Developing a tool to assess institutional readiness to conduct knowledge translation activities in low-and middle-income countries

Academic institutions are well-poised to conduct knowledge translation (KT) but face many barriers, particularly in low resource settings where the need is greatest. Assessing the readiness of academic institutions to conduct KT can help our understanding of these barriers. However, limited research has been conducted to understand inuencers of readiness for KT in low- and middle-income countries (LMICs) and there are no tools for KT readiness assessment designed for LMIC contexts. This paper describes the development of a tool for assessing organizational readiness to conduct KT among academic institutions in LMICs. A review and to with and organized into a A faculty academic as well as of a global knowledge-to-action thematic working group. An exploratory factor analysis was used to identify underlying dimensions of a tool for assessing institutional to conduct This paper describes an empirical study to develop a tool for assessing institutional readiness to conduct KT among academic institutions in LMICs. The paper builds on published tools for readiness assessment developed for HIC settings, and identies constructs that are relevant for assessing readiness of academic institutions to do KT activities in LMICs, and the validity and reliability of an adapted tool measuring these constructs for LMIC contexts.

contextual factors including structural inequities (e.g. historically imbalanced relationships with institutions in the global north and research agendas driven by outside researchers) (6,7) and limited resources (8). We conducted a qualitative study with participants and academic institutions in 6 countries (Bangladesh, DRC, Ethiopia, India, Indonesia, and Nigeria) and found that soft-skills, robust networks, and alignment between institutional priorities and incentives are important factors that shape institutional capacity to conduct KT activities in LMICs (9). While strategies such as trainings, mentorship, and institutional leadership engagement have been developed to address these barriers and speci c factors (10,11), these strategies have been mostly applied to academic institutions in highincome countries (HICs) (12)(13)(14) with limited empirical evidence of their effectiveness in LMIC settings (15).
Strategies for addressing barriers to conducting KT activities by academic institutions require organizational change within those institutions, and the degree to which institutions are ready to change is a key determinant for the success of these strategies (16). Indeed, some institutions are becoming increasingly aware of the importance of organizational changes and the need to implement processes that motivate and enhance change (17,18). This could be motivated by increased demand from policy makers for evidence-based data or funders offering new opportunities that prioritize or require KT activities. Assessing organizational readiness for change enables exploration of facilitators and barriers for individuals and organizations to implement change (19), and has been used to tailor strategies for addressing barriers to KT activities (20). However, most of these readiness assessments for KT have been conducted in HICs, and there is an underuse of validated tools for these assessments (18).
Validated readiness assessment tools include Organizational Readiness for Implementing Change (ORIC) (19), Organizational Readiness to Change Assessment (ORCA) (21) and Texas Christian University, Organizational Readiness to Change (TCU ORC) (22). These tools largely overlap and assess domains such as availability of resources, individual attributes and motivation, and organizational climate. (19,21,22) Psychometric assessments have been conducted on these tools which largely support their reliability and structure (19,23,24). Most tools have been implemented in HICs, largely in health care settings including hospitals, primary care settings, public health agencies, and public sector organizations. Gagnon et al. developed the OR4KT tool, designed to assess organizational readiness for KT in healthcare organizations, but note that its applicability to LMICs is limited (20). Our literature review identi ed only one validated tool, the TCU ORC, that had been used in a LMIC (25). This study utilized the TCU model to examine the organizational functioning of drug treatment facilities in South Africa but makes no mention of adapting the tool to address unique contextual factors. A recent systematic review identi ed 30 tools designed to address organizational readiness for institutions in LMICs but none were validated (26). The lack of readiness assessments to do KT and availability of adapted tools for conducting readiness assessment further contributes to the 'know-do' gap in LMICs.
This paper describes an empirical study to develop a tool for assessing institutional readiness to conduct KT among academic institutions in LMICs. The paper builds on published tools for readiness assessment developed for HIC settings, and identi es constructs that are relevant for assessing readiness of academic institutions to do KT activities in LMICs, and the validity and reliability of an adapted tool measuring these constructs for LMIC contexts.

