Key Protective Factors Research Articles

Network analysis of adolescent non-suicidal self-injury subgroups identified through latent profile analysis

BACKGROUND Non-suicidal self-injury (NSSI) is common among adolescents and frequently co-occurs with depression. Understanding the distinct patterns of NSSI behaviors, along with their associated risk and protective factors, is crucial for developing effective interventions. AIM To classify NSSI behaviors and examine interactions between risk and resilience factors in Chinese adolescents. METHODS A cross-sectional study involving 3967 Chinese students (51.7% female, mean age 13.58 ± 2.24 years) who completed questionnaires on parenting styles, bullying, childhood maltreatment, depression, resilience, and NSSI. Latent profile analysis (LPA) was used to identify NSSI subtypes, and network analysis explored interactions between risk and resilience factors. RESULTS Three NSSI subtypes were identified: NSSI with depression (18.8%), NSSI without depression (12.3%), and neither (68.9%). Bullying was the central risk factor across subtypes, while emotional control and family support were key protective factors. Statistical analyses showed significant differences between groups (P < 0.001). CONCLUSION This study identified three NSSI subtypes among Chinese adolescents. Bullying emerged as a central risk factor, while emotional control and family support were key protective factors. Targeting these areas may help reduce NSSI behaviors in this population.

College students' sense of belonging and alcohol use amidst COVID-19: Evidence from a 21-day daily diary study.

Alcohol use, combined with the heightened mental health crisis among college students highlighted during the pandemic, remains a significant public health concern. We examine (1) how college students' daily assessed sense of belonging with their institution, a key protective factor for better collegiate mental health, is associated with same-day alcohol-use behaviors (2) and how the associations are moderated by key sociodemographic characteristics relevant to this population (women, minoritized students, first-generation [FG], and students identifying as Lesbian, Gay, Bisexual, Transgender, or Queer [LGBTQ]) amidst COVID-19. We used mixed models using data from a 21-day daily diary study of college students (N = 2,012) in Spring 2021. Results revealed that on days when students felt more uncertainty about their sense of belonging to their college (i.e., low belonging), they were less likely to drink, and drink less overall. This effect was observed after students were back on campus after pandemic-related college closures ended. Heterogeneity by minoritized student subgroups were also observed. College students' sense of belonging continues to be an important psychosocial determinant of health and health behaviors among young adults; at times in unintended ways. This reiterates the importance of examining dynamic relationships between belonging and population health. Public health significance statements: These results provide important insight into the linkages between a key psychosocial factor-students' sense of belonging in college-and their alcohol use patterns amidst COVID-19. Institutional programming and prevention efforts to curb alcohol misuse should be implemented with consideration of how those linkages may differ dynamically considering both between- and within-person variance in belonging.

Protective factors, risk factors, and intervention strategies in the prevention and reduction of crime among adolescents and young adults aged 12-24 years: A scoping review protocol.

Evidence indicates that criminal behaviour in youth is linked with a range of negative physical, mental, and social health consequences. Despite a global decrease over the last 30 years, youth crime remains prevalent. Identifying and mapping the most robust risk and protective factors, and intervention strategies for youth crime could offer important keys for predicting future offense outcomes and assist in developing effective preventive and early intervention strategies. Current reviews in the area do not include literature discussing at risk populations such as First Nations groups from countries such as Australia, Canada and New Zealand. This is a critical gap given the disproportionally high rates of incarceration and youth detention among First Nations people globally, particularly in countries with a colonial past. The aim of this scoping review is to identify and map the key risk and protective factors, along with intervention strategies, that are essential for recognizing adolescents and young adults at risk of crime. This scoping review protocol has been developed in line with the Arksey and O'Malley framework and the Joanna Briggs Institute (JBI) Reviewers' Manual. The review protocol was preregistered with Open Science Framework (https://osf.io/kg4q3). ProQuest, PubMed, Web of Science, Scopus, and PsycInfo were used to retrieve relevant articles. Grey literature was searched using Google searches and ProQuest dissertations databases. Original research articles examining protective factors, risk factors, and intervention strategies for prevention and reduction of crime in 12-24-year-olds were included. Two independent reviewers conducted eligibility decisions and data extraction. Findings has been reported in accordance with the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews. Anticipated findings suggest that current research has extensively examined factors across all levels of the socioecological model, from individual to community levels, revealing a predominant focus on individual-level predictors such as substance use, prior criminal history, and moral development. The review is expected to identify effective interventions that address critical factors within each domain, including Multisystemic Therapy (MST) and Multidimensional Treatment Foster Care (MTFC), which have shown promise in reducing youth crime. Additionally, it will likely highlight significant trends in risk and protective factors, such as the dual role of academic achievement-both as a risk and protective factor-and the impact of family-based interventions. The review will also address gaps in research, particularly regarding Indigenous youth, underscoring the need for targeted studies to better understand their unique challenges. These findings will guide future research and inform the development of comprehensive prevention and early intervention programs tailored to diverse youth populations.

