At a 2022 national innovation summit, participants determined that it was essential to embed innovation into the fabric of healthcare.1 According to the National Research Council, academic-practice partnerships (APPs) are a primary catalyst for innovation and healthcare development and are associated with increased capacity for research, funding, quality of care, and innovations.2-4 In 2023, the American Association of Colleges of Nursing–American Organization for Nursing Leadership (AACN-AONL)5 task force unveiled a shared vision for integrating nursing education and practice that includes: 1) leading innovation to maximize nursing impact; 2) transforming healthcare; and 3) achieving health equity. This renewed focus presents a timely opportunity to develop a scale that measures the success of APP innovation. Such a scale would allow AACN-AONL and other constituents to leverage their capacity, increase their potential, and limit the constraints for healthcare progress.

Historically, the AACN-AONL task force defined an APP as a mechanism for advancing nursing practice to improve the health of the public6 but noted some challenges such as uneven time commitment, cultural and value differences, infrastructure differences, multiplicity of partner demands, stereotyping, lack of trust, administrative differences, legal differences, and lack of transparency.7,8 In addition, in a 2011 study, academic leaders did not perceive the need to leverage financial and human resources to engage clinical nurses in research. Findings highlighted differences among executives across academia and practice. Academic-practice leader incongruence may be related to the absence of a theoretical basis to drive APPs for healthcare.9-11 There is evidence of 2 fundamental gaps: 1) a lack of consensus in understanding that knowledge generation and innovation are critical to managing scarce healthcare resources in today's world; and 2) the need to prioritize knowledge generation and innovation as much as fiscal management to solve problems and thrive.12 Academics and practitioners alike clearly need a common language and shared model for understanding and planning APPs for innovativeness, which is defined as a social process for the generation, acceptance, and implementation of new processes, products, or services for the 1st time in an organizational setting.13

Published articles on academic-practice initiatives highlight innovative research interventions and the translation of research findings into practice.3-7 These articles fail to establish a transparent theoretically based strategic mechanism to generate innovations based on a uniform need for progress. The current institutional review board (IRB)–approved study developed and validated a comprehensive scale, Innovativeness Across Academia and Practice for Healthcare Progress Scale (IA-APHPS), to provide a diagnostic tool to facilitate innovative processes and outputs across APPs. This scale was developed based on the only published theory of innovations10,14 that transcends nursing practice and academia, providing a framework for measuring the progress that nurse faculty and practicing nurses aim to achieve in their roles or circumstances.

Previous Research

Engagement in APPs has been atheoretical and without consistent evidence about which areas of progress (functional, social, and emotional) are needed to advance healthcare excellence or nursing practice.15-17 Functional progress is defined as taking initiative toward resolving issues by improving the experience of a job (i.e., technology). Social progress involves taking initiative toward resolving issues by fulfilling the social needs of others (i.e., work experience). Finally, emotional progress entails taking the initiative toward resolving feelings for self and others (i.e., counseling techniques).16

Three types of APPs associated with differing levels of engagement have been identified as key to partnerships.18,19 These types include: 1) a moderately structured APP that may be ad hoc in response to a specific need; 2) a structured APP with a shared vision that builds on strength, demonstrates collaboration and cooperation, and supports change for the sake of improvement; and 3) a highly structured APP characterized by a formal contract, bylaws, and financial arrangements between partners. Academic-practice partnerships are based on guiding principles, supported by reports, anecdotes, white papers, small samples, and descriptive studies.5,11,20-22 Research on APPs suggested that they improve patient care quality and safety, foster innovation and research collaboration, and support student learning experiences.23 In a 2013 systematic review, authors noted that formal evaluations of APPs are limited and of poor quality.18 Fifty-five percent of the APPs reported favorable outcomes, yet these reports were not supported by data.18 Swedish researchers recognized that quantifying the impact of APPs was unclear; thus, they used a logic model to examine the impact on a primary care and academic setting.12 Findings revealed that impact does not occur in isolation. There needs to be a clear integration for measuring impact such as identifying components of the integration. The components should be understandable and relevant.11 For example, a practice-focused APP merged the strengths and resources of 2 faith-based community organizations by emphasizing quality care and patient outcomes, resulting in a reduction in hospital-acquired infections.3

