Positive and Negative Risk-Taking in Adolescence and Early Adulthood: A Citizen Science Study During the COVID-19 Pandemic

Positive and negative risk-taking behaviors in adolescents: Distinct characteristics, interrelationships, and influencing factors

Adolescence is a crucial period marked by significant developmental changes, during which risk-taking behaviors can be both a normative part of development and a potential source of concern. However, prior research has focused predominantly on the adverse aspects of risk-taking (i.e., negative risk-taking), overlooked the positive counterpart (i.e., positive risk-taking), and lacked a detailed examination of both. This study aims to elucidate the distinct characteristics and interrelationships of positive and negative risk-taking behaviors among adolescents and to identify the key factors that influence these behaviors. Through a comprehensive synthesis of theoretical and empirical literature, we explore the multifaceted nature of risk-taking, highlighting its complex influencing factors, including individual traits, family dynamics, peer influence, school environment, and broader community contexts. By identifying the shared and unique factors contributing to positive and negative risk-taking behaviors, we can enable adolescents to navigate this complex stage of life and design targeted interventions. Future research directions include the application of person-centered approaches, the implementation of longitudinal tracking and the interactive effects of influencing factors, among other aspects.

Predictors of Adolescents' Positive and Negative Risk-Taking: Similarities and Differences

INTRODUCTION

The similarities and differences between positive (PRT) and negative risk-taking (NRT) remain unclear. The present study explored the issue in terms of sensation-seeking, impulse control, and peer factors.

METHODS

Adolescents from Liaoning, China, participated in a 2-year longitudinal study with two follow-up surveys. The initial sample in June 2022 included 339 adolescents aged 12-15 years (51% female, Mage = 13.38). The follow-up in June 2023 comprised 324 adolescents aged 13-16 years (52% female, Mage = 14.38) from the original sample.

RESULTS

(1) Sensation seeking was positively related to PRT and NRT, motor control was negatively related to both, whereas attentional control and planning were positively related to PRT and negatively related to NRT. Moreover, Planning (T1) positively predicted PRT (T2). Additionally, changes in sensation seeking positively predicted PRT and NRT (T2), and changes in impulse control negatively predicted both. (2) Only sensation seeking and motor control interaction negatively predicted NRT but positively predicted PRT. (3) Negative peer norms were positively related to PRT and NRT, and positive peer norms was negatively related to NRT. The positive predictive effect of peer norms (T1) disappeared.

CONCLUSIONS

PRT and NRT were both associated with higher sensation seeking, lower motor control and higher negative peer norm. Unlike negative risk taking, positive risk taking was associated with higher planning, the interaction between sensation-seeking and motor control and friendship quality. Our findings provide new information on the nuances of impulse control and positive and negative risk-taking, and have implications for understanding and promoting positive adolescent risk-taking, and PRT is an adaptive and strategic behavior compared to negative risk-taking, which can be facilitated by an increase in the planning and quality of friendship.

From developmental neuroscience to policy: A novel framework based on participatory research

Insights from developmental neuroscience are not always translated to actionable policy decisions. In this review, we explore the potential of bridging the gap between developmental neuroscience and policy through youth participatory research approaches. As the current generation of adolescents lives in an increasingly complex and rapidly changing society, their lived experiences are crucial for both research and policy. Moreover, their active involvement holds significant promise, given their heightened creativity and need to contribute. We therefore advocate for a transdisciplinary framework that fosters collaboration between developmental scientists, adolescents, and policy makers in addressing complex societal challenges. We highlight the added value of adolescents' lived experiences in relation to two pressing societal issues affecting adolescents' mental health: performance pressure and social inequality. By integrating firsthand lived experiences with insights from developmental neuroscience, we provide a foundation for progress in informed policy decisions.

Can adolescents be game changers for 21st-century societal challenges?

Adolescents growing up in the 21st century face novel challenges that affect today's adolescents differently compared with previous generations. Adolescents' prosocial values and social engagement can contribute in unique ways to combatting societal challenges. Participatory research provides tools to transform adolescents' prosocial motivations into drivers for societal change.

Citizen science to approach machine learning to society: Detecting loneliness in older adults

Background

Even if we are not aware of it, machine learning techniques are part of our daily lives. It is of the utmost interest that citizens become familiar with the use of these techniques and discover their potential to solve everyday problems.

