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Heliyon Apr 2024With the increasing focus on Environmental, Social, and Governance (ESG) and Corporate Social Responsibility (CSR) on a global scale, stakeholders expect businesses to...
With the increasing focus on Environmental, Social, and Governance (ESG) and Corporate Social Responsibility (CSR) on a global scale, stakeholders expect businesses to transform and enhance social responsibility. Over time, ESG and CSR have developed into vital performance metrics for businesses. Businesses are actively putting improvement measures into place in response to this new paradigm in order to stay competitive in this changing environment. China's dual commitment to CSR and sustainable development is in line with wider objectives, such as resolving issues of pay inequality. In 2012, the China Banking Regulatory Commission (CBRC) unveiled the "Green Credit Guidelines" (GCG), which take corporate governance's environmental considerations into account. These regulations set standards and specifically target high-pollution corporations. Companies may need to restructure their corporate structures and create efficient governance mechanisms in order to comply with these regulations and reduce carbon emissions. This will have an impact on the compensation packages of executives and regular employees. The most important question is how the "GCG" will affect the wage disparity in highly polluting companies. This study examines the 2012 "GCG" and its potential to reduce internal wage disparities, viewing it as a critical element of green financial policy. The paper uses data from Chinese A-share listed companies from 2007 to 2020. Besides, it uses the Difference-in-Differences method to assess the impact of China's GCG, treating its implementation as a quasi-natural experiment and controlling for concurrent policy effects to precisely identify its net impact on corporate carbon emissions and internal wage disparities. The findings show that "GCG" considerably closed internal wage disparities. Furthermore, the "GCG" has a path of guidance, incentives, and punishments that reduce internal wage disparities and promote a more equitable wage distribution within businesses. According to heterogeneity analysis, policies have a greater impact on the wage gap in businesses that are highly dependent on outside funding and have political connection. In order to achieve a compensation balance and meet the objectives of social responsibility and corporate sustainable development, the government should strengthen the complementary effects of green financial policies. The compensation balance in highly polluting companies has important theoretical and practical ramifications. On the one hand, it represents the convergence of income equality, corporate governance, and environmental responsibility. It helps to expand knowledge of sustainable development, fair compensation, and environmental policies. On the other hand, the widening pay disparity between executives and average employees reflects the exacerbation of income inequality in China, which could potentially impact companies' long-term development. Conversely, a well-balanced pay plan can improve worker productivity and motivation while empowering stakeholders to make wise investment choices.
PubMed: 38655360
DOI: 10.1016/j.heliyon.2024.e27851 -
Heliyon Apr 2024This study explored relationships between academic entitlement (AE) and Ratemyprofessors.com (RMP) use. It also investigated, while controlling for AE, if RMP evaluation...
Academic entitlement and Ratemyprofessors.com evaluations bias student teaching evaluations: Implications for faculty evaluation and policy-lenient professors' occupational health.
This study explored relationships between academic entitlement (AE) and Ratemyprofessors.com (RMP) use. It also investigated, while controlling for AE, if RMP evaluation positivity influences students' intentions to ask for policy exemptions, beliefs professors would provide them, intentions to reward and punish professors contingent upon provision of policy exemptions by improving or lowering their student teaching evaluations, and intentions to evaluate and reenroll with professors. Following exposure to RMP evaluations, participants ( = 320) rated their intentions and beliefs toward a fictional professor. They also completed an AE measure. AE was related to frequency of writing RMP evaluations as well as participants' intentions to ask for exemptions, beliefs they would receive them, and intentions to reward and punish professors. RMP evaluation positivity affected participants' intentions to ask for and beliefs they would receive policy exemptions as well as intention to evaluate and reenroll with professors. Effects did not differ by professor or student gender. Participants reported intention to improve the evaluation of professors who provide any policy exemption. This study's findings suggest that student attitudes related to AE and impacted by RMP evaluations have significant implications for professors' occupational health via requests for policy exemptions and the consequences of professors' responses to them. These findings also contribute to the body of evidence that student teaching evaluations do not exclusively measure teaching effectiveness. Similar to grade leniency, policy leniency may bias student teaching evaluations. These contribute to the ongoing discussion of the use of student teaching evaluations in faculty personnel decisions and underscore the need for robust approaches to professor evaluation.
