How emotions, relationships, and culture constitute each other: advances in social functionalist theory.

Social Functionalist Theory (SFT) emerged 20 years ago to orient emotion science to the social nature of emotion. Here we expand upon SFT and make the case for how emotions, relationships, and culture constitute one another. First, we posit that emotions enable the individual to meet six "relational needs" within social interactions: security, commitment, status, trust, fairness, and belongingness. Building upon this new theorising, we detail four principles concerning emotional experience, cognition, expression, and the cultural archiving of emotion. We conclude by considering the bidirectional influences between culture, relationships, and emotion, outlining areas of future inquiry.

Social microclimates and well-being.

Emotional well-being has a known relationship with a person's direct social ties, including friendships; but do ambient social and emotional features of the local community also play a role? This work takes advantage of university students' assignment to different local networks-or "social microclimates"-to probe this question. Using Least Absolute Shrinkage and Selection Operator (LASSO) regression, we quantify the collective impact of individual, social network, and microclimate factors on the emotional well-being of a cohort of first-year college students. Results indicate that well-being tracks individual factors but also myriad social and microclimate factors, reflecting one's peers and social surroundings. Students who belonged to emotionally stable and tight-knit microclimates (i.e., had emotionally stable friends or resided in densely connected residence halls) reported lower levels of psychological distress and higher levels of life satisfaction, even when controlling for factors such as personality and social network size. Although rarely discussed or acknowledged in the policies that create them, social microclimates are consequential to well-being, especially during life transitions. The effects of microclimate factors are small relative to some individual factors; however, they explain unique variance in well-being that is not directly captured by emotional stability or other individual factors. These findings are novel, but preliminary, and should be replicated in new samples and contexts. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Correction: Predicting all-cause risk of 30-day hospital readmission using artificial neural networks.

[This corrects the article DOI: 10.1371/journal.pone.0181173.].

Predicting all-cause risk of 30-day hospital readmission using artificial neural networks.

Avoidable hospital readmissions not only contribute to the high costs of healthcare in the US, but also have an impact on the quality of care for patients. Large scale adoption of Electronic Health Records (EHR) has created the opportunity to proactively identify patients with high risk of hospital readmission, and apply effective interventions to mitigate that risk. To that end, in the past, numerous machine-learning models have been employed to predict the risk of 30-day hospital readmission. However, the need for an accurate and real-time predictive model, suitable for hospital setting applications still exists. Here, using data from more than 300,000 hospital stays in California from Sutter Health's EHR system, we built and tested an artificial neural network (NN) model based on Google's TensorFlow library. Through comparison with other traditional and non-traditional models, we demonstrated that neural networks are great candidates to capture the complexity and interdependency of various data fields in EHRs. LACE, the current industry standard, showed a precision (PPV) of 0.20 in identifying high-risk patients in our database. In contrast, our NN model yielded a PPV of 0.24, which is a 20% improvement over LACE. Additionally, we discussed the predictive power of Social Determinants of Health (SDoH) data, and presented a simple cost analysis to assist hospitalists in implementing helpful and cost-effective post-discharge interventions.