Objective and Methods

In this article, we describe the methodology and results of a highly replicable citizen science project that allows citizens to get closer to the scientific process and understand the potential of machine learning to solve a social problem of interest to them. For this purpose, we have chosen a problem of social relevance in contemporary societies, namely the detection of loneliness in older adults. Citizens are challenged to apply machine learning techniques to identify levels of loneliness from natural language.

Results

The results of this project suggest that citizens are willing to engage in science when the challenges posed are of social interest to them. A total of 1517 citizens actively engaged in the project. A database containing 1112 texts about loneliness expressions was collected. An accuracy of 83.12% using the logistic regression algorithm and 62.23% accuracy when using the Naïve Bayes algorithm was reached in detecting loneliness from texts.

Conclusions

Detecting loneliness using machine learning techniques is an attractive and relevant topic that allows citizens to be involved in science and introduces them to machine learning practices. The methodology of this project can be replicated in other places around the world.

Digital Health Dashboards for Decision-Making to Enable Rapid Responses During Public Health Crises: Replicable and Scalable Methodology

BACKGROUND

The COVID-19 pandemic has reiterated the need for cohesive, collective, and deliberate societal efforts to address inherent inefficiencies in our health systems and overcome decision-making gaps using real-time data analytics. To achieve this, decision makers need independent and secure digital health platforms that engage citizens ethically to obtain big data, analyze and convert big data into real-time evidence, and finally, visualize this evidence to inform rapid decision-making.

OBJECTIVE

The objective of this study is to develop replicable and scalable jurisdiction-specific digital health dashboards for rapid decision-making to ethically monitor, mitigate, and manage public health crises via systems integration beyond health care.

METHODS

The primary approach in the development of the digital health dashboard was the use of global digital citizen science to tackle pandemics like COVID-19. The first step in the development process was to establish an 8-member Citizen Scientist Advisory Council via Digital Epidemiology and Population Health Laboratory's community partnerships. Based on the consultation with the council, three critical needs of citizens were prioritized: (1) management of household risk of COVID-19, (2) facilitation of food security, and (3) understanding citizen accessibility of public services. Thereafter, a progressive web application (PWA) was developed to provide daily services that address these needs. The big data generated from citizen access to these PWA services are set up to be anonymized, aggregated, and linked to the digital health dashboard for decision-making, that is, the dashboard displays anonymized and aggregated data obtained from citizen devices via the PWA. The digital health dashboard and the PWA are hosted on the Amazon Elastic Compute Cloud server. The digital health dashboard's interactive statistical navigation was designed using the Microsoft Power Business Intelligence tool, which creates a secure connection with the Amazon Relational Database server to regularly update the visualization of jurisdiction-specific, anonymized, and aggregated data.

RESULTS

The development process resulted in a replicable and scalable digital health dashboard for decision-making. The big data relayed to the dashboard in real time reflect usage of the PWA that provides households the ability to manage their risk of COVID-19, request food when in need, and report difficulties and issues in accessing public services. The dashboard also provides (1) delegated community alert system to manage risks in real time, (2) bidirectional engagement system that allows decision makers to respond to citizen queries, and (3) delegated access that provides enhanced dashboard security.

CONCLUSIONS

Digital health dashboards for decision-making can transform public health policy by prioritizing the needs of citizens as well as decision makers to enable rapid decision-making. Digital health dashboards provide decision makers the ability to directly communicate with citizens to mitigate and manage existing and emerging public health crises, a paradigm-changing approach, that is, inverting innovation by prioritizing community needs, and advancing digital health for equity.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

RR1-10.2196/46810.

A perspective on enhancing representative samples in developmental human neuroscience: Connecting science to society

Marginalized groups are often underrepresented in human developmental neuroscientific studies. This is problematic for the generalizability of findings about brain-behavior mechanisms, as well as for the validity, reliability, and reproducibility of results. In the present paper we discuss selection bias in cohort studies, which is known to contribute to the underrepresentation of marginalized groups. First, we address the issue of exclusion bias, as marginalized groups are sometimes excluded from studies because they do not fit the inclusion criteria. Second, we highlight examples of sampling bias. Recruitment strategies are not always designed to reach and attract a diverse group of youth. Third, we explain how diversity can be lost due to attrition of marginalized groups in longitudinal cohort studies. We provide experience- and evidence-based recommendations to stimulate neuroscientists to enhance study population representativeness via science communication and citizen science with youth. By connecting science to society, researchers have the opportunity to establish sustainable and equal researcher-community relationships, which can positively contribute to tackling selection biases.