PubMed: 38655302
DOI: 10.1016/j.heliyon.2024.e29473 -
Frontiers in Neurorobotics 2024Rail surface defects present a significant safety concern in railway operations. However, the scarcity of data poses challenges for employing deep learning in defect...
Rail surface defects present a significant safety concern in railway operations. However, the scarcity of data poses challenges for employing deep learning in defect detection. This study proposes an enhanced ACGAN augmentation method to address these issues. Residual blocks mitigate vanishing gradient problems, while a spectral norm regularization-constrained discriminator improves stability and image quality. Substituting the generator's deconvolution layer with upsampling and convolution operations enhances computational efficiency. A gradient penalty mechanism based on regret values addresses gradient abnormality concerns. Experimental validation demonstrates superior image clarity and classification accuracy compared to ACGAN, with a 17.6% reduction in FID value. MNIST dataset experiments verify the model's generalization ability. This approach offers practical value for real-world applications.
PubMed: 38654752
DOI: 10.3389/fnbot.2024.1397369 -
Pediatric Reports Apr 2024Although current policies discourage the use of corporal punishment (CP), its use is still widespread in the US. The objective of this study was to assess the proportion...
Although current policies discourage the use of corporal punishment (CP), its use is still widespread in the US. The objective of this study was to assess the proportion of parents who used CP during the pandemic and identify related risk and protective factors. We analyzed results of a nationwide cross-sectional internet panel survey of 9000 US caregivers who responded in three waves from November 2020 to July 2021. One in six respondents reported having spanked their child in the past week. Spanking was associated with intimate partner violence and the use of multiple discipline strategies and not significantly associated with region or racial self-identification. Parents who spanked sought out more kinds of support, suggesting an opportunity to reduce spanking through more effective parenting resources. Additionally, these results suggest that parents who report using CP may be at risk for concurrent domestic violence.
PubMed: 38651465
DOI: 10.3390/pediatric16020026 -
Brain Communications 2024Deep brain stimulation of the subthalamic nucleus is an effective treatment for the clinical motor symptoms of Parkinson's disease, but may alter the ability to learn...
Deep brain stimulation of the subthalamic nucleus is an effective treatment for the clinical motor symptoms of Parkinson's disease, but may alter the ability to learn contingencies between stimuli, actions and outcomes. We investigated how stimulation of the functional subregions in the subthalamic nucleus (motor and cognitive regions) modulates stimulus-action-outcome learning in Parkinson's disease patients. Twelve Parkinson's disease patients with deep brain stimulation of the subthalamic nucleus completed a probabilistic stimulus-action-outcome task while undergoing ventral and dorsal subthalamic nucleus stimulation (within subjects, order counterbalanced). The task orthogonalized action choice and outcome valence, which created four action-outcome learning conditions: action-reward, inhibit-reward, action-punishment avoidance and inhibit-punishment avoidance. We compared the effects of deep brain stimulation on learning rates across these conditions as well as on computed Pavlovian learning biases. Dorsal stimulation was associated with higher overall learning proficiency relative to ventral subthalamic nucleus stimulation. Compared to ventral stimulation, stimulating the dorsal subthalamic nucleus led to a particular advantage in learning to inhibit action to produce desired outcomes (gain reward or avoid punishment) as well as better learning proficiency across all conditions providing reward opportunities. The Pavlovian reward bias was reduced with dorsal relative to ventral subthalamic nucleus stimulation, which was reflected by improved inhibit-reward learning. Our results show that focused stimulation in the dorsal compared to the ventral subthalamic nucleus is relatively more favourable for learning action-outcome contingencies and reduces the Pavlovian bias that could lead to reward-driven behaviour. Considering the effects of deep brain stimulation of the subthalamic nucleus on learning and behaviour could be important when optimizing stimulation parameters to avoid side effects like impulsive reward-driven behaviour.
