Different Paths to Death Row: A Comparison of Men Who Committed Heinous and Less Heinous Crimes

2003 ◽  
Vol 18 (1) ◽  
pp. 15-33 ◽  
Author(s):  
Dorothy Van Soest ◽  
Hyun-Sun Park ◽  
Toni K. Johnson ◽  
Beverly McPhail
Keyword(s):  
Risk Factors ◽  
Environmental Factors ◽  
Crime Prevention ◽  
Violent Crime ◽  
Life Experiences ◽  
Violent Event ◽  
Violent Crimes ◽  
Factors Affecting ◽  
The Road ◽  
Criminal Histories

Part of the answer to violent crime prevention is to understand the route that those who have committed violent crimes have traveled in order to find ways to guide others from the road leading to such violence. An investigation of the lifelong personal and environmental factors affecting 37 men who were executed in 1997 focuses on distinctions between men in two categories based on heinousness of violent crime. The study aimed to identify risk factors and events that preceded the violent event and to compare the constellation of variables of the men who committed particularly heinous murders characterized by extreme rage and brutality with those whose crimes and criminal histories were characterized mostly by property crimes without intentional harm to people. Descriptive results suggest differences between the two groups of men related to 19 variables and the emergence of two diverse profiles of risk factors and life experiences.

scholarly journals THE EFFECTS OF INDIVIDUAL AND SOCIAL RISK FACTORS ON VIOLENT CRIMES OF JUVENILES

2021 ◽  
pp. 153-161
Author(s):  
Ayhan ERBAY ◽  
Ezgi ILDIRIM ÖZCAN
Keyword(s):  
Risk Factors ◽  
Domestic Violence ◽  
Substance Use ◽  
Violent Crime ◽  
Male Gender ◽  
Family Type ◽  
Social Risk ◽  
Social Risk Factors ◽  
Violent Crimes ◽  
The Family

Introduction: The involvement of children in violent crimes as a perpetrator gets the attention of both researchers and society. The purpose of the study is to identify the risk factors that distinguish juveniles in terms of violent crime. The criminal behavior children were examined in two categories in this study. The first category is composed of non-violent crimes against individuals such as larcency, drug use, opposition to the law of meetings and demonstrations. The second category consists of crimes involving intensive violence against individuals such as armed robbery, felonious injury, murder, and sexual assault. Method: Within the scope of the study 940 court files of the juveniles, which are adjudicated between 2015 and 2019 in the Istanbul Courthouse Jurisdiction, were selected randomly. Available information about children in the court file recorded retrospectively. Individual (gender, age at the date of crime, substance use, working in a job, self-harming behavior, run away from home and previous crime history) and social risk factors (duration of education, risky peer, parental education level, working parent, parental crime history, family type, number of siblings, sibling crime history, sibling substance abuse, domestic violence, sharing problems with the family, total monthly income of the family, domestic migration) was compiled using binary coding system. The factors affecting violent crime were determined by logistic regression analysis. Findings: It was found that individual risk factors consist of being male gender, age at the date of crime, substance use, working in a job and run away from home. Whereas the context of social risk factors, it was found that the father's job, father's history of crime and not sharing personal troubles with the family predicted violent crimes. When the researchers put both groups into analysis at the same time, they found that being male gender, age at the date of crime, working in a job, a runaway from home, domestic violence, sharing personal problems with the family have predicted violence. Result: As a result, both individual and social risk factors have been found effective in dragging children to violent crimes.

scholarly journals Identification of Road Traffic Injury Risk Prone Area Using Environmental Factors by Machine Learning Classification in Nonthaburi, Thailand

2021 ◽  
Vol 13 (7) ◽  
pp. 3907
Author(s):  
Morakot Worachairungreung ◽  
Sarawut Ninsawat ◽  
Apichon Witayangkurn ◽  
Matthew N. Dailey
Keyword(s):  
Machine Learning ◽  
Environmental Factors ◽  
Injury Risk ◽  
Road Traffic ◽  
Road Traffic Injury ◽  
Machine Learning Algorithms ◽  
Factors Affecting ◽  
Road Traffic Injuries ◽  
The Road ◽  
Traffic Injury