Predictive risk models for COVID-19 patients using the multi-thresholding meta-algorithm

This study aims to develop a Machine Learning model to assess the risks faced by COVID-19 patients in a hospital setting, focusing specifically on predicting the complications leading to Intensive Care Unit (ICU) admission or mortality, which are minority classes compared to the majority class of discharged patients. We operate within a multiclass framework comprising three distinct classes, and address the challenge of dataset imbalance, a common source of model bias. To effectively manage this, we introduce the Multi-Thresholding meta-algorithm (MTh), an innovative output-level methodology that extends traditional thresholding from binary to multiclass classification. This methodology dynamically adjusts class probabilities using misclassification costs, making it highly effective in imbalanced datasets. Our approach is further enhanced by integrating the simplicity, transparency, and effectiveness of Bayesian networks to create a robust predictive model. Using patient admission data, the model accurately identifies key risk and protective factors for COVID-19 outcomes. Our findings indicate that certain patient characteristics, such as high Charlson Index and pre-existing conditions, significantly influence the risk of ICU admission and mortality. Moreover, we introduce an explanatory model that elucidates the interrelationships among these factors, demonstrating the influence of therapeutic limits on the overall risk assessment of COVID-19 patients. Overall, our research provides a significant contribution to the field of Machine Learning by offering a novel solution for multiclass classification in the context of imbalanced datasets. This model not only enhances predictive accuracy but also supports critical decision-making processes in healthcare, potentially improving patient outcomes and optimizing clinical resource allocation.

A Half-Century Analysis of Firearm-Related Mortality Trends in the United States: A Retrospective Study Utilizing National Data (1968-2022).

This study aims to provide a comprehensive examination of trends in firearm-related deaths over the past 55 years, shedding light on the changing landscape and identifying key risk and protective factors associated with firearm-related deaths in the United States. This retrospective study utilizes data from the Centers for Disease Control and Prevention's Web-based Injury Statistics Query and Reporting System (WISQARS) for 1968-2022 to determine trends in firearm-related deaths. A multivariate logistic regression model was employed to identify independent predictors of firearm-related suicides, homicides, and unintentional deaths, exploring intersectionality by introducing interaction terms between race/ethnicity and level of education. Firearm-related deaths showed a fluctuating but upward trend from 12.0/100,000 persons in 1968 to 14.5/100,000 in 2022, with firearm-related suicides consistently accounting for a significant proportion of firearm-related deaths, from 45.7% in 1968 to 56.1% in 2022, with a peak of 63% in 2013. From the multivariate regression analysis, individuals aged 10-19 years had the highest risk of firearm-related suicides (OR = 3.04, 95% CI = 2.92-3.16) and homicides (OR = 2.87, 95% CI = 2.77-2.97). In addition, White people with higher education (OR = 1.42, 95% CI = 1.40-1.45) had the highest risk of firearm-related suicides, while Black people with lower educational attainment (OR = 6.68, 95% CI = 6.50-6.87) had the highest risk of firearm-related homicides. Conclusion: Our findings underscore the urgent need for targeted, evidence-driven public health interventions and policies. Primary suicide prevention strategies focusing on means restriction and reshaping perceptions around firearm ownership emerge as critical components. Comprehensive, multidimensional approaches that engage firearm owners and communities and address structural factors are imperative to curbing the multifaceted challenges associated with firearm-related injuries and deaths. Targeted interventions must include individuals aged 10-19 and specifically focus on suicides and homicides in the most relevant demographic segments of the population.