Results:
Review of the literature revealed 26 organizational readiness tools related to health institutions (including hospitals, clinics, research institutes, etc.) and 30 tools developed speci cally to assess organizational readiness for implementing global health interventions. Both sets of tools had been collated and analyzed in systematic reviews (23,26). Common constructs across these tools included availability of resources, individual attributes and motivation, and organizational climate.
Eighteen stakeholders, both internal and external to the 6 STRIPE academic institution were interviewed. Three crosscutting themes emerged as relevant for readiness to conduct KT in LMICs. This included 1) the complexity of the policy process and necessity of "soft-skills", 2) misalignment between institutional missions and incentives and 3) the role of internal and external networks. The results from the consultation process are described in more detail elsewhere (9). The constructs identi ed from the literature review and the cross-cutting themes from the consultation process were organized into a quantitative tool with 5 domains and 76 items, with new items developed for the themes. A total of 9 additional questions on demographics, facilitators, and barriers to doing KT were added to translate the tool into a survey questionnaire [Appendix 1].
We received 158 responses to the survey across the 6 institutions and TWG. There were 47 respondents who completed 9% or less of the survey, which were subsequently dropped from analysis. These respondents did not cluster by country, age, or gender and appeared random. A total of 111 responses were included in the nal analysis. According to Arrindell and van der Ende, a sample size (N) to items (p) ratio, i.e. N:p ratio of 3:1 is adequate for demonstrating a stable factor structure with an alpha level of 0.05 (32). Hence, our sample size of 111 will be adequate for demonstrating the validity and reliability of a tool with at least 37 items.
The most commonly conducted KT activities included "taught a course on communication, advocacy, stakeholder engagement or KT" (98%, n=109), "conducted a stakeholder meeting" (62%, n=69), and "given a presentation at a scienti c conference" (60%, n=67, ). Individuals who indicated having experience with KT activities were signi cantly more likely to have written a policy brief (p-value 0.0059), conducted a stakeholder meeting (p-value 0.0364), engaged with policy makers to set priorities (p-value 0.0129), and to have given a presentation at a scienti c conference (pvalue 0.0011). Two KT activities also varied signi cantly by country, "authored or co-authored an article in a peer-review journal" (p-value 0.0001), and "given a presentation at a scienti c conference" (p-value 0.0002). Additional descriptive statistics are presented in Table 2.
[Insert Table 2] We ran an exploratory factor analysis on the complete data set (version 0) which included 76 items. This approach yielded 22 factors; both the KMO measure and Barlett's test yielded no value. Constructs covered in these factors included individual motivation, organizational climate, organizational culture, internal resources, individual knowledge and skills, internal and external networks, funding sources, prioritization, and shared ethos for change (change valence). Many factors overlapped, each addressing similar or related constructs. The correlation matrix was reviewed for highly correlated items and those with correlations above 0.5 were dropped; 17 items were removed in this process. Highly correlated items included, "Q1: I am con dent that I can conduct KT activities", "Q3: I feel personally motivated to do KT", and "Q10 I have the skills to conduct KT". Q10 was also heavily correlated with "Q9: I know how to do KT" and "Q11: I have experience conducting KT". Wherever possible, items were kept that did not correlate heavily with other items. These items were also reviewed to ensure they captured the same or similar information.
For the remaining 59 items (version 1.0) we repeated the EFA, followed by an oblique rotation, producing as simple a structure as possible while permitting correlations among factors. This yielded 17 factors, with a KMO measure of 0.4 and a signi cant value for Bartlett's test (p=0.000). The items, their medians, and inter-quartile range (IQR) for version 1.0 can be found in the supplemental material [Appendix 2]. Individual motivation, networks, prioritization, organizational climate, and resources were still captured by these factors. New constructs emerged in this model including institutional peer pressure, the process of conducting KT, and perceived value of KT.
All cross-loading items (i.e. items loading on more than one factor) or with a loading less than 0.5 were then dropped, resulting in the removal of 26 additional items (version 2.0). Table 3 shows each item included in version 2.0, the item's median, and IQR. Some dropped items addressed individual motivation (e.g. Q13: I am passionate about conducting KT" and "KT activities have a positive impact on the health of communities") and institutional climate (e.g. Q32: My institution provides opportunities for professional development in KT"; Q59: I have at least one mentor who conducts KT with the ministry of health"). We re-ran the factor analysis with 31 items, followed by oblique rotation, and identi ed seven factors. The model produced a KMO of 0.52 and a signi cant value for Barlett's test (p=0.000). In this model one item (Q38: If I want to conduct a KT activity, I know where to nd people in my institution who can help) loaded on two factors and was thus removed; in the resulting model, another item loaded to 2 factors and the item was dropped. A nal set of 31 items was retained.
[Insert Table 3] We re-ran the FA with the 31 retained items to identify a ve-factor model. Items were selected based on the strength of the factor loading, uniqueness of the factors, the resulting scree-plot, and cross-loading criteria -and we selected 23 items for the ve factors, with a N:p ratio of 5:1. Figure 1 displays the eigen value plot of the nal factor model. For the nal ve factor model, the average communality (aka uniqueness) of selected items was 0.61. The items (p) per factor (r) ratio (p:r ratio) in the nal model presented in Table 3 is 23:5 with N = 111. According to MacCallum et al., sample sizes of N = 100 and N = 200 are needed to estimate stable factor structure with 95% convergence for p:r ratio of 20:3 and 10:3 respectively if the average communality is low (less than 0.4) (31). If the communality is high (>0.4), as found in our study, a N = 60 is adequate for p:r ratio of 10:3 or 20:3 and will estimate the factor structure with over 99% convergence.
[Insert Figure 1] The ve nal factors that emerged from the analysis were named, 1) Institutional Climate, 2) Organization Change E cacy, 3) Prioritization and Cosmopolitanism, 4) Self-e cacy, and 5) Financial resources, based on their item characteristics and the underlying theories for those items (9). Factor 1 contains 6 items and was labeled 'institutional climate' because each item described aspects of their institution, colleagues, and leadership. Factor 2 contains 5 items and was labeled 'organization change e cacy' to capture organizational members' shared beliefs in their joint abilities. Factor 3, 'prioritization and cosmopolitanism' which was also comprises of 5 items relates to internal and external institutional networks and priorities. Factor 4, comprises of 4 items, captures individual in uencers of 'selfe cacy' including knowledge, skills, and time. Factor 5, ' nancial resources', contains items related to internal and external budgets for KT activities.
Based on data collected during the stakeholder consultation process, these factors demonstrate face and content validity. That is, they appear to measure factors relevant to KT in these settings and represent the complex facets of the constructs These ve factors combined accounted for 69% of the total variance. The factor loadings, ranged from 0.40-0.77, are presented in Table 4; the intercorrelations between the factors ranged from 0.04 to 0.31. The nal model observed a KMO measure of 0.554, and Bartlett's test of sphericity was signi cant (χ2 (465) = 771.570, p < .000). Cronbach's alpha coe cients were 0.78, 0.73, 0.62, 0.68, and 0.52 respectively. Factors 1 and 2 report an alpha above 0.7 which is traditionally acceptable (34,35). Factors 3-5 report a lower alpha which can indicate a need for cautious interpretation. However, given the theoretical underpinnings for each of these factors related both to concepts of individual and institutional readiness, and the small number of items loading to each factor, we concluded the Cronbach's alpha value are still helpful.
[Insert Table 4 ] Discussion: Assessing readiness for individuals and institutions in LMICs is different than in HICs. While some approaches, barriers, and facilitators are shared, low-resource settings have unique contexts that in uence KT processes, the individuals who conduct KT, and the organizations in which they operate. We sought to develop a robust organizational readiness tool designed to re ect these contextual factors particularly for KT activities.
Five factors emerged as relevant for readiness to conduct KT in LMICs: Institutional Climate, Organization Change E cacy, Prioritization and Cosmopolitanism, Self-e cacy, and Financial resources.