PubMed: 38646144
DOI: 10.1093/braincomms/fcae111 -
BioRxiv : the Preprint Server For... Apr 2024Adaptive decision-making requires consideration of objective risks and rewards associated with each option, as well as subjective preference for risky/safe alternatives....
Adaptive decision-making requires consideration of objective risks and rewards associated with each option, as well as subjective preference for risky/safe alternatives. Inaccurate risk/reward estimations can engender excessive risk-taking, a central trait in many psychiatric disorders. The lateral orbitofrontal cortex (lOFC) has been linked to many disorders associated with excessively risky behavior and is ideally situated to mediate risky decision-making. Here, we used single-unit electrophysiology to measure neuronal activity from lOFC of freely moving rats performing in a punishment-based risky decision-making task. Subjects chose between a small, safe reward and a large reward associated with either 0% or 50% risk of concurrent punishment. lOFC activity repeatedly encoded current risk in the environment throughout the decision-making sequence, signaling risk before, during, and after a choice. In addition, lOFC encoded reward magnitude, although this information was only evident during action selection. A Random Forest classifier successfully used neural data accurately to predict the risk of punishment in any given trial, and the ability to predict choice via lOFC activity differentiated between and risk-preferring and risk-averse rats. Finally, risk preferring subjects demonstrated reduced lOFC encoding of risk and increased encoding of reward magnitude. These findings suggest lOFC may serve as a central decision-making hub in which external, environmental information converges with internal, subjective information to guide decision-making in the face of punishment risk.
PubMed: 38645204
DOI: 10.1101/2024.04.08.588332 -
MedRxiv : the Preprint Server For... May 2024Large language models (LLMs) have been proposed to support many healthcare tasks, including disease diagnostics and treatment personalization. While AI may be applied to...
Large language models (LLMs) have been proposed to support many healthcare tasks, including disease diagnostics and treatment personalization. While AI may be applied to assist or enhance the delivery of healthcare, there is also a risk of misuse. LLMs could be used to allocate resources based on unfair, inaccurate, or unjust criteria. For example, a social credit system uses big data to assess "trustworthiness" in society, punishing those who score poorly based on evaluation metrics defined only by a power structure (corporate entity, governing body). Such a system may be amplified by powerful LLMs which can rate individuals based on multimodal data - financial transactions, internet activity, and other behavioural inputs. Healthcare data is perhaps the most sensitive information which can be collected and could potentially be used to violate civil liberty via a "clinical credit system", which may include limiting or rationing access to standard care. This report simulates how clinical datasets might be exploited and proposes strategies to mitigate the risks inherent to the development of AI models for healthcare.
PubMed: 38645190
DOI: 10.1101/2024.04.10.24305470 -
BMC Health Services Research Apr 2024The growing adoption of continuous quality improvement (CQI) initiatives in healthcare has generated a surge in research interest to gain a deeper understanding of CQI.... (Review)
Review
BACKGROUND
The growing adoption of continuous quality improvement (CQI) initiatives in healthcare has generated a surge in research interest to gain a deeper understanding of CQI. However, comprehensive evidence regarding the diverse facets of CQI in healthcare has been limited. Our review sought to comprehensively grasp the conceptualization and principles of CQI, explore existing models and tools, analyze barriers and facilitators, and investigate its overall impacts.
METHODS
This qualitative scoping review was conducted using Arksey and O'Malley's methodological framework. We searched articles in PubMed, Web of Science, Scopus, and EMBASE databases. In addition, we accessed articles from Google Scholar. We used mixed-method analysis, including qualitative content analysis and quantitative descriptive for quantitative findings to summarize findings and PRISMA extension for scoping reviews (PRISMA-ScR) framework to report the overall works.
RESULTS
A total of 87 articles, which covered 14 CQI models, were included in the review. While 19 tools were used for CQI models and initiatives, Plan-Do-Study/Check-Act cycle was the commonly employed model to understand the CQI implementation process. The main reported purposes of using CQI, as its positive impact, are to improve the structure of the health system (e.g., leadership, health workforce, health technology use, supplies, and costs), enhance healthcare delivery processes and outputs (e.g., care coordination and linkages, satisfaction, accessibility, continuity of care, safety, and efficiency), and improve treatment outcome (reduce morbidity and mortality). The implementation of CQI is not without challenges. There are cultural (i.e., resistance/reluctance to quality-focused culture and fear of blame or punishment), technical, structural (related to organizational structure, processes, and systems), and strategic (inadequate planning and inappropriate goals) related barriers that were commonly reported during the implementation of CQI.