The theoretical framework for innovations across academia and practice posits that nurse faculty and practicing nurses are making progress in their jobs or circumstances that transcend across practice and academia in 3 challenging areas: 1) patient access and care across the life span; 2) nursing education and research; and 3) the health system environment.10 Nurses in academia and practice recognize that opportunities include process needs, knowledge gaps, incongruences, and the need for changes in perceptions.24 Identifying opportunities results in solutions or innovations that are interconnected because faculty and practice nurses share common ground in their core disciplines.5,10 The innovations across academia and practice theoretical framework used here offered foundational insight into the dynamic and synergistic nature of APPs for innovations through the development of the IA-APHPS.

Development of the IA-APHPS

The initial measure was curated and modified using published existing scales, adapted to the healthcare context, and new items were drafted based on existing literature or theories.10,12,16,25-46 The measure of partnership innovation occurred over 2 phases. Phase I consisted of drafting an initial bank of survey items and subjecting them to a content validation process, whereas phase II consisted of a pilot test of the validated measure. In this section, authors highlight the process underlying phase I before discussing the analytical strategy surrounding the modification of the measures in phase II.

Phase I: Content Validation

The 1st phase of scale development followed Colquitt and colleagues'47 procedures to generate survey items and assess the definitional correspondence and definitional distinctiveness of a scale. We drew from Joseph and colleagues'10 theoretical framework for healthcare innovations across academia-practice to develop an initial bank of construct definitions and Likert-type items (108 items in 8 conceptually distinct domains). To ensure that the items of the scale accurately reflected their underlying constructs, researchers submitted the initial survey bank for review by 16 subject matter experts (SMEs). These SMEs were selected based on their national expertise and publications in nursing leadership and/or nursing innovation. Each SME rated the clarity and accuracy of a subset of the scale using 1-to-5 ratings and open-ended responses (3-4 SMEs per subdomain). Researchers pooled the ratings and comments of SMEs, assessed the degree to which scale items represented their relevant constructs (ie, definitional correspondence), and aligned items with focal constructs other than orbiting constructs (ie, definitional distinctiveness). Individual members of the research team completed an initial round of revisions according to their expertise in the 8 conceptual domains of our scale. The research team made final revisions to construct definitions during 2 in-person meetings. Researchers revised and removed scale items to better capture concepts about healthcare partnership innovation. An example that details how a particular item in the scale transformed through this content validation process is provided in Figure 1. These processes concluded phase I of the scale development, which resulted in 119 items across 7 domains (long-term healthcare impact, short-term healthcare impact, leadership, culture, collaboration, infrastructure resources, and inquiry and application) and 28 unique subdomains. Final domains, subdomains, and definitions are presented in Tables 1 and 2. Subdomains were revised and finalized in phase II hereinafter.

Figure 1:

An example of phase 1 content validation procedures.

Table 1 - 7 Dimensions of the IA-APHPS Dimension Definition  1. Long-term healthcare impact (distal impact) The long-term positive or negative impact of the adoption of healthcare innovations sustained through projects, policies, and research  2. Short-term healthcare impact (proximal impact) The short-term positive or negative impact of innovation as observed through daily processes  3. Leadership The degree of influence that inspires and promotes performance excellence  4. Culture The facilitators and barriers to identifying opportunities for change, developing ideas, applying and adapting technology, and taking initiative to make progress in a job  5. Collaboration Mutual respect, trust, cooperation, and understanding of roles while working together toward shared goals  6. Infrastructure resources The structural support and resources within the work environment  7. Inquiry and application The rigorous procedures used to advance knowledge across the care continuum Subdomain and Items From the Inquiry and Application Domain (Example)  Translational methods
  1. I understand innovation is a multiphase
process with tailored strategies that fit
settings.
  2. I understand the process steps for EBP.
  3. Research/EBP processes are critical for
applying innovations in practice. Implementation science
1. I include values of diverse stakeholders in innovation design.
2. I design innovations to improve HC equity.
3. I include the preferences of diverse stakeholders in innovation
implementation.

