Risk and protective factors among at‐risk ultraorthodox Jewish youth in Israel: A contextual model of positive adjustment

2020 ◽  
Author(s):  
Yael Itzhaki‐Braun ◽  
Yafit Sulimani‐Aidan
Keyword(s):  
At Risk ◽  
Protective Factors ◽  
Risk And Protective Factors ◽  
Contextual Model ◽  
Positive Adjustment ◽  
Jewish Youth

Prospective Risk and Protective Factors for Juvenile Arrest Among Youth At Risk for Maltreatment

2019 ◽  
Vol 24 (3) ◽  
pp. 286-298 ◽  
Author(s):  
Anne S. Morrow ◽  
Miguel T. Villodas ◽  
Moira K. Cunius
Keyword(s):  
Risk Factors ◽  
At Risk ◽  
Conduct Disorder ◽  
Protective Factors ◽  
Family Violence ◽  
Risk And Protective Factors ◽  
Chi Squared ◽  
Youth At Risk ◽  
Actuarial Risk ◽  
Prospective Risk

This study aimed to prospectively identify ecological risk factors for juvenile arrest in a sample of youth at risk for maltreatment. Chi-Squared Automatic Interaction Detector analysis was performed with data from 592 youth from the Longitudinal Studies of Child Abuse and Neglect to identify the optimal combination of age 14 predictors of past-year arrest at age 16. Results extended previous research, which has identified being male, having more conduct disorder symptoms, suspension from school, perceived school importance, witnessing family violence, and having a jailed family member as key risk and protective factors for arrest by identifying important interactions among these risk factors. These interactions differentiate youth at the greatest risk of arrest, which, in this sample, were males with greater than two symptoms of conduct disorder who witnessed family violence. These findings suggest that longitudinal and multi-informant data could inform the refinement of actuarial risk assessments for juvenile arrest.

scholarly journals SERIOUS CONDUCT PROBLEMS AMONG GIRLS AT RISK: TRANSLATING RESEARCH INTO INTERVENTION

2011 ◽  
Vol 2 (1/2) ◽  
pp. 142 ◽  
Author(s):  
Marlene M. Moretti ◽  
Candice Odgers ◽  
N. Dickon Reppucci ◽  
Nicole L.A. Catherine
Keyword(s):  
At Risk ◽  
Protective Factors ◽  
Conduct Problems ◽  
Risk And Protective Factors ◽  
Developmental Pathways ◽  
The Past ◽  
Victims Of Violence ◽  
Shared Risk ◽  
Exposure To Trauma

<span style="font-size: small; font-family: Times New Roman;">Until recently, research on serious conduct problems focused primarily on boys and men. In the past decade, however, we have gained a better understanding of the unique and shared risk and protective factors for girls and boys, and the role of gender in relation to developmental pathways associated with such problems. In this paper we discuss findings from the Gender and Aggression Project on risk and protective factors for girls who are perpetrators but also victims of violence. We discuss our findings from a developmental perspective, with the goal of understanding how exposure to adversity and violence early in life places girls at risk for aggression and violence, among other problems, and how continued exposure to trauma and the disruption of interpersonal and self-regulatory developmental processes cascades into ever deeper and broader problems. This research points more clearly to the need for  accessible, evidence-based, and developmentally sensitive intervention.</span>

scholarly journals M136. PSYCHOSIS POLYRISK SCORE (PPS): IMPROVING DETECTION OF INDIVIDUALS AT-RISK AND PREDICTION OF CLINICAL OUTCOMES

2020 ◽  
Vol 46 (Supplement_1) ◽  
pp. S187-S187
Author(s):  
Dominic Oliver ◽  
Giulia Spada ◽  
Joaquim Radua ◽  
Philip McGuire ◽  
Paolo Fusar-Poli
Keyword(s):  
Risk Factors ◽  
At Risk ◽  
High Risk ◽  
General Population ◽  
Protective Factors ◽  
Genetic Risk ◽  
Risk And Protective Factors ◽  
Healthy Controls ◽  
Prevalence Data ◽  
Psychosis Risk