CONCLUSIONS
Implementing CQI initiatives necessitates thoroughly comprehending key principles such as teamwork and timeline. To effectively address challenges, it's crucial to identify obstacles and implement optimal interventions proactively. Healthcare professionals and leaders need to be mentally equipped and cognizant of the significant role CQI initiatives play in achieving purposes for quality of care.
Topics: Humans; Quality Improvement; Concept Formation; Delivery of Health Care; Health Personnel; Health Facilities
PubMed: 38641786
DOI: 10.1186/s12913-024-10828-0 -
PloS One 2024Unfair competition on internet platforms (UCIP) has become a critical issue restricting the platform economy's healthy development. This paper applies evolutionary game...
Unfair competition on internet platforms (UCIP) has become a critical issue restricting the platform economy's healthy development. This paper applies evolutionary game theory to study how to utilize multiple subjects' synergy to supervise UCIP effectively. First, the "multi-agent co-governance" mode of UCIP is constructed based on the traditional "unitary supervision" mode. Second, the government and internet platform evolutionary game models are built under two supervision modes. Finally, MATLAB is used to simulate and analyze the evolutionary stage and parameter sensitivity. In addition, we match the model's evolutionary stage with China's supervisory process. The results show that (1) the Chinese government's supervision of UCIP is in the transitional stage from "campaign-style" to "normalization." (2) Moderate government supervision intensity is essential to guide the game system to evolve toward the ideal state. If the supervision intensity is too high, it will inhibit the enthusiasm for supervision. If the supervision intensity is too low, it cannot form an effective deterrent to the internet platforms. (3) When the participation of industry associations and platform users is low, it can only slow down the evolutionary speed of the game system's convergence to the unfavorable state. Nevertheless, it cannot reverse the evolutionary result. (4) Maintaining the participation level of industry associations and platform users above a specific threshold value while increasing punishment intensity will promote the transition of government supervision from the "campaign-style" to the "normalization" stage. This paper provides ideas and references for the Chinese government to design a supervision mechanism for UCIP.
Topics: China; Drive; Emotions; Game Theory; Government; Internet; Economic Competition
PubMed: 38635791
DOI: 10.1371/journal.pone.0301627 -
NeuroImage Apr 2024Punishment of moral norm violators is instrumental for human cooperation. Yet, social and affective neuroscience research has primarily focused on second- and...
Punishment of moral norm violators is instrumental for human cooperation. Yet, social and affective neuroscience research has primarily focused on second- and third-party norm enforcement, neglecting the neural architecture underlying observed (vicarious) punishment of moral wrongdoers. We used naturalistic television drama as a sampling space for observing outcomes of morally-relevant behaviors to assess how individuals cognitively process dynamically evolving moral actions and their consequences. Drawing on Affective Disposition Theory, we derived hypotheses linking character morality with viewers' neural processing of characters' rewards and punishments. We used functional magnetic resonance imaging (fMRI) to examine neural responses of 28 female participants while free-viewing 15 short story summary video clips of episodes from a popular US television soap opera. Each summary included a complete narrative structure, fully crossing main character behaviors (moral/immoral) and the consequences (reward/punishment) characters faced for their actions. Narrative engagement was examined via intersubject correlation and representational similarity analysis. Highest cortical synchronization in 9 specifically selected regions previously implicated in processing moral information was observed when characters who act immorally are punished for their actions with participants' empathy as an important moderator. The results advance our understanding of the moral brain and the role of normative considerations and character outcomes in viewers' engagement with popular narratives.
Topics: Humans; Female; Punishment; Morals; Magnetic Resonance Imaging; Adult; Young Adult; Drama; Cortical Synchronization; Empathy; Cerebral Cortex; Narration
PubMed: 38631616
DOI: 10.1016/j.neuroimage.2024.120613