lars Almquist, University of Washington, United States
Data availability statement
The data analyzed in this study is subject to the following licenses/restrictions: Available upon request. Requests to access these datasets should be directed to hbegna@stanford.edu.
Ethics statement
The studies involving humans were approved by Stanford University Institutional Review Board. The studies were conducted in accordance with the local legislation and institutional requirements. Written informed consent for participation was not required from the participants or the participants’ legal guardians/next of kin in accordance with the national legislation and institutional requirements.
Author contributions
HB: Investigation, Data curation, Methodology, Writing – review & editing, Software, Conceptualization, Visualization, Formal analysis, Validation, Writing – original draft, Project administration. HG: Writing – review & editing, Writing – original draft, Project administration, Methodology, Conceptualization, Investigation, Validation, Supervision. SK: Writing – review & editing, Writing – original draft, Validation, Investigation. MM: Resources, Validation, Conceptualization, Writing – review & editing, Funding acquisition, Supervision, Writing – original draft. JM: Writing – review & editing, Investigation, Resources, Conceptualization, Supervision, Writing – original draft.
Conflict of interest
SK and JM are employed by The Center for Implementation (TCI), the organization whose training program and de-identified participant data are analyzed in this study. To mitigate potential bias, TCI provided de-identified data but did not participate in data coding, formal analysis, or statistical computation; these were conducted exclusively by Stanford-affiliated authors. TCI-affiliated authors’ roles were limited to conceptualization, methodology design, and validation of thematic and competency coding against TCI's programmatic content. We further note that some degree of potential conflict of interest is common, and often unavoidable, in implementation science training research, since the organizations and trainers being studied are frequently among the few parties with the program-level access needed to conduct such studies. We attempted to mitigate this issue while still working together collaboratively by creating a division of labor related to the analysis of the data described below.
The remaining author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Generative AI statement
The author(s) declared that generative AI was not used in the creation of this manuscript.
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Publisher's note
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Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/frhs.2026.1787246/full#supplementary-material
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
The data analyzed in this study is subject to the following licenses/restrictions: Available upon request. Requests to access these datasets should be directed to hbegna@stanford.edu.