Road traffic injuries are a major cause of morbidity and mortality worldwide and currently rank ninth globally among the leading causes of disease burden regarding disability-adjusted life years lost. Nonthaburi and Pathum Thani are parts of the greater Bangkok metropolitan area, and the road traffic injury rate is very high in these areas. This study aimed to identify the environmental factors affecting road traffic injury risk prone areas and classify road traffic injuries from an environmental factor dataset using machine learning algorithms. Road traffic injury risk prone areas were set as the dependent variables for the analysis, with other factors that influence road traffic injury risk prone areas being set as independent variables. A total of 20 environmental factors were selected from the spatial datasets. Then, machine learning algorithms were applied using a grid search. The first experiment from 2017 in Nonthaburi and Pathum Thani was used for training the model, and then, 2018 data from Nonthaburi and Pathum Thani were used for validation. The second experiment used 2018 Nonthaburi data for the training, and 2018 Pathum Thani data were used for the validation. The important factors were grocery stores, convenience stores, electronics stores, drugstores, schools, gas stations, restaurants, supermarkets, and road geometrics, with length being the most critical factor that influenced the road traffic injury risk prone model. The first and second experiments in a random forest model provided the best model environmental factors affecting road traffic injury risk prone areas, and machine learning can classify such road traffic injuries.

Violent Crime Among Israeli Inmates: Does Gender Matter?

2020 ◽  
Vol 15 (3) ◽  
pp. 319-339
Author(s):  
Gila Chen
Keyword(s):  
Mental Health ◽  
Risk Factors ◽  
Gender Differences ◽  
Violent Crime ◽  
Economic Status ◽  
Violent Crimes ◽  
Violent Offending ◽  
Abuse And Neglect ◽  
The Relationship ◽  
Destructive Behaviors

The purpose was to examine (a) gender differences in violent crime, (b) gender differences in the risk factors related to violent offending, and (c) the factors that moderate the relationship between gender and violent crime among 290 inmates. The findings indicated no gender differences in violent crimes. The findings revealed that violent crime is associated with family crime, exposure to interparental violence, child abuse and neglect (CAN), and mental health. Low–medium economic status and CAN were associated with higher risk of violence only among the women. The findings highlight the intersection of CAN with self-destructive behaviors which require simultaneous interventions.

scholarly journals Analysis of the Factors Affecting Violent Crime Rates in the US

2020 ◽  
Vol 10 (5) ◽  
pp. 106-109
Author(s):  
Dr. Abhijeet Bhattacharya
Keyword(s):  
Population Density ◽  
Violent Crime ◽  
Unemployment Rate ◽  
District Of Columbia ◽  
Crime Rates ◽  
Gun Ownership ◽  
Violent Crimes ◽  
Factors Affecting ◽  
The Us ◽  
Ownership Rate

The goal of this study is to analyze the factors affecting violent crime rates in the US. It is hypothesized that an increase in the gun ownership rate tends to increase violent crimes in the US. It is hypothesized that urban areas in the US tend to have more violent crimes than rural areas. An OLS regression model is formulated using cross-sectional data set across 50 states and the District of Columbia for the year 2019. The endogenous variable is the violent crime rates per 100,000 inhabitants across 50 states and the District of Columbia. The independent variables used in the OLS regression model are population density per square mile, unemployment rate, percentage of the population living in poverty, and gun ownership rate. The four exogenous variables that are found to be statistically significant are gun ownership, unemployment rate, population density per square mile, and percentage of population living in property. An attempt is also made to formulate strategies that would help in reducing violent crime rates in the US.