Supporting mental health in farming communities where and when it is needed most: A longitudinal analysis of risk and protective factors

The cyclical nature of agriculture exposes farmers to challenges that vary over time, influenced by a myriad of external factors beyond their control. This longitudinal study aimed to examine mental health trajectories for Western Australian farmers over a 12 month period, and determine associations with key risk and protective factors. Farmers and farm residents (N = 125) completed an online survey at three timepoints (baseline, 6 months and 12 months) that assessed anxiety, depression, stress, farming stressors, coping strategies, sense of belonging and social supports. Survey timing coincided with periods of harvest and seeding. Risk factors associated with poorer mental health among farmers included a range of dysfunctional and emotion coping strategies, family/relationship stressors, financial/external stressors and daily stressors. Protective factors included sense of belonging and social support. Stress was found to change over time and family/relationship stressors and financial/external stressors at baseline were found to moderate anxiety and stress at 6 months and 12 months. Addressing factors associated with poor mental health trajectories and bolstering factors that are protective for mental health over time will aid clinical and community services in tailoring their services to meet the needs of farmers.

Protective Factors in the LGBTIQ+ Adolescent Experience: A Systematic Review.

Background: LGBTIQ+ adolescents face multiple forms of violence due to minority stress, putting their mental health at risk. These adolescents, particularly trans and gender-diverse youth, face significant challenges, including family violence, bullying, and limited access to health services. This article investigates the factors that protect their mental health. Method: A systematic review of articles published between 2019 and 2024 was conducted using the PRISMA method. Results: The study identifies family and school support, peer relationships, and individual resilience as key protective factors to reduce depression, anxiety, and suicidal behavior, as well as increase self-esteem and well-being. Conclusions: This review calls for further research into protective factors related to the microsystem, exosystem, and macrosystem and the impact of intervention programs with adolescents and their families.

183 Association between mental health and different patterns of physical activity in adolescents in Luxembourg – HBSC 2022

Abstract Purpose Regular physical activity (PA) is a key protective factor for health in youths. The WHO recommends that adolescents do at least an average of 60 minutes per day of moderate-to-vigorous physical activity (MVPA) and incorporate vigorous-intensity aerobic activities (VPA) at least 3 days a week. Nonetheless, lower amounts of PA already carry benefits. This study aims to explore and to examine association between well-being and PA levels in adolescents in Luxembourg. Methods A representative sample of 8117 adolescents (11-to-18-years-old) took part in the 2022 Health Behaviour in School-aged Children (HBSC) Luxembourg survey. Based on the WHO-5 Index adolescents were categorised as “depression symptomatology”, “low mood” and normal well-being. In addition, they were categorised as frequency per week (0 to 7 days) of MVPA and VPA practice. Multinomial logistic regressions were performed, controlling by sex and SES. Normal well-being and physical inactivity were the respective refence groups. Results The results showed an association between mental well-being and both MVPA and VPA (p<.001). Compared to their inactive peers and independently of sex and SES, adolescents in Luxembourg who practiced MVPA 4 days per week or VPA 2 times per week had decreased odds to present low mood (ORMVPA=0.63, 95%CI[0.45-0.87] and ORVPA=0.75, 95%CI[0.60-0.92]) and PA even in a small frequency (1 day per week) already decreased the odds to have depressive symptoms (ORMVPA=0.67, 95%CI[0.51-0.89] and ORVPA=0.61, 95%CI[0.49-0.75]). Conclusions These findings highlight the significance of physical activity in fostering well-being, revealing an inverse relationship between the frequency of physical activity and the occurrence of depressive symptomatology and low well-being. In this regard, the advantages of engaging in physical activity are linked not only to physiological benefits but also to social interactions within these practices. In this context, the benefits of regular physical activity extend beyond its direct impact on mental health, encompassing both physiological and social dimensions.