Abbreviations: EBP, evidence-based practice; HC, healthcare.

Table 2 - Subdimensions of the IA-APHPS Domain Subdomain Definition  1. Long-term healthcare impact (distal impact) 1. Education and research Perception that the organization is known for its expertise and leadership in teaching, specialization, consulting, and conduct of research 2. Patient care Perception that patient care delivery has improved through the adoption of healthcare research and innovation 3. Outputs Healthcare quality outcomes (eg, safety, cost effectiveness, technology improvement, nursing staffing) that have resulted from implementation of health innovations  2. Short-term healthcare impact (proximal impact) 4. Work technology innovation Solutions that are creative but not officially approved when using technology 5. Satisfaction Feeling of contentment and fulfillment with friends, family, work, and self during role transition  3. Leadership (adapted from Baldridge excellence framework)38 6. Top-level administrators The degree to which my top administrators influence performance excellence 7. Manager The degree to which my manager influences performance excellence while at work 8. Strategy The degree to which my organization pursues performance excellence through its strategic actions 9. Customer relations The degree to which my organization pursues performance excellence by collaborating with and responding to its customers (patients or students)  4. Culture 10. Opportunity identification A situation that sparked ideas that results in possible sources of innovation 11. Patient care interventions Actions by caregivers performed to treat illnesses and improve symptoms, comfort, and overall health of patients and families 12. Adaptability Ease in one's ability to adjust to a change, new role, or new situation with no measurable distress 13. Functional opportunity for advancement Taking an initiative toward resolving issues by improving the actual experience of a job, process, technology, or equipment 14. Social opportunity for advancement Taking an initiative toward resolving issues by fulfilling social needs for others 15. Emotional opportunity for advancement Taking an initiative toward emotional growth and resolving feelings for self and others  5. Collaboration 16. Interpersonally just treatment Personal perception of being accepted and treated with fairness, respect, and dignity (adapted from Colquitt45) 17. Ability-based trustworthiness Partner possesses skills and abilities to meet my and my unit's expectations for our joint work in the areas of interest (adapted from Mayer and Davis37) 18. Shared understanding Perception that team members have a joint understanding of the situation and are working toward the same goals 19. Being trusted with information Perception that information is being shared that make the other party vulnerable if it were misused (adapted from Williams43) 20. Psychological safety Perception that environment is safe for interpersonal risk taking (adapted from Edmondson29)  6. Infrastructure resources 21. Resource availability and systems improvement The perception that the organization proactively identifies and removes barriers to provide resources, support, and a positive work culture for employees 22. Design infrastructure Resources to build systems for desired outcomes 23. Research and EBP infrastructure Systems, processes, programs, resources, and related services that are used by clinicians, leaders, researchers, and faculty to conduct research and EBP  7. Inquiry and application 24. Translational methods Multidirectional and interprofessional approaches to apply research and other sources of evidence to improve the health of individuals, communities, and populations 25. Implementation science The study of methods used to promote the adoption, integration, and sustainability of innovations (ie, evidence-based practices, interventions, and policies) to improve the quality in routine, real-world healthcare and public health settings

Abbreviation: EBP, evidence-based practice.