Institutional Climate
Institutional climate conceptualizes how individuals perceive and describe their work setting (36). This can include shared perceptions of what is rewarded within an institution and what is expected of people in their roles. In other words, organizational members' shared beliefs and values. The items included in this factor highlight different components of climate including rewards for innovation and creativity, nancial incentives to conduct KT, and provision of trainings. Other items describe concepts of shared values through colleague collaboration, both in practice and availability to collaborate. Some interview participants described this as an enabling environment which could include access to infrastructure but also to networks An enabling environment is where you have everything you need then can be everything. In the ministry you have the internet, these laptops, these digital tools, the supportive director and supportive leadership who has recognized the importance of knowledge translation and is using it to in uence change.-Nigeria External 1 I think having mentors that consider knowledge translation important and that prioritize knowledge translation would be a great motivating factor. Mentors, as well as senior colleagues…but just seeing other people doing it, and seeing how they do it, and potentially how it can be rewarding, I think, is helpful and encouraging individual researchers to also conduct knowledge translation. -Nigeria Internal 2 Organization Change E cacy Weiner rst described this concept as "organizational members' shared beliefs in their collective capabilities to organize and execute the courses of action involved in change implementation" (16). In 2014, Shea et al. assessed the psychometric properties of scales developed to measure change e cacy and other facets of organizational readiness (19). The change e cacy items focused on con dence in abilities to manage change processes, coordinate tasks, maintain momentum, and get investment. Items mapped to this factor from our scale similarly describe con dence and motivation (i.e. investment) and further contextualize this for KT. For example, items Q46, Q51, and Q69 account for the perceived role of external stakeholders, including the ministry of health and members of government, and their views on data and reliance on the institution, to conduct KT. Organization members may commit to change because they value it, have little choice, or feel obliged (16) -it makes sense that the views and actions of external stakeholders could in uence those values, feelings, and obligations, particularly for health-related institutions.