Does ESG protect firms equally during crises? The role of supply chain concentration

The notion of environmental, social and governance (ESG) has been used by firms as a tool to resist crises. The aspect of ESG that enhances firm viability remains uncertain, as does whether this aspect varies across firms that exhibit distinct supply chain concentrations (SCCs). Clarifying these issues is essential for companies’ attempts to tailor their ESG portfolios in the context of operations to suit their unique circumstances and to improve supply chain viability. The COVID-19 pandemic offers an opportunity to examine these issues. Our study utilizes data regarding Chinese listed firms to conduct an empirical assessment of how ESG affects stock market performance in response to the increase in COVID-19 cases both within China (the national outbreak) and at the global level excluding China (the global pandemic). The results reveal that a significant decline in stock returns occurred as COVID-19 cases increased. Firms that exhibited higher levels of preexisting ESG performance were associated with a less pronounced decrease in stock returns during the national outbreak. Notably, environmental and social responsibilities emerged as key protective factors in response to the national COVID-19 outbreak, while corporate governance proved to be more effective with regard to addressing the global pandemic. In addition, our study reveals that the safeguard provided by environmental and social responsibilities functions as a form of insurance primarily among low-SCC firms. On the other hand, the protective effect of high corporate governance is more evident among high-SCC firms. These findings offer valuable insights for companies seeking to fine-tune their ESG portfolios and navigate the interactions between ESG and SCC.

Artificial Intelligence in Urology: Application of a Machine Learning Model to Predict the Risk of Urolithiasis in a General Population.

This research presents our application of artificial intelligence (AI) in predicting urolithiasis risk. Previous applications, including AI for stone disease, have focused on stone composition and aiding diagnostic imaging. AI applications centered around patient-specific characteristics, lifestyle considerations, and diet have been limited. Our study comprised a robust sample size of 976 Chilean participants, with meticulously analyzed demographic, lifestyle, and health data through a comprehensive questionnaire. We developed a predictive model using various classifiers, including logistic regression, decision trees, random forests, and extra trees, reaching high accuracy (88%) in identifying individuals at risk of kidney stone formation. Key protective factors highlighted by the algorithm include the pivotal role of hydration, physical activity, and dietary patterns that played a crucial role, emphasizing the protective nature of higher fruit and vegetable intake, balanced dairy consumption, and the nuanced impact of specific protein sources on kidney stone risk. In contrast, identified risk factors encompassed gender disparities with males found to be 2.31 times more likely to develop kidney stones than females. Thirst and self-perceived dark urine color emerged as strong predictors, with a significant increase in the likelihood of stone formation. The development of predictive tools with AI, in urolithiasis management signifies a paradigm shift toward more precise and personalized health care. The algorithm's ability to process extensive datasets, including dietary habits, heralds a new era of data-driven medical practice. This research underscores the transformative impact of AI in medical diagnostics and prevention, paving the way for a future where health care interventions are not only more effective but also tailored to individual patient needs. In this case, AI is an important tool that can help patients stay healthy, prevent diseases, and make informed decisions about their overall well-being.

An ensemble machine learning model for predicting one-year mortality in elderly coronary heart disease patients with anemia

ObjectiveThis study was designed to develop and validate a robust predictive model for one-year mortality in elderly coronary heart disease (CHD) patients with anemia using machine learning methods.MethodsDemographics, tests, comorbidities, and drugs were collected for a cohort of 974 elderly patients with CHD. A prospective analysis was performed to evaluate predictive performances of the developed models. External validation of models was performed in a series of 112 elderly CHD patients with anemia.ResultsThe overall one-year mortality was 43.6%. Risk factors included heart rate, chronic heart failure, tachycardia and β receptor blockers. Protective factors included hemoglobin, albumin, high density lipoprotein cholesterol, estimated glomerular filtration rate (eGFR), left ventricular ejection fraction (LVEF), aspirin, clopidogrel, calcium channel blockers, angiotensin converting enzyme inhibitors (ACEIs)/angiotensin receptor blockers (ARBs), and statins. Compared with other algorithms, an ensemble machine learning model performed the best with area under the curve (95% confidence interval) being 0.828 (0.805–0.870) and Brier score being 0.170. Calibration and density curves further confirmed favorable predicted probability and discriminative ability of an ensemble machine learning model. External validation of Ensemble Model also exhibited good performance with area under the curve (95% confidence interval) being 0.825 (0.734–0.916) and Brier score being 0.185. Patients in the high-risk group had more than six-fold probability of one-year mortality compared with those in the low-risk group (P < 0.001). Shaley Additive exPlanation identified the top five risk factors that associated with one-year mortality were hemoglobin, albumin, eGFR, LVEF, and ACEIs/ARBs.ConclusionsThis model identifies key risk factors and protective factors, providing valuable insights for improving risk assessment, informing clinical decision-making and performing targeted interventions. It outperforms other algorithms with predictive performance and provides significant opportunities for personalized risk mitigation strategies, with clinical implications for improving patient care.