Studying the Birth Outcome in Tikrit Teaching Hospital

2018 ◽  
Vol 1 (1) ◽  
pp. 55-57
Author(s):  
Areej Noaman
Keyword(s):  
Risk Factors ◽  
Developing Countries ◽  
Teaching Hospital ◽  
Birth Outcome ◽  
Morbidity And Mortality ◽  
Healthy Baby ◽  
Factors Affecting ◽  
Neonatal Deaths ◽  
Healthy Mother

  Background : A successful birth outcome is defined as the birth of a healthy baby to a healthy mother. While relatively low in industrialized world, maternal and fetal morbidity and mortality and neonatal deaths occur disproportionately in developing countries. Aim of the Study: To assess birth outcome and identify some risk factors affecting it for achieving favorable birth outcome in Tikrit Teaching Hospital

The IFLA Global Vision Project in Zimbabwe: Towards a Unified Field Road Map for Libraries

Mousaion ◽  
2020 ◽  
Vol 37 (2) ◽  
Author(s):  
Collence Takaingenhamo Chisita ◽  
Nyarai Patience Chibanda
Keyword(s):  
Economic Transformation ◽  
International Federation ◽  
Access To Information ◽  
Factors Affecting ◽  
The Road ◽  
Unified Field ◽  
Road Map ◽  
Library Associations ◽  
Global Vision ◽  
Library Profession

The development of libraries in any country is critical for its socio-economic transformation especially during this 21st century era where access to information and knowledge underpins development. The International Federation of Library Associations and Institutions (IFLA) launched the Global Vision Project in 2017 as a way of strengthening library throughout the world. The project has seen over 190 countries participating worldwide. For most nations, especially those in the developing countries, this has indeed created platforms for strong and united library associations that are powering literate, informed and participative societies. A number of countries in Africa including Zimbabwe have taken the initiatives to participate in the IFLA Global Vision. This article seeks to examine the challenges and opportunities   for librarians in Zimbabwe in building a united library field. It will also scrutinize the road travelled by librarians in Zimbabwe in their pursuit of a vision to reposition their libraries on the global library landscape. The   article will also study the factors affecting the development of a unified library sector in Zimbabwe. It will also explore how the national professional association Zimbabwe Library Association (ZIMLA) can contribute towards a unified library profession through collaboration. The article also proposes a strategy to enhance cooperation among librarians in Zimbabwe.

Does HbA1cc Play a Role in the Development of Cardiovascular Diseases?

2018 ◽  
Vol 24 (24) ◽  
pp. 2876-2882 ◽  
Author(s):  
Kailash Prasad
Keyword(s):  
Risk Factors ◽  
Cardiovascular Diseases ◽  
Cardiovascular System ◽  
Half Life ◽  
Serum Levels ◽  
Serum Glucose ◽  
Cvd Risk ◽  
Factors Affecting ◽  
Diagnosis And Management ◽  
Coronary Artery Atherosclerosis

Cardiovascular diseases (CVD) may be mediated through increases in the cardiovascular risk factors. Hemoglobin A1c (HbA1c) also called glycated hemoglobin is presently used for the diagnosis and management of diabetes. It has adverse effects on cardiovascular system. This review deals with its synthesis and effects on the cardiovascular system. The serum levels of HbA1c have been reported to be affected by various factors including, the lifespan of erythrocytes, factors affecting erythropoiesis, agents interfering glycation of Hb, destruction of erythrocytes, drugs that shift the formation of Hb, statins, and drugs interfering the HbA1c assay. Levels of HbA1c are positively correlated with serum glucose and advanced glycation end products ( AGE), but no correlation between AGE and serum glucose. AGE cannot replace HbA1c for the diagnosis and management of diabetes because there is no correlation of AGE with serum glucose, and because the half-life of protein with which glucose combines is only 14-20 days as compared to erythrocytes which have a half-life of 90-120 days. HbA1c is positively associated with CVD such as the carotid and coronary artery atherosclerosis, ischemic heart disease, ischemic stroke and hypertension.HbA1c induces dyslipidemia, hyperhomocysteinemia, and hypertension, and increases C-reactive protein, oxidative stress and blood viscosity that would contribute to the development of cardiovascular diseases. In conclusion, HbA1c serves as a useful marker for the diagnosis and management of diabetes. AGE cannot replace