Prioritization and Cosmopolitanism
Public health institutes are increasingly called on to align and adapt their activities to the health priorities of the country. Major international funders including the U.S. Centers for Disease Control have recently released calls for applications that address this issue of priority alignment between country governments and in-country public health organizations. When applied to academic organizations this tool highlighted the role of institutional strategies, missions, and visions, and the importance of conducting KT activities that address national priorities. Integral to this concept is also the extent to which organizational networks can be leveraged to conduct KT. Bloland et al. argue that an important priority for public health institutions is to collaborate with MoHs to improve their abilities to not only accumulate data, but also manage that knowledge and translate it into actionable policies (37). This network component is fundamental to prioritization.
Self-e cacy Readiness has often referred to an individual psychological state of motivation and plays an important role in many theories of behavior change including the Health Belief Model (e.g. self-e cacy) (38), Prochaska's Stages of Change Model (e.g. determination) (39), and Social Cognitive Theory (e.g. capability and self-e cacy) (40). It is unsurprising that individual level factors such as knowledge, training, and roles emerged as an important factor in this analysis. One unique item assessing time to dedicate to KT, included in this factor, was developed through data that emerged from the qualitative interviews. Participants re ected on competing priorities for their time and the need for protected and nancially supported time to conduct KT.
We are researchers, and we are trained to do research and the research also takes up a lot of our time and energy. And knowledge translation itself takes a lot of time and energy and a completely different skill set; it's not a research skill.
-Indonesia Internal 1 Financial resources KT activities, like most research and programmatic work, require the availability of nancial resources. Items loading to this factor indicated that this is true not just for the academic institution (e.g. project budgets), but also at the MoH.
KT models for evidence sharing frequently describe knowledge-push (knowledge supplied by researchers), demandpull (demand for knowledge from policy-makers), and interactive approaches (41)(42)(43). These underscore that KT is a dynamic process, requiring time, input, and resources from knowledge generators, translators, and users. Given the nuances of this process it is interesting but unsurprising that the item, "Conducting KT activities is more of an art than a science", loaded to this factor with a focus on resources.
Components of each of these ve factors have been considered by existing organizational readiness tools. This factor analysis builds on existing measurement tools and further demonstrates the dynamic nature of KT and underscores important contextual considerations for LMIC institutions. This includes the role of internal collaborations (institutional climate) and external networks (prioritization and cosmopolitanism), which often rely on leadership and senior institutional members. Literature has also noted the important role that funding organizations play in supporting KT in LMICs, both through a prioritization and a nancial lens (44).
The nal ve-factor model captures many of the concepts represented in the original model but condenses constructs related to the individual (e.g. motivation and knowledge became self-e cacy) and the organization (e.g. different constructs of climate merged). Prioritization and cosmopolitanism, two constructs that capture internal and external networks, are consolidated in the nal model, demonstrating the relationship between priority setting and networks. This is the rst organizational readiness tool designed for KT activities in LMICs. The model can be used by LMIC institutions to assess their readiness for KT, understand implementation challenges of KT initiatives, explore facilitators and barriers, and provide quantitative measurements for these institutions. The model can also be used to explore determinants of KT in these settings to inform the development of strategies to improve capacity for KT for institutions and their members.