Empowering Highly Skilled African Immigrants: Key Protective Factors for Success in Quebec’s Labour Market

Empowering Highly Skilled African Immigrants: Key Protective Factors for Success in Quebec’s Labour Market

What makes us strong: Conceptual and functional comparisons of psychological flexibility and resilience

Psychological flexibility and resilience represent adaptive functioning and the pursuit of values in the presence of adversity such as during the COVID-19 pandemic. Understanding the conceptual and functional differences between these constructs is essential given their overlapping roles as key protective factors. This study involved 1059 participants from a Korean community sample who completed self-reported surveys measuring psychological flexibility, resilience, and mental health outcomes. Network analysis was used to create a sparse network comprising six nodes for psychological flexibility and ten nodes for resilience. Bridge strength centrality was estimated to identify the nodes connecting the two constructs. In addition, we employed a relative weights analysis to evaluate the relative significance of each psychological flexibility process on mental health outcomes while accounting for the composite resilience score. Within the psychological flexibility cluster, “Leaving thoughts be,” “Steady self,” “Awareness of value,” and “Being engaged” emerged as bridge elements, with “Steady self” exhibiting the highest bridge strength. Additionally, different patterns were observed in the unique contribution of each psychological flexibility process to positive and negative mental health outcomes. These findings suggest the potential role of “Steady self” as a catalyst for the transfer of skills and coping mechanisms between the different dimensions of psychological flexibility and resilience. The influential processes identified in this study had predictive value in their association with mental health outcomes. Future directions and implications are discussed.

Association between urinary polycyclic aromatic hydrocarbons and risk of metabolic associated fatty liver disease.

To investigate the effect of urinary PAHs on MAFLD. The study included 3,136 adults from the National Health and Nutrition Examination Survey (NHANES) conducted between 2009 and 2016. Among them, 1,056 participants were diagnosed with MAFLD and were designated as the case group. The analysis of the relationship between monohydroxy metabolites of seven PAHs in urine and MAFLD was carried out using logistic regression and Bayesian kernel regression (BKMR) models. In single-pollutant models, the concentration of 2-hydroxynaphthalene (2-OHNAP) was positively correlated with MAFLD (OR = 1.47, 95% CI 1.18, 1.84), whereas 3-hydroxyfluorene (3-OHFLU) and 1-hydroxypyrene (1-OHPYR) demonstrated a negative correlation with MAFLD (OR = 0.59, 95% CI 0.48 0.73; OR = 0.70, 95% CI 0.55, 0.89). Conversely, in multi-pollutant models, 2-OHNAP, 2-hydroxyfluorene (2-OHFLU), 2-hydroxyphenanthrene, and 3-hydroxyphenanthrene (2&3-OHPHE) displayed positive correlations with MAFLD (OR = 6.17, 95% CI 3.15, 12.07; OR = 2.59, 95% CI 1.37, 4.89). However, 3-OHFLU and 1-OHPYR continued to exhibit negative correlations with MAFLD (OR = 0.09, 95% CI 0.05, 0.15; OR = 0.62, 95% CI 0.43, 0.88). Notably, the BKMR analysis mixtures approach did not indicate a significant joint effect of multiple PAHs on MAFLD, but identified interactions between 3-OHFLU and 2-OHFLU, 1-OHPYR and 2-OHFLU, and 1-OHPYR and 3-OHFLU. No significant association was found between mixed PAHs exposure and the risk of MAFLD. However, interactions were observed between 3-OHFLU and 2-OHFLU. Both 2-OHFLU and 2&3-OHPHE exposure are significant risk factors for MAFLD, whereas 3-OHFLU is a key protective factor for the disease.

Multi-polygenic prediction of frailty highlights chronic pain and educational attainment as key risk and protective factors.