Phase II: Analytical Strategy

In phase II, the content-validated scale for partnership innovativeness was pilot-tested in REDCap on a sample of practicing clinicians and academic nurses as well as nursing leaders and academic leaders. Researchers analyzed each scale within the measures for internal consistency (reliability) with Cronbach's α and performed a confirmatory factor analysis (CFA) to determine whether the structure of the scale corresponded to the team's conceptual understanding of healthcare partnership innovativeness and to establish discriminant validity.48

To determine whether the values observed in the scales corresponded to the latent constructs of the conceptual domains and subdomains, researchers examined 2 key indices of fit in the CFA: the comparative fit index (CFI) and the root-mean-square error of approximation (RMSEA). Although there are no universally agreed-upon cutoff values for these indices, values above or near 0.90 generally indicate a good fit on the CFI, and values below or near 0.08 indicate an adequate model fit on the RMSEA.49 Accordingly, we used these indices to first examine the extent the individual indicators loaded onto their respective subdomains and the correlations among the subdomains. Researchers then assessed how well the 7 domains and their subdomains in the scale fit with the overall conceptual model. The statistical software used in these analyses was SAS.50 Researchers discuss the results of these statistical analyses in the next section.

Sample

The sample frame consisted of nurses in academia and practice, specifically, faculty and academic leaders in academia, and clinicians and leadership in practice at large medical centers. The 5 participating sites were in 4 divisions of the United States (South Atlantic, East North Central, West North Central, and Mountain). Over 1000 nurses participated in this IRB-approved study. The primary IRB was the University of Iowa. On the basis of individualized organizational policies, participating sites either use Iowa's IRB or applied to their own IRB. For the analyses, researchers had complete data for up to 826 participants because some entries had missing data points (Table 3).

Region Site Size (No. Beds) Frequency, % (N = 826) South Atlantic 700+ 29.42 East North Central 1000+ 23 West North Central 800+ 35.35 South Atlantic 2700+ 5.45 Mountain 700+ 6.54 Other 0.24 Settings Healthcare system (61.62%), college/university (4.12%), both academic center and college (6.17%), academic medical center (27.97%), and medical school (0.12%) Length of time as a nurse 0-5 y (21.43%), 6-10 y (18.04%), 11-15 y (16.46%), 16-20 y (11.14%), 21-25 y (6.66%), 26 y or more (26.15%), and prefer not to answer (0.12%) Length of time in current workplace 0-5 y (47.09%), 6-10 y (20.82%), 11-15 y (10.77%), 16-20 y (8.23%), 21-25 y (4.84%) and 26 y or more (8.23%) Highest degree AA/AS (10.65%), BSN (60.05%), nonnursing BS (0.36%), MSN (17.31%), nonnursing master's (1.82%), DNP (4.48%), PhD (4%), other (0.61%), and prefer not to answer (0.73%) Total participants N = 826

Abbreviations: AA/AS, associate degree; DNP, Doctor of Nursing Practice.

Data Analysis and Results

Researchers split the sample in two and used the 1st portion, sample 1, to refine the items and subdomains within the model and the 2nd sample, sample 2, to confirm the refined model.

Sample 1

For each domain, researchers ran fully disaggregated CFAs on the subdomains of each domain separately, allowing the subdomains to correlate freely. Table 4 shows the CFA model fit results for the fully disaggregated CFAs for the subdomains in each domain. Table 4 lists each domain and the fit statistics for analyses related to the subdomains within that domain for each sample. Sample 1 appears in the 1st row of each domain with the superscript a. All domains for sample 1 had CFIs above 0.90. The RMSEA score ranged from 0.06 to 0.09 except for 1 score of 0.1 for domain 7, inquiry and application.

Domain χ 2 (df) CFI RMSEA  1. Long-term healthcare impact (distal impact) 249.39 (62)a; n = 318 0.94a 0.09a 242.27 (62)b; n = 372 0.95b 0.09b 332.10 (62)c; n = 690 0.90c 0.09c  2. Short-term healthcare impact (proximal impact) 117.28 (26)a; n = 338 0.91a 0.09a 131.18 (26)b; n = 392 0.89b 0.10b 205.81 (26)c; n = 730 0.95c 0.07c  3. Leadership 299.19 (84)a; n = 316 0.96a 0.09a 319.47 (84)b; n = 363 0.96b 0.09b 470.29 (84)c; n = 679 0.96c 0.08c  4. Culture 712.88 (237)a; n = 274 0.92a 0.08a 766.07 (237)b; n = 321 0.91b 0.07b 1036.24 (237)c; n = 595 0.93c 0.07c  5. Collaboration 438.87 (142)a; n = 194 0.95a 0.08a 336.76 (142)b; n = 217 0.97b 0.06b 503.34 (142)c; n = 411 0.97c 0.06c  6. Infrastructure resources 149.59 (51)a; n = 169 0.96a 0.09a 152.48 (51)b; n = 220 0.96b 0.08b 187.53 (51)c; n = 389 0.97c 0.07c  7. Inquiry and application 24.82 (8)a; n = 183 0.98a 0.10a 16.16 (8)b; n = 222 0.99b 0.06b 31.88 (8)c; n = 405 0.98c 0.08c

