Health care leaders' perspectives on the business impact of mobile health clinics.

BACKGROUND: By analyzing how health care leaders in the United States view mobile health programs and their impact on the organization's bottom line, this study equips those who currently operate or plan to deploy mobile clinics with a business case framework. Our aim is to understand health care leaders' perspectives about business-related incentives and disincentives for mobile healthcare. METHODS: We conducted 25 semi-structured key informant interviews with U.S. health care leaders to explore their views and experiences related to mobile health care. We used deductive and inductive thematic analysis to identify patterns in the data. An advisory group with expertise in mobile health, health management, and health care finance informed data collection and analysis. RESULTS: In addition to improving health outcomes, mobile clinics can bolster business objectives of health care organizations including those related to budget, business strategy, organizational culture, and health equity. We created a conceptual framework that demonstrates how these factors, supported by community engagement and data, come together to form a business case for mobile health care. DISCUSSION: Our study demonstrates that mobile clinics can contribute to health care organizations' business goals by aligning with broader organizational strategies. The conceptual model provides a guide for aligning mobile clinics' work with business priorities of organizations and funders. CONCLUSIONS: By understanding how health care leaders reconcile the business pressures they face with opportunities to advance health equity using mobile clinics, we can better support the strategic and sustainable expansion of the mobile health sector.

Building causal knowledge in behavior genetics without racial/ethnic diversity will result in weak causal knowledge.

Behavior genetics often emphasizes methods over the underlying quality of the psychological information to which the methods are applied. A core aspect of this quality is the demographic diversity of the samples. Building causal genetic models based only on European-ancestry samples compromises their generalizability. Behavior genetics researchers must spend additional time and resources diversifying their samples before emphasizing causation.

The roles of liquidity and delay in financial markets based on an optimal forecasting model.

We investigate the roles of liquidity and delay in financial markets through our proposed optimal forecasting model. The efficiency and liquidity of the financial market are examined using stochastic models that incorporate information delay. Based on machine learning, we estimate the in-sample and out-of-sample forecasting price performances of the six proposed methods using the likelihood function and Bayesian methods, and the out-of-sample prediction performance is compared with the benchmark model ARIMA-GARCH. We discover that the forecasting price performance of the proposed simplified delay stochastic model is superior to that of the benchmark methods by the test methods of a variety of loss function, superior predictive ability test (SPA), Akaike information criterion (AIC), and Bayesian information criterion (BIC). Using data from the Chinese stock market, the best forecasting model assesses the efficiency and liquidity of the financial market while accounting for information delay and trade probability. The rise in trade probability and delay time affects the stability of the return distribution and raises the risk, according to stochastic simulation. The empirical findings show that empirical and best forecasting approaches are compatible, that company size and liquidity (delay time) have an inverse relationship, and that delay time and liquidity have a nonlinear relationship. The most efficient have optimal liquidity.