Frailty is a complex trait. Twin studies and recent Genome-Wide Association Studies have demonstrated a strong genetic basis of frailty but there remains a lack of genetic studies exploring genetic prediction of Frailty. Previous work has shown that a single polygenic predictor - represented by a Frailty polygenic score - predicts Frailty, measured via the frailty index, in independent samples within the United Kingdom. We extended this work, using a multi-polygenic score (MPS) approach to increase predictive power. Predictor variables - twenty-six polygenic scores (PGS) were modelled in regularised Elastic net regression models, with repeated cross-validation, to estimate joint prediction of the polygenic scores and order the predictions by their contributing strength to Frailty in two independent cohorts aged 65+ - the English Longitudinal Study of Ageing (ELSA) and Lothian Birth Cohort 1936 (LBC1936). Results showed that the MPS explained 3.6% and 4.7% of variance compared to the best single-score prediction of 2.6% and 2.2% of variance in ELSA and LBC1936 respectively. The strongest polygenic predictors of worsening frailty came from PGS for Chronic pain, Frailty and Waist circumference; whilst PGS for Parental Death, Educational attainment, and Rheumatoid Arthritis were found to be protective to frailty. Results from the predictors remaining in the final model were then validated using the longitudinal LBC1936, with equivalent PGS scores from the same GWAS summary statistics. Thus, this MPS approach provides new evidence for the genetic contributions to frailty in later life and sheds light on the complex structure of the Frailty Index measurement.

Better emotion regulation mediates gratitude and increased stress in undergraduate students across a university semester.

It is well established that university students are vulnerable to poor mental health. Although increased gratitude has been shown to reduce stress among students, a clearer understanding of key mechanisms underpinning this relationship are needed to better inform theoretical models and potential interventions targeted at improving well-being in university students. The present study provides the first direct test of whether capacity for emotion regulation mediates the relationship of gratitude with stress at the beginning and middle of the academic semester. This study is also the first to assess this relationship in a cross-lagged panel mediation model. We used a repeated measures design and a total of 343 undergraduate students completed two online surveys with validated measures of gratitude, stress, and emotion regulation-one at the beginning and the second in the middle of the academic semester. Results showed that emotion regulation mediated the relationship between gratitude and stress at the beginning of semester, and again 6weeks later. A Clogg's z-score test suggested that the strength of the indirect effect significantly increased across the two time points. A post-hoc cross-lagged panel model found that high gratitude at the beginning of the semester predicted low stress in the middle of the semester via emotion regulation. These data provide novel evidence that emotion regulation may serve as a key protective factor against undergraduate stress. The results provide evidence to support leading theories on how gratitude can reduce stress and promote well-being in university students. They also highlight the practical importance of strengthening emotion regulation abilities in university students and can be used to inform future targeted interventions to improve well-being in university students.

‘Getting addicted to it and losing a lot of money… it’s just like a hole.’ A grounded theory model of how social determinants shape adolescents’ choices to not gamble

BackgroundGambling abstinence when underage lowers the risk of harmful gambling in later life. However, little research has examined why many young people refrain from gambling, even though this knowledge can inform protective strategies and lower risk factors to reduce underage gambling and subsequent harm. This study draws on the lived experience of adolescent non-gamblers to explore how social determinants while growing up have shaped their reasons and choices to not gamble.MethodsFourteen Australian non-gamblers, aged 12–17 years, participated in an in-depth individual interview (4 girls, 3 boys) or online community (4 girls, 3 boys). Questions in each condition differed, but both explored participants’ gambling-related experiences while growing up, including exposure, attitudes and behaviours of parents and peers, advertising, simulated gambling and motivations for not gambling. The analysis used adaptive grounded theory methods.ResultsThe grounded theory model identifies several reasons for not gambling, including not being interested, being below the legal gambling age, discouragement from parent and peers, concern about gambling addiction and harm, not wanting to risk money on a low chance of winning, and moral objections. These reasons were underpinned by several social determinants, including individual, parental, peer and environmental factors that can interact to deter young people from underage gambling. Key protective factors were parental role modelling and guidance, friendship groups who avoided gambling, critical thinking, rational gambling beliefs, financial literacy and having other hobbies and interests.ConclusionsChoices to not gamble emanated from multiple layers of influence, implying that multi-layered interventions, aligned with a public health response, are needed to deter underage gambling. At the environmental level, better age-gating for monetary and simulated gambling, countering cultural pressures, and less exposure to promotional gambling messages, may assist young people to resist these influences. Interventions that support parents to provide appropriate role modelling and guidance for their children are also important. Youth education could include cautionary tales from people with lived experience of gambling harm, and education to increase young people’s financial literacy, ability to recognise marketing tactics, awareness of the risks and harms of gambling, and how to resist peer and other normalising gambling influences.

