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      Accelerating Adoption of Clinical Innovations: Insights on Strategic Leadership Styles for Fostering Dynamic Capabilities by Public Referral Hospitals in Nairobi City County, Kenya

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      African Journal of Empirical Research
      AJER Publishing

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          Abstract

          It is unclear if there is a significant link between dynamic organisational capabilities and the adoption of clinical innovations. Specifically, Dynamic capabilities are the adaptive, innovative, and strategic resources organisations purposely utilise in complex and uncertain situations over a long time. The increasing need to understand how productive dynamic capabilities inform healthcare leaders to elevate decision quality in healthcare particularly to impact the implementation of clinical innovations remains underexplored in public referral hospitals. The research employed a nomothetic, descriptive, and cross-sectional survey design, gathering data from 189 consented healthcare leaders in four public referral hospitals in Nairobi City County. This approach offers advantages over idiographic methods by providing a broader understanding of general laws, patterns, or trends applicable to a large population. The data were collected through a digitized questionnaire. Descriptive and inferential results were presented numerically within text or tables and figures with organisational capabilities showing positive and statistical relationships with clinical innovation adoption (β = 0.2145; CI 95% [0.0512-0.3777], p = 0.01).  In conclusion, organizational capabilities are shown as the most statistically significant and positive factors in the adoption of clinical innovations by public referral hospitals in Nairobi City County. This has implications for healthcare managers, theory, policy, and practice on where to focus and invest more and to aid them in choosing the most efficacious strategic leadership style. Managerial recommendation entails sustainably adopting clinical innovations through effective organizational capability development and maintenance. Additionally, policy changes can streamline innovation implementation, suggesting areas for further research.

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          Artificial intelligence in healthcare

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            Mapping the challenges of Artificial Intelligence in the public sector: Evidence from public healthcare

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              Violating the normality assumption may be the lesser of two evils

              When data are not normally distributed, researchers are often uncertain whether it is legitimate to use tests that assume Gaussian errors, or whether one has to either model a more specific error structure or use randomization techniques. Here we use Monte Carlo simulations to explore the pros and cons of fitting Gaussian models to non-normal data in terms of risk of type I error, power and utility for parameter estimation. We find that Gaussian models are robust to non-normality over a wide range of conditions, meaning that p values remain fairly reliable except for data with influential outliers judged at strict alpha levels. Gaussian models also performed well in terms of power across all simulated scenarios. Parameter estimates were mostly unbiased and precise except if sample sizes were small or the distribution of the predictor was highly skewed. Transformation of data before analysis is often advisable and visual inspection for outliers and heteroscedasticity is important for assessment. In strong contrast, some non-Gaussian models and randomization techniques bear a range of risks that are often insufficiently known. High rates of false-positive conclusions can arise for instance when overdispersion in count data is not controlled appropriately or when randomization procedures ignore existing non-independencies in the data. Hence, newly developed statistical methods not only bring new opportunities, but they can also pose new threats to reliability. We argue that violating the normality assumption bears risks that are limited and manageable, while several more sophisticated approaches are relatively error prone and particularly difficult to check during peer review. Scientists and reviewers who are not fully aware of the risks might benefit from preferentially trusting Gaussian mixed models in which random effects account for non-independencies in the data. Supplementary Information The online version contains supplementary material available at 10.3758/s13428-021-01587-5.
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                Author and article information

                Contributors
                Journal
                African Journal of Empirical Research
                AJERNET
                AJER Publishing
                2709-2607
                January 01 2024
                March 16 2024
                : 5
                : 1
                : 362-370
                Article
                10.51867/ajernet.5.1.35
                90e5ff6a-aab0-48db-8fd8-efa9d69c0684
                © 2024

                https://creativecommons.org/licenses/by-nc/4.0

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