Participatory methods used in the evaluation of medical devices: a comparison of focus groups, interviews, and a survey.

BACKGROUND: Stakeholder engagement in evaluation of medical devices is crucial for aligning devices with stakeholders' views, needs, and values. Methods for these engagements have however not been compared to analyse their relative merits for medical device evaluation. Therefore, we systematically compared these three methods in terms of themes, interaction, and time-investment. METHODS: We compared focus groups, interviews, and an online survey in a case-study on minimally invasive endoscopy-guided surgery for patients with intracerebral haemorrhage. The focus groups and interviews featured two rounds, one explorative focussing on individual perspectives, and one interactive focussing on the exchange of perspectives between participants. The comparison between methods was made in terms of number and content of themes, how participants interact, and hours invested by all researchers. RESULTS: The focus groups generated 34 themes, the interviews 58, and the survey 42. Various improvements for the assessment of the surgical procedure were only discussed in the interviews. In focus groups, participants were inclined to emphasise agreement and support, whereas the interviews consisted of questions and answers. The total time investment for researchers of focus groups was 95 h, of interviews 315 h, and survey 81 h. CONCLUSIONS: Within the context of medical device evaluation, interviews appeared to be the most appropriate method for understanding stakeholder views since they provide a scope and depth of information that is not generated by other methods. Focus groups were useful to rapidly bring views together. Surveys enabled a quick exploration. Researchers should account for these methodological differences and select the method that is suitable for their research aim.

Mesoscale effects of trader learning behaviors in financial markets: A multi-agent reinforcement learning study.

Recent advances in the field of machine learning have yielded novel research perspectives in behavioural economics and financial markets microstructure studies. In this paper we study the impact of individual trader leaning characteristics on markets using a stock market simulator designed with a multi-agent architecture. Each agent, representing an autonomous investor, trades stocks through reinforcement learning, using a centralized double-auction limit order book. This approach allows us to study the impact of individual trader traits on the whole stock market at the mesoscale in a bottom-up approach. We chose to test three trader trait aspects: agent learning rate increases, herding behaviour and random trading. As hypothesized, we find that larger learning rates significantly increase the number of crashes. We also find that herding behaviour undermines market stability, while random trading tends to preserve it.

Association of Park Renovation With Park Use in New York City.

Importance: Equity-driven citywide park redesign and renovation, such as the Community Parks Initiative (CPI), has the potential to increase park use and opportunities for physical activity in underserved communities. Objective: To evaluate changes in patterns of park use following park redesign and renovation in low-income New York City (NYC) neighborhoods. Design, Setting, and Participants: The Physical Activity and Redesigned Community Spaces study was a prospective quality improvement preintervention-postintervention study design with matched control parks. Thirty-three intervention and 21 control neighborhood parks were selected based on specific criteria related to poverty rates, population growth, and population density in park neighborhoods and not having received more than $250 000 in investment in the past 2 decades. Data were collected at baseline (prerenovation) and 2 follow-up points (3 months and 1 year post renovation) between June 5 and December 4 from 2016 to 2022. Participants were individuals observed as users of study parks. Intervention: The CPI, which involved the redesign and renovation of neighborhood parks by the municipal government of New York City. Main Outcomes and Measures: Main outcomes encompassed park use and physical activity levels assessed using the well-validated System for Observing Play and Recreation in Communities. Park use was quantified by total number of park users, categorized by age group (≤20 years vs ≥21 years), sex, and physical activity level (sitting or standing vs walking or vigorous activity). Changes in outcomes between groups were compared via the generalized estimation equation. Results: A total of 28 322 park users were observed across 1458 scans. At baseline, 6343 of 10 633 users (59.7%) were 20 years or younger, 4927 of 10 632 (46.3%) were female and 5705 (53.7%) were male, and 4641 of 10 605 (43.8%) were sitting or standing. Intervention parks showed more net park users compared with control parks from baseline to the final follow-up (difference-in-difference relative rate ratio, 1.69 [95% CI, 1.22-2.35] users/scan; P = .002). The association was driven by a significant increase in adult users at intervention parks and overall decrease in all users at control parks. Park users engaging in sitting or standing at intervention parks increased (difference, 4.68 [95% CI, 1.71-7.62] users/scan; P = .002) and park users engaging in walking or vigorous physical activity at control parks decreased (difference, -7.30 [95% CI, -10.80 to -4.26] users/scan; P < .001) over time. Conclusions and Relevance: In this quality improvement study, park redesign and renovation were positively associated with park use in low-income neighborhoods. However, park renovations may need to be accompanied by other programmatic strategies to increase physical activity.