aSample 1.

bSample 2.

cCombined samples.

To arrive at the final model, researchers analyzed the fit indices and modifications of the initial CFAs. Researchers removed individual scale items that cross-loaded with other subscales. In 3 cases, where items had a great deal of overlap, 2 subdomain scales were combined, or subdomains were replaced. For the short-term healthcare outcomes domain, the items from the workaround technology and work innovation scales were combined to form a new work technology innovation subdomain. For the infrastructure resources domain, items from the resource availability and health systems improvement subdimension scales were combined to form a single resources/healthcare systems subdomain. For the collaboration scale, researchers replaced 2 subscales, boundary spanning and support, which overlapped with another subdomain, psychological safety.

To further support the discriminant validity of the model, researchers conducted pairwise CFAs for all domain pairs. Researchers confirmed that items and subdomains did not cross-load across domains for each pair of domains. Researchers did not run a fully disaggregated CFA across all 7 domains because the number of parameters to be estimated was too large for the sample size. However, in the combined sample section hereinafter, researchers report the results of a partially aggregated CFA with all 7 domains to further confirm the overall fit of the model and discriminant validity of the domains and subdomains.

Sample 2 and the Combined Sample

Researchers replicated the initial fully disaggregated models with sample 2. The CFA results for sample 2 appear in Table 4 with the superscript b in the 2nd row of each domain. All domains with sample 2 had CFIs above 0.90 and RMSEA scores that ranged from 0.06 to 0.09 with the exception of domain 2, the short-term healthcare impact domain, which had a CFI of 0.89 and an RMSEA of 0.1, slightly outside the optimal range for these fit indices.

With the combined sample, denoted in Table 4 by the superscript c, all domains had CFIs above 0.90 and RMSEA scores that ranged from 0.06 to 0.09, reflecting adequate to excellent fit.

The Combined Sample Only

In the last test of model fit and discriminant validity, researchers analyzed a partially aggregated model with all 7 domains, using the averages of the subdomains as indicators. As shown in Figure 2, the model fits well with a CFI of 0.92 and an RMSEA of 0.06 (n = 477). The final subdomains are listed in Figure 2, and definitions of each subdomain in the final IA-APHPS scale appear in Table 2. Descriptive statistics and correlations among the domains in the final scale are provided in Table 5.

Figure 2:

Seven-factor CFA.

Table 5 - Descriptive Statistics and Correlations Among Domains Mean SD 1 2 3 4 5 6 7 1. Short-term healthcare impact (proximal impact) 5.831 0.701 0.779 2. Long-term healthcare impact (distal impact) 5.31 0.984 0.536 0.925 3. Leadership 5.279 1.074 0.490 0.722 0.941 4. Culture 5.768 0.641 0.656 0.571 0.563 0.938 5. Collaboration 5.292 0.927 0.528 0.606 0.695 0.605 0.965 6. Infrastructure resources 4.937 1.159 0.467 0.638 0.766 0.501 0.66 0.926 7. Inquiry and application 5.636 0.883 0.407 0.449 0.333 0.554 0.436 0.369 0.866

Highest N = 826 and lowest N = 473. All correlations are significant at P < 0.0001. Standardized Cronbach's α is included in bold along the diagonal.

