Does psychological well-being (e.g. life satisfaction, happiness, meaning in life) predict the length of a person's life, or are measures of well-being simply assessing the absence of ill-being (depression, loneliness, sadness) and are therefore redundant with ill-being measures? Individual studies have examined this question by measuring and controlling for both well-being and ill-being, but inconsistent findings have been reported. METHODS: A systematic review and random-effects meta-analysis was conducted on the literature of prospective longitudinal studies linking all-cause mortality to well-being. Studies that controlled for ill-being were compared to studies that did not control for ill-being to identify the unique predictive ability of well-being. Separate effects were estimated for different well-being constructs (e.g., optimism) and study characteristics (e.g., patient population). Publication bias was assessed via sensitivity analyses such as removing outliers, trim-and-fill, and RoBMA. RESULTS: A total of 99 prospective longitudinal studies predicted future all-cause mortality from current measures of well-being and controlled for concurrent measures of ill-being. Overall, well-being predicted significantly lower mortality rates when controlling for ill-being (Hazard Ratio [HR] = 0.86 [0.84-0.88]). This finding was apparent when well-being was operationalized with very different constructs and assessments, ranging from optimism to life satisfaction to perceived social support. The magnitude of this association was highly heterogeneous across studies (I2~0.75) with the 95% Prediction Interval for the Hazard Ratio ranging from 0.72 to 1.04. Findings were robust to methods that adjusted effect sizes for the influence of outlying observations, publication bias, or substantial heterogeneity. Surprisingly, the average effect of well-being on mortality was not any stronger when studies failed to control for ill-being (HR = 0.90 [0.88-0.92]), which suggested that well-being predicted mortality independently of the association with an absence of ill-being. CONCLUSION: These results justify research seeking to evaluate the causal role of a broad range of well-being constructs on longevity because they imply that well-being is not simply a lack of ill-being, but more research is needed to identify the causal impacts and mechanisms of these associations.

Economic inequality is on the rise, and children will bear its burden. Yet children's perspectives are rarely considered. We urgently need interdisciplinary research to better understand how inequality gets into children's heads and under their skins, and to inform policies that center children's lived experiences. Economic inequality is on the rise, and children will bear its burden. Yet children's perspectives are rarely considered. We urgently need interdisciplinary research to better understand how inequality gets into children's heads and under their skins, and to inform policies that center children's lived experiences.

Over the past decade, mindset research has increasingly moved beyond the individual, examining how beliefs about ability are shaped and reinforced by the broader social context. This paper extends that work by focusing on peers, a powerful but underexplored influence in adolescent development. We introduce a theoretical framework for understanding peer mindset culture: the shared beliefs that peers hold and communicate about whether ability is fixed or can grow. We argue that fixed mindset peer cultures can erode students' sense of belonging by (a) triggering identity threats (e.g., stereotype threat, perceived mismatch with peers' beliefs, or social contagion of fixed norms), and (b) weakening social connection (e.g., peer selection and low support). We argue that these dynamics are especially consequential for students from minoritized backgrounds, those who are struggling academically, and those who personally endorse a growth mindset. Finally, we outline principles for designing peer-focused mindset interventions. Our goal is to provide avenues for future research and practical strategies to support all students in developing a strong sense of belonging in their learning environments.

Compared to continuing-generation bachelor's graduates (CGGs), first-generation bachelor's graduates (FGGs) are underrepresented in U.S. STEM PhD programs. To better understand how students choose to pursue PhDs, we analyzed interview data from 62 FGG and CGG first-year PhD students in bioscience. Across the sample, four themes emerged to illustrate how social, cognitive, and contextual conditions influenced students' decisions to pursue a PhD: research experiences, social support, career autonomy and impact, and program fit and finances. Findings showed how undergraduate experiences critically shape FGGs' career expectations and goals and revealed additional differences in FGGs' and CGGs' career values and decision-making.

Hesborn Wao, Ph

Large language models (LLMs), such as OpenAI's GPT-4, Google's Bard or Meta's LLaMa, have created unprecedented opportunities for analysing and generating language data on a massive scale. Because language data have a central role in all areas of psychology, this new technology has the potential to transform the field. In this Perspective, we review the foundations of LLMs. We then explain how the way that LLMs are constructed enables them to effectively generate human-like linguistic output without the ability to think or feel like a human. We argue that although LLMs have the potential to advance psychological measurement, experimentation and practice, they are not yet ready for many of the most transformative psychological applications - but further research and development may enable such use. Next, we examine four major concerns about the application of LLMs to psychology, and how each might be overcome. Finally, we conclude with recommendations for investments that could help to address these concerns: field-initiated 'keystone' datasets; increased standardization of performance benchmarks; and shared computing and analysis infrastructure to ensure that the future of LLM-powered research is equitable. Large language models (LLMs), which can generate and score text in human-like ways, have the potential to advance psychological measurement, experimentation and practice. In this Perspective, Demszky and colleagues describe how LLMs work, concerns about using them for psychological purposes, and how these concerns might be addressed.

Teachers' growth mindset supportive language (GMSL)--rhetoric emphasizing that one's skills can be improved over time--has been shown to significantly reduce disparities in academic achievement and enhance students' learning outcomes. Although teachers espouse growth mindset principles, most find it difficult to adopt GMSL in their practice due the lack of effective coaching in this area. We explore whether large language models (LLMs) can provide automated, personalized coaching to support teachers' use of GMSL. We establish an effective coaching tool to reframe unsupportive utterances to GMSL by developing (i) a parallel dataset containing GMSL-trained teacher reframings of unsupportive statements with an accompanying annotation guide, (ii) a GMSL prompt framework to revise teachers' unsupportive language, and (iii) an evaluation framework grounded in psychological theory for evaluating GMSL with the help of students and teachers. We conduct a large-scale evaluation involving 174 teachers and 1,006 students, finding that both teachers and students perceive GMSL-trained teacher and model reframings as more effective in fostering a growth mindset and promoting challenge-seeking behavior, among other benefits. We also find that model-generated reframings outperform those from the GMSL-trained teachers. These results show promise for harnessing LLMs to provide automated GMSL feedback for teachers and, more broadly, LLMs' potentiality for supporting students' learning in the classroom. Our findings also demonstrate the benefit of large-scale human evaluations when applying LLMs in educational domains.

The contributed papers in this special issue each provide valuable perspectives on how social processes are relevant to academic motivation. Yet a critical question remains: How can this research lead to concrete guidance for educators who wish to create motivating and equitable classrooms? We propose this complex task can be simplified by encouraging educators to address students' concerns about how they are viewed by instructors in school. Our review of the literature suggests that two meta-concerns are particularly important to address for students from groups marginalized in education: whether instructors may (1) see them as limited in academic potential and (2) narrowly define them by their academic success. We argue that effective teaching practices address these concerns by communicating two corresponding messages: (1) inclusive expectations, I recognize your potential for academic growth and (2) broad regard, I regard you as a whole person, with a range of personal values, social identities, and relationships. These messages can shift students away from a narrow sense of self, in which their value is defined by current academic performance, and towards an expansive sense of self, in which students feel both academically capable and valued for more than just their academic success. We present evidence that novice instructors can use this framework to develop or adapt practices that are attuned to marginalized students' two meta-concerns and enhance student motivation and engagement. Throughout this commentary, we describe how this framework can build on the important theoretical advances presented elsewhere in this special issue.