Abstract Background Primary prevention in Clinical High Risk for psychosis (CHR-P) can ameliorate the course of psychotic disorders. Further advancements of knowledge have been slowed by the standstill of the field, which is mostly attributed to its epidemiological weakness. This underlies the limited identification power for at-risk individuals and the relatively modest ability of CHR-P interviews to rule-in a state of risk for psychosis. One potential avenue for improving identification of individuals at risk for psychosis is a Psychosis Polyrisk Score (PPS) integrating genetic and non-genetic risk and protective factors for psychosis. The PPS hinges on recent findings that risk enrichment in CHR-P samples is accounted for by the accumulation of non-genetic factors e.g. parental and sociodemographic risk factors, perinatal risk factors, later risk factors, and antecedents. Methods A prototype of the PPS has been developed encompassing 26 non-genetic risk and protective factors, utilising Relative Risks (RR) from an umbrella review of risk and protective factors for psychosis onset in the general population. This was combined with prevalence data to ensure positive scores indicated increased psychosis risk and negative scores indicated decreased psychosis risk. To pilot this, patients referred for a CHR-P assessment (n=15) and healthy controls (n=66) were recruited and assessed with the PPS. Additionally, to investigate the range and distribution of these scores in the general population, 10,000,000 permutations were run utilising prevalence data to produce a simulated dataset. Results In the simulated general population data, scores ranged from -15 (least risk, equivalent RR = 0.03) to 39.5 (highest risk, RR = 8912.51). 50% of individuals had an RR &lt; 1 (PPS &lt; 0), 26.7% of individuals had an RR &gt; 3 (PPS &gt; 5), and 2.7% RR &gt; 30 (PPS &gt; 15). Patients referred for a CHR-P assessment had higher PPS scores (median=9, IQR=12.75) than healthy controls (median=-1.75, IQR=8.875). PPS scores in the simulated general population dataset (median=0, IQR=9.5) were similarly lower than patients. Discussion The PPS has potential for improving identification of individuals at risk for psychosis. Its distribution in a simulated general population is reflective of expected psychosis risk, with the vast majority of people not being at-risk and very few being at high risk. In addition to supplementing current assessments for CHR-P, this could be implemented at an earlier stage to stratify individuals based on psychosis risk and inform prognoses and clinical decision-making. This promise warrants further research to ascertain its prognostic accuracy and optimal thresholds for clinical intervention.

The Women's Experience: A Look at Risk and Protective Factors for Deployed Female Air Force Personnel

2018 ◽  
Vol 27 (12) ◽  
pp. 1449-1455
Author(s):  
Nicole C. Breeden ◽  
Janet A. Welsh ◽  
Jonathan R. Olson ◽  
Daniel F. Perkins
Keyword(s):  
At Risk ◽  
Protective Factors ◽  
Air Force ◽  
Risk And Protective Factors ◽  
Women's Experience

Social–ecological predictors of externalizing behavior trajectories in at-risk youth

2017 ◽  
Vol 30 (1) ◽  
pp. 255-266 ◽  
Author(s):  
Caleb J. Figge ◽  
Cecilia Martinez-Torteya ◽  
Jessica E. Weeks
Keyword(s):  
At Risk ◽  
Protective Factors ◽  
Externalizing Behavior ◽  
Externalizing Problems ◽  
Child Behavior Checklist ◽  
Information Criterion ◽  
Risk And Protective Factors ◽  
At Risk Youth ◽  
Social Ecological ◽  
Ecological Predictors

AbstractExtant research consistently links youth externalizing problems and later maladaptive outcomes, and these behaviors are particularly detrimental given their relative stability across development. Although an array of risk and protective factors for externalizing problems have been identified, few studies have examined factors reflecting the multiple social–ecological levels that influence child development and used them to predict longitudinal trajectories of externalizing problems. The current study examined externalizing behavior trajectories in a sample of 1,094 at-risk youth (539 boys, 555 girls) from the Longitudinal Studies in Child Abuse and Neglect multisite longitudinal study of child maltreatment. Normed Child Behavior Checklist externalizing scores were used to estimate group trajectories via growth-based trajectory modeling at ages 10, 12, 14, and 16 using the SAS PROC TRAJ procedure. Model fit was assessed using the Bayes information criterion and the Akaike information criterion statistics. Analyses revealed optimal fit for five distinct behavioral trajectories: low stable, mid-increasing, mid-decreasing, medium high, and high stable. Multinomial logistic regressions revealed that a combination of risk and protective factors at individual, family, school, and neighborhood levels contribute to distinct trajectories of externalizing problems over time. Predictors of low and decreasing trajectories can inform interventions aimed at addressing externalizing problems among high-risk adolescents.

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