Strengths & Limitations
The development of this tool was rooted in organizational change theory, building on validated tools. We conducted the research across multiple contexts in both Africa and Asia to capture constructs generalizable to institutions based in LMICs. A signi cant limitation of this research is the small sample size. Most well-accepted guidelines for sample size to conduct EFA recommend a ratio of ve to ten subjects per item. However, studies have argued smaller sample sizes may be justi ed when higher correlation coe cients are observed (45). The original tool was lengthy which could have resulted in response fatigue, leading to inaccurate data or incomplete responses. This may have been further exacerbated by selection bias if participants whose responses were dropped were similar in some way (e.g. all of a similar age group, from the same country, or with similar professional foci). Finally, we noted that participants from DRC and Ethiopia were possible outliers, skewing older (and therefore likely more experienced with KT) and more male than respondents from other countries while also having fewer responses than many of the other settings.

Conclusion:
KT is perceived as valuable for bridging the "know-do" gap, bringing evidence-based interventions into policies and practice. The ve factors that emerged from this research as relevant to readiness to conduct KT highlight unique contextual in uencers and opportunities for capacity strengthening in LMICs. The organizational focus of these Page 9/21 factors further points to a need for capacity building that includes but goes beyond individual training. Future research will be conducted to further understand the in uencers of these readiness factors and systematically develop capacity building strategies for academic institutions in LMICs to conduct KT. Availability of data and materials:

Abbreviations
The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.
Competing interests: The authors declare that they have no competing interests Authors' contributions AK conceived the study and paper with guidance from OOA. AK conducted the literature review, stakeholder engagement, tool development, tool distribution, and authored all drafts of the manuscript. AR supported data analysis and contributed to authorship of the initial draft of the paper. OOA provided signi cant oversight throughout the conceptualization, analysis, and write-up process and edited each draft. All authors read and approved the nal manuscript. Funding: The parent-project to this research, STRIPE, is funded by the Bill and Melinda Gates Foundation. The Foundation played no role in funding this sub-set of research or in the design of the study and collection, analysis, and interpretation of the data, and in writing the manuscript.      Values <0.4 are supressed. Factor 1 = "Institutional climate"; Factor 2 = "Organization change e cacy"; Factor 3 = "Prioritization and cosmopolitanism"; Factor 4 = "Self-e cacy"; Factor 5 = "Financial resources Figures Figure 1 displays the eigen value plot of the nal factor model. For the nal ve factor model, the average communality (aka uniqueness) of selected items was 0.61. The items (p) per factor (r) ratio (p:r ratio) in the nal model presented in Table 3 is 23:5 with N = 111. According to MacCallum et al., sample sizes of N = 100 and N = 200 are needed to estimate stable factor structure with 95% convergence for p:r ratio of 20:3 and 10:3 respectively if the average communality is low (less than 0.4) (31). If the communality is high (>0.4), as found in our study, a N = 60 is adequate for p:r ratio of 10:3 or 20:3 and will estimate the factor structure with over 99% convergence.

Supplementary Files
This is a list of supplementary les associated with this preprint. Click to download. AppendixIIv1.0medianiqr.docx AppendixIKTSurvey.docx