Does working capital management improve financial performance in China's agri-food sector during COVID-19? A comparison with the 2008 financial crisis.

The objective of this study is to explore the impact of working capital management on firms' financial performance in China's agri-food sector from 2006 to 2021. In addition, we analyze whether this impact is the same during the 2008 financial crisis and the 2020 COVID-19 crisis. Working capital management is measured by working capital investment policy (measured by current assets to total assets ratio), working capital financing policy (measured by current liabilities to total assets ratio), cash conversion cycle, and net working capital ratio. The results reveal that current assets to total assets ratio and net working capital ratio positively influence financial performance measured through return on assets (ROA), while current liabilities to total assets ratio and cash conversion cycle negatively influence ROA. We also find that the relationship between working capital management and financial performance is more affected during COVID-19 than in the 2008 financial crisis. The findings might provide important implications for company managers to make optimal working capital management practices, depending on the economic environment.

What influences the performance of carbon emissions in China?-Research on the inter-provincial carbon emissions' conditional configuration impacts.

The severe global warming issue currently threatens humans' existence and development. Countries and international organizations have effectively implemented policies to reduce carbon emissions and investigate low-carbon growth strategies. Reducing carbon emissions is a hot topic that academics and government policy-making departments are concerned about.Through necessary condition analysis (NCA) and fuzzy set qualitative comparative analysis(fsQCA), this paper investigates local governments' configuration linkage effect and path choice to improve carbon emission performance from six dimensions: energy consumption, industrial structure, technological innovation, government support, economic development, and demographic factors. The research findings include the following: (1) Individual condition does not represent necessary conditions for the government's carbon performance. Among the two sets of second-order equivalence configurations(S and Q) (five high-level carbon performance configurations), those dominated by economic development or low energy consumption can produce high-level carbon performance. Therefore, the six antecedent conditions dimensions work together to explain how the government can create high levels of carbon performance. (2)According to the regional comparison, China's eastern, central, and western regions exhibit similarities and differences in the driving forces behind high carbon emission performance. All three regions can demonstrate carbon emission performance when all the factors are combined. However, when constrained by the conditions of each region's resource endowment, the eastern region emphasizes the advantage of economic and technological innovation, the central region favors government support and demographic factors, and the western region prefers upgrading industrial structure based on a specific level of economic development.

The peer effects of resident stock market participation: Evidence from 2019 CHFS.

Limited resident's participation in the stock market has become a key constraint to the capital market development. Utilizing the 2019 China Household Financial Survey (CHFS) data, our paper designs probit models to examine the peer effects of residents' stock market participation and explore the intermediary mechanisms with a multiple intermediary model. We find that: (1) Resident involvement in stock market decision-making exhibits significant peer effects. (2) Heterogeneity analysis reveals that males and rural residents display more pronounced peer effects than females and urban residents. Additionally, middle-aged residents demonstrate more potent peer effects than their younger and older counterparts, with the intensity of peer effects correlating with education levels. (3)We observe that the peer effects of market participation operate by altering economic expectations and enhancing residents' financial literacy. (4) Further investigation establishes that individuals engaging in stock market investments manifest peer effects when deciding whether to diversify their stock portfolio. This study holds reference value for analyzing the impact of social interaction on financial behaviors and regulating individuals' financial conduct.

Private equity investment in health care delivery, Australia, 2008-2022.

OBJECTIVES: To examine the scale of private equity investment in Australian health care delivery assets (clinics, hospitals, imaging facilities, other doctor-led health care services). STUDY DESIGN, SETTING: Extraction of information about private equity acquisitions of hospitals, clinics, imaging centres and in vitro fertilisation facilities in Australia, 2008-2022, from a commercial database (PitchBook), supplemented by information from publicly available online media sources. MAIN OUTCOME MEASURES: Number and value of private equity acquisitions of health care assets, 2008-2022; numbers of clinic parent company and clinic acquisitions, 2017-2022. RESULTS: A total of 75 private equity acquisitions of health care delivery assets in Australia during 2008-2022 were identified; the annual number rose from three acquisitions in 2008 to eighteen in 2022. During 2008-2010, five of seven acquisitions were of in vitro fertilisation providers; during 2020-2022, 22 of 39 acquisitions were of clinics or clinic groups, including eleven of eighteen in 2022. The total value of the 39 acquisitions for which purchase price could be ascertained (52%) was $24.1 billion. During 2017-2022, the clinic specialty with the greatest number of private equity acquisitions was general practice (256 of 446 clinics purchased within acquisitions). Seven companies owning ophthalmology clinics (24 clinics) were acquired by private equity. Four private equity acquisitions during 2017-2022 included 60 oncology clinics, all related to a single clinic group. CONCLUSIONS: The number of private equity acquisitions of Australian health care delivery assets increased during 2008-2022. Doctors should be aware of the motivations and dynamics of private equity companies, as they are increasingly likely to interact with these firms and assets owned by these firms.

