Development of a Comprehensive Measure of Organizational Readiness (Motivation × Capacity) For Implementation: A Study Protocol

BACKGROUND: Organizational readiness is important for the implementation of evidence-based interventions. Currently, there is a critical need for a comprehensive, valid, reliable, and pragmatic measure of organizational readiness that can be used throughout the implementation process. This study aims to develop a readiness measure that can be used to support implementation in two critical public health settings: federally qualified health centers (FQHCs) and schools. The measure is informed by the Interactive Systems Framework for Dissemination and Implementation and R = MC heuristic (readiness = motivation × innovation-specific capacity × general capacity). The study aims are to adapt and further develop the readiness measure in FQHCs implementing evidence-based interventions for colorectal cancer screening, to test the validity and reliability of the developed readiness measure in FQHCs, and to adapt and assess the usability and validity of the readiness measure in schools implementing a nutrition-based program. METHODS: For aim 1, we will conduct a series of qualitative interviews to adapt the readiness measure for use in FQHCs. We will then distribute the readiness measure to a developmental sample of 100 health center sites (up to 10 staff members per site). We will use a multilevel factor analysis approach to refine the readiness measure. For aim 2, we will distribute the measure to a different sample of 100 health center sites. We will use multilevel confirmatory factor analysis models to examine the structural validity. We will also conduct tests for scale reliability, test-retest reliability, and inter-rater reliability. For aim 3, we will use a qualitative approach to adapt the measure for use in schools and conduct reliability and validity tests similar to what is described in aim 2. DISCUSSION: This study will rigorously develop a readiness measure that will be applicable across two settings: FQHCs and schools. Information gained from the readiness measure can inform planning and implementation efforts by identifying priority areas. These priority areas can inform the selection and tailoring of support strategies that can be used throughout the implementation process to further improve implementation efforts and, in turn, program effectiveness

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

From study plans to capacity building: a journey towards health equity in cancer survivorship

This article highlights the importance of pausing and reflecting on one's motivation, capacity, and positionality when engaging in health equity research and encourages researchers to engage in critical self-reflection and contribute to the ongoing dialogue on the ethical conduct of health equity-focused cancer research. In response to the urgent need to address health disparities and improve health equity in cancer survivorship care, the Cancer Prevention and Control Research Network (CPCRN) Survivorship workgroup discussed developing a study focused on understanding how racism impacts patient engagement in cancer survivorship care. However, during the study's development, the workgroup recognized limitations in research team composition and infrastructure. The workgroup engaged in critical self-reflections, individually and collectively, leading to the halting of the research study. Consequently, they redirected their efforts towards strengthening the necessary infrastructure for conducting such research, including diverse investigator representation and equitable partnerships with cancer survivors. The description of this process, along with suggestions for reflection, may be helpful and informative to other researchers and research networks seeking to center marginalized voices and work in partnership to address healthcare and health equity.Open access articleThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected] copyright statement above the abstract on the PDF file is incorrect; this article is not US government work with no copyright protection