Findings

This is the 1st theoretically based validated measure developed to facilitate innovations across academia and practice. In the earlier theoretical framework for healthcare innovations across academia and practice, it was clear that both nurses in practice and academia are trying to achieve progress or advancement functionally, socially, or emotionally by innovating amid both relational and structural constraints.10 The IA-APHPS allows nurses to be intentional with strategy. Nursing leadership can use this model of components to enable innovations that may not be limited by constraints. These findings demonstrate that strategizing by entities can focus solely on the relational components of the model, the structural components of the model, or both components based on how the entity scored. Hereinafter are the definitions and reliability scores for the 3 components (relational, structural, and impact). Both the relational and structural components should be used as levers for partnering. The 3 relational components consist of leadership (α = 0.941), culture (α = 0.938), and collaboration (α = 0.965). Definitions are provided in Table 1. The 2 structural components consist of infrastructure and resources (α = 0.926), and inquiry and application (α = 0.866). Definitions are provided in Table 1. Finally, the impact components are the 2 ways to measure progress or advancement. The 2 impact scales are long-term healthcare impact (α = 0.925) and short-term healthcare impact (α = 0.779).

Discussion

The IA-APHPS illustrates that enabling innovations in an organization requires innovativeness, which is primarily a social process for the generation, acceptance, and implementation of new processes, products, or services for the 1st time in an organizational setting.13 This is the 1st theoretically based validated measure designed to facilitate innovations across academia, practice, or industry, or collectively. The IA-APHPS has fulfilled 2 needs identified from the literature. First is an international call for the identification of components in the understanding and examination of an APP. The relational and structural components are understandable and relevant and can guide entities in recognizing strengths and opportunities when leveraging APPs.11 Second, a validated scale consisting of 98 items was developed to assess innovativeness across practice and academia. This new validated scale may now strategically leverage this initiative, increase this capacity, improve the potential, and reduce partnering constraints to ensure advancement for healthcare excellence using either or both the relational and structural components of the IA-APHPS.10,14

Implications

The implications are multifaceted for practice-academia, research, and policy. Nurses now have new knowledge to ensure the capacity and potential for innovation. Most importantly, nurses can identify the potential constraints and work to remove them. The following are recommendations for practice-academia, research, and policy:

Practice-academia teams should measure each entity's innovativeness using the IA-APHPS. Be intentional about leveraging the partnership. If the opportunity lies in components that are relational and/or structural, then leveraging that component is necessary. This may require skill development by each entity to leverage and maximize the partnership. Future research should shorten the scale and define the cutoff scores for low, medium, or high relational and/or structural components. Interventions and longitudinal studies may be conducted to assess outcome measures and changes in innovativeness over time. Innovativeness levels between different geographical regions, types of healthcare organizations, or educational institutions can be compared to identify factors contributing to variations. Researchers can develop predictive models using innovativeness scores to forecast healthcare outcomes or the success of collaborative initiatives. Policy stakeholders should promote the importance of APPs and innovation in broader healthcare policy frameworks. The IA-APHPS should be promoted as a fundamental tool for not only evaluating but also fortifying the capacity of APPs to facilitate nurse-driven innovation to contribute to health disparities (patient care), education and research, outputs, work technology innovation, satisfaction, and overall health outcomes. Furthermore, funding should be allocated for the implementation of the IA-APHPS. Conclusion

In this study, researchers provide psychometric results of a new measure called the IA-APHPS. The final scale consists of 98 items. This is the 1st theoretically based validated measure designed to facilitate innovations. The intended audience for completing the IA-APHPS is any entity that employs nurses (academia, healthcare organizations, or industry). This scale can be completed individually or collaboratively to leverage each other's assets to innovate.

Acknowledgments

We thank all current and past Innovation Scholarly Interest Group members at the University of Iowa College of Nursing, Dr Sue Moorhead, Dr Barbara Rakel from the Office of Nursing Research, all subject matter experts, and participating sites, and in loving memory of Mary Madelina Joseph.

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