Improving new energy subsidy efficiency considering learning effect: A case study on wind power.

Subsidy policies are instrumental in driving the development of new energy. However, the effective allocation of new energy subsidies over time is challenging given fiscal constraints. This study addresses this challenge by considering the learning effect associated with the new energy industry. A two-stage dynamic programming model is proposed to capture the investment decision-making process of companies under new energy subsidy policies and government subsidy setups. Theoretical findings suggest that company investment decisions in new energy are influenced by a guiding principle: The subsidy rate should be negatively correlated with the variation rate of production scale increment (VRPSI). We calibrate this investment decision principle using wind power data from 14 countries. According to this principle, excessive subsidy rates may result in a low VRPSI, thereby diminishing future investment profitability in the new energy industry and leading to subsidy inefficiency. Upon investigating the efficiency of annual subsidy allocation, we find that the subsidy rates were potentially set too high in 2014, 2016, and 2017. Furthermore, the government should exercise caution regarding an inefficient subsidy pattern whereby companies invest in new energy only when the subsidy rate exceeds a certain threshold, neglecting traditional power sources. It is crucial to note that although this study uses wind power industry data for calibration and simulation, the theoretical model can be broadly applied to other new energy industries and emerging industries with increasing marginal net profit.

The cost of inaction on health equity and its social determinants.

Rising levels of inflation, debt and macrofiscal tightening are putting expenditures on the social sectors including health under immense scrutiny. Already, there are worrying signs of reductions in social sector investments. However, even before the pandemic, evidence showed the significant returns on investments in health equity and its social determinants. Emerging data and trends show that these potential returns have increased during the COVID-19 pandemic - investments in social determinants can mitigate widespread reductions in human capital and the increasing likelihood of costly syndemics, while promoting access to healthcare innovations that have thus far been inequitably distributed. Therefore, we argue that, despite immediate fiscal pressures, this is exactly the time to invest in health equity and its broader social determinants, as the returns on such investments have never been greater.

Brain activity of professional investors signals future stock performance.

A major aspiration of investors is to better forecast stock performance. Interestingly, emerging "neuroforecasting" research suggests that brain activity associated with anticipatory reward relates to market behavior and population-wide preferences, including stock price dynamics. In this study, we extend these findings to professional investors processing comprehensive real-world information on stock investment options while making predictions of long-term stock performance. Using functional MRI, we sampled investors' neural responses to investment cases and assessed whether these responses relate to future performance on the stock market. We found that our sample of investors could not successfully predict future market performance of the investment cases, confirming that stated preferences do not predict the market. Stock metrics of the investment cases were not predictive of future stock performance either. However, as investors processed case information, nucleus accumbens (NAcc) activity was higher for investment cases that ended up overperforming in the market. These findings remained robust, even when controlling for stock metrics and investors' predictions made in the scanner. Cross-validated prediction analysis indicated that NAcc activity could significantly predict future stock performance out-of-sample above chance. Our findings resonate with recent neuroforecasting studies and suggest that brain activity of professional investors may help in forecasting future stock performance.

The effectiveness of emerging markets' legal structure in explaining financial development.

This article examines the role of legal structure in explaining financial development in twenty-three emerging markets, which has not been explored in institutional economics literature before. This study relied on Pedroni, and Kao cointegration tests, which is followed by the renowned panel cointegration technique. The results of the Pedroni and Kao cointegration tests show that the variables understudy is cointegrated in the long-run. These findings are confirmed by the panel cointegration showing that legal structure (LS) has positive impact on financial development (FIND) in long-run that support Law and Finance, and New Institutional Economics theories in emerging markets. This study is the first to directly examine the long-run impact of LS on FIND in emerging markets, and the result remains consistent across alternative measure of FIND. The findings of this study have important policy implications for emerging markets. Policymakers should focus on creating a legal environment that is conducive to financial development. This includes strengthening the legal framework, improving regulatory regimes, and promoting market autonomy. Additionally, policymakers should work to attract foreign investment, which can help spur economic growth and development in emerging markets. The findings of the study are consistent across battery of robustness testing.

What factors affecting investment decision? The moderating role of fintech self-efficacy.