Exploration of the individual and combined effects of predictors of engagement, dropout, and change from digital interventions for recurrent binge eating.

Our ability to predict responsiveness to digital interventions for eating disorders has thus far been poor, potentially for three reasons: (1) there has been a narrow set of predictors explored; (2) prediction has mostly focused on symptom change, ignoring other aspects of the user journey (uptake, early engagement); and (3) there is an excessive focus on the unique effects of predictors rather than the combined contributions of a predictor set. We evaluated the univariate and multivariate effects of outcome predictors in the context of a randomized trial (n = 398) of digitally delivered interventions for recurrent binge eating. Thirty baseline variables were selected as predictors, ranging from specific symptoms, to key protective factors, to technological acceptance, and to online treatment attitudes. Outcomes included uptake, early engagement, and remission. Univariate (d) and multivariate (D) standardized mean differences were calculated to estimate the individual and combined effects of predictors, respectively. At the univariate level, few predictors produced an effect size larger than what is considered small (d > .20) across outcomes. However, our multivariate approach enhanced prediction (Ds = .65 to 1.12), producing accuracy rates greater than chance (63%-71% accuracy). Less than half of the chosen variables proved to be useful in contributing to predictions in multivariate models. Findings suggest that accuracy in outcome prediction from digitally delivered interventions may be better driven by the aggregation of many small effects rather than one or several largely influential predictors. Replication with different data streams (sensor, neuroimaging) would be useful. Our ability to predict who will and will not benefit from digital interventions for eating disorders has been poor. We highlight the viability of a multivariate approach to outcome prediction, whereby prediction may be better driven by the aggregation of many small effects rather than one or a few influential predictors.

Identifying Risk and Protective Factors in Research on Mental Health and Black American Adolescents: 1990 Through 2022

This literature review synthesizes research on Black American adolescents’ mental health published between 1990 and 2022 to identify prominent risk factors and highlight key protective factors for their mental health. Two research questions guided this review: (1) How did researchers examine mental health in Black American adolescents; and (2) What are the risk and protective factors that contribute to mental health outcomes among Black American adolescents? Across more than 30 years of investigation and over 300 published articles, researchers identified several prominent factors that pose risk to Black American adolescents’ mental health, including neighborhood conditions, violence exposure and victimization, racism and racial discrimination, and more recently, the COVID-19 pandemic. However, research also elucidated factors that protect or directly promote Black American adolescents’ mental health, including family, racial and ethnic socialization, racial and ethnic identity, out-of-school time activities, interpersonal relationships, coping, sleep, and social-cognition. The authors conclude with future directions for research and implications for practice.

Distinct members of the Caenorhabditis elegans CeMbio reference microbiota exert cryptic virulence that is masked by host defense.

Microbiotas are complex microbial communities that colonize specific niches in the host and provide essential organismal functions that are important in health and disease. Understanding the ability of each distinct community member to promote or impair host health, alone or in the context of the community, is imperative for understanding how differences in community structure affect host health and vice versa. Recently, a reference 12-member microbiota for the model organism Caenorhabditis elegans, known as CeMbio, was defined. Here, we show the differential ability of each CeMbio bacterial species to activate innate immunity through the conserved PMK-1/p38 MAPK, ACh-WNT, and HLH-30/TFEB pathways. Although distinct CeMbio members differed in their ability to activate the PMK-1/p38 pathway, the ability to do so did not correlate with bacterial-induced lifespan reduction in wild-type or immunodeficient animals. In contrast, most species activated HLH-30/TFEB and showed virulence toward hlh-30-deficient animals. These results suggest that the microbiota of C. elegans is rife with bacteria that can shorten the host's lifespan if host defense is compromised and that HLH-30/TFEB is a fundamental and key host protective factor.