This study aims to determine, from the perspective of investors, the factors that predict Islamic unit trust (IUT) investment intentions. Additionally, this paper examines the moderating effect of fintech self-efficacy (FSE) on the relationship between attitude and investment intention. A total of 392 data were collected from IUT investors in Malaysia and analyzed using partial least squares structural equation modeling. The findings reveal that subjective norms have the highest impact on investment intention, followed by attitude and FSE, while religiosity is not significantly associated with investment intention in Islamic unit trust funds. Attitude significantly mediates religiosity-intention and Islamic financial literacy-intention relationships. FSE significantly moderates the attitude-intention relationship. The results shed light on the key factors that increase investing behavior and have direct managerial implications with regard to marketing strategies and target markets. These findings suggest that IUT service providers should take the lead in attracting customers through effective and targeted marketing initiatives, particularly by enhancing customers' FSE and capabilities. This study provides empirical evidence on the interrelationships between Islamic financial literacy, religiosity, and FSE in examining investors' behavior using the Theory of Planned Behavior framework. The study explores the moderating role of FSE on the relationship between attitude and investment intention.

Does green investment affect environment pollution: Evidence from asymmetric ARDL approach?

Pollution in the environment is today the biggest issue facing the globe and the main factor in the development of many fatal diseases. The main objective of the study to investigate green investments, economic growth and financial development on environmental pollution in the G-7 countries. This study used annual penal data from 1997 to 2021. The panel NARDL (Non-linear autoregressive distributed lag) results affirm that the positive change of green investment and negative shock in green investment have a significant and positive association with environment pollution in G-7 nations. Our findings provide more evidence for the long-term asymmetry between financial development and environmental performance. However, the findings confirm that a positive modification in financial development has a positive and significant effect on environment pollution. Whereas negative shock in financial development is negative and insignificant relationship with environment pollution. Moreover, the outcomes of the study reveal that both positive shock in gross domestic product growth and negative shock of economic growth have a significant and positive link with environment pollution in G-7 countries. According to the findings, by lowering carbon dioxide emissions, green investments reduced environmental pollution in the G-7 nations over the long and short term. Moreover, it is an innovative research effort that provides light on the connection between green investments, financial development, and the environment while making mention to the EKC in G-7 countries. After all these, our recommendation is to increases green investment expenditures to reduce environmental pollution in the G-7 nations based on our findings. Additionally, one important way for the nation to achieve its sustainable development goals is to improve advancements in the financial sector.

Long-term shareholder perks and stock price reaction.

This study aimed to examine the impact and source of announcements introducing additional long-term shareholder perks on stock prices of Japanese listed companies. We produced more precise analysis results by categorizing the total sample into favorable change and unfavorable change sample. As a result, we found that long-term shareholder perks have a positive impact on stock prices through the expansion of the shareholder base in the case of a favorable change, whereas there is no negative impact on stock liquidity due to an increased number of long term individual shareholders. On the contrary, in the case of an unfavorable change, we found a weak trend of shrinkage in the shareholder base due to individual shareholders' defection and a consequent decrease in stock liquidity. In the case of a favorable change, the long-term shareholder perks program functions as a means to increase the number of shareholders and encourage them to hold the shares for a longer period of time.

Spatio-temporal heterogeneity of China's import and export trade, factors influencing it, and its implications for developing countries' trade.

This study constructed a multidimensional indicator system to evaluate spatio-temporal heterogeneity of China's import and export trade of 31 provinces from 2000 to 2022. This study describes the distribution of China's import and export trade by using location Gini coefficient and exploratory spatial analysis. Additionally, Multiple linear regression was used to ascertain the extent of contribution by various factors on the spatio-temporal heterogeneity of import and export trade. The simulation results show that inter-provincial import and export trade displayed distinct spatio-temporal differentiation characteristics with a prominent east-to-west disparity from 2000 to 2022. The trade links between various regions of the country have gradually strengthened, with a corresponding high correlation to the level of economic development. GDP, financial expenditure, freight transportation volume, technology market turnover, foreign investment, and disposable income of all residents, significantly influence the per capita export and import volume. In general, it is suggested that China and developing countries should take effective measures to promote balanced trade development, strengthen regional cooperation and coordination, and promote green trade and sustainable development.

Implementation of large, multi-site hospital interventions: a realist evaluation of strategies for developing capability.

BACKGROUND: This study presents guidelines for implementation distilled from the findings of a realist evaluation. The setting was local health districts in New South Wales, Australia that implemented three clinical improvement initiatives as part of a state-wide program. We focussed on implementation strategies designed to develop health professionals' capability to deliver value-based care initiatives for multisite programs. Capability, which increases implementers' ability to cope with unexpected scenarios is key to managing change. METHODS: We used a mixed methods realist evaluation which tested and refined program theories elucidating the complex dynamic between context (C), mechanism (M) and outcome (O) to determine what works, for whom, under what circumstances. Data was drawn from program documents, a realist synthesis, informal discussions with implementation designers, and interviews with 10 key informants (out of 37 identified) from seven sites. Data analysis employed a retroductive approach to interrogate the causal factors identified as contributors to outcomes. RESULTS: CMO statements were refined for four initial program theories: Making it Relevant- where participation in activities was increased when targeted to the needs of the staff; Investment in Quality Improvement- where engagement in capability development was enhanced when it was valued by all levels of the organisation; Turnover and Capability Loss- where the effects of staff turnover were mitigated; and Community-Wide Priority- where there was a strategy of spanning sites. From these data five guiding principles for implementers were distilled: (1) Involve all levels of the health system to effectively implement large-scale capability development, (2) Design capability development activities in a way that supports a learning culture, (3) Plan capability development activities with staff turnover in mind, (4) Increased capability should be distributed across teams to avoid bottlenecks in workflows and the risk of losing key staff, (5) Foster cross-site collaboration to focus effort, reduce variation in practice and promote greater cohesion in patient care. CONCLUSIONS: A key implementation strategy for interventions to standardise high quality practice is development of clinical capability. We illustrate how leadership support, attention to staff turnover patterns, and making activities relevant to current issues, can lead to an emergent learning culture.

Foreign direct investment and domestic innovation: Roles of absorptive capacity, quality of regulations and property rights.

Foreign Direct Investment is theoretically expected to facilitate the transfer of knowledge from the home country to the host country, however, the empirical evidence on the subject is mixed. Some studies have shown that, on one hand, as competition grows, the incentive to innovate reduces with the decrease in monopoly rents (Schumpeterian effect). On the other hand, market competition can also boost investments in R&D activities incentivized by incremental profits (Escape-Competition effect). Therefore, this study aims to explore which of these two effects dominates in the selected group of countries. This study also identifies the moderators of the relationship between FDI stock and domestic innovation. It examines the role of absorptive capacity, quality of regulations, and property rights protection in the innovative activities of the host countries. Generalized Method of Moments is used to estimate the parameters of the multivariate regression equation. The analysis is based on panel data consisting of 49 countries over 14 years. The results show that FDI has a negative relationship with domestic innovation, indicating the presence of the Schumpeterian effect. The extensions of the main models show that FDI positively affects domestic innovation in countries with higher absorptive capacity, the superior quality of regulation, and stronger protection of property rights. This study shows that the positive relationship between FDI and domestic innovation is conditional on the ability to absorb knowledge and quality of governance in the recipient countries.

Private Equity Investment in Health Care Soars-But at What Cost?

Study finds more hospital-acquired adverse events after acquisitions.

Modeling opening price spread of Shanghai Composite Index based on ARIMA-GRU/LSTM hybrid model.

In the dynamic landscape of financial markets, accurate forecasting of stock indices remains a pivotal yet challenging task, essential for investors and policymakers alike. This study is motivated by the need to enhance the precision of predicting the Shanghai Composite Index's opening price spread, a critical measure reflecting market volatility and investor sentiment. Traditional time series models like ARIMA have shown limitations in capturing the complex, nonlinear patterns inherent in stock price movements, prompting the exploration of advanced methodologies. The aim of this research is to bridge the gap in forecasting accuracy by developing a hybrid model that integrates the strengths of ARIMA with deep learning techniques, specifically Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) networks. This novel approach leverages the ARIMA model's proficiency in linear trend analysis and the deep learning models' capability in modeling nonlinear dependencies, aiming to provide a comprehensive tool for market prediction. Utilizing a comprehensive dataset covering the period from December 20, 1990, to June 2, 2023, the study develops and assesses the efficacy of ARIMA, LSTM, GRU, ARIMA-LSTM, and ARIMA-GRU models in forecasting the Shanghai Composite Index's opening price spread. The evaluation of these models is based on key statistical metrics, including Mean Squared Error (MSE) and Mean Absolute Error (MAE), to gauge their predictive accuracy. The findings indicate that the hybrid models, ARIMA-LSTM and ARIMA-GRU, perform better in forecasting the opening price spread of the Shanghai Composite Index than their standalone counterparts. This outcome suggests that combining traditional statistical methods with advanced deep learning algorithms can enhance stock market prediction. The research contributes to the field by providing evidence of the potential benefits of integrating different modeling approaches for financial forecasting, offering insights that could inform investment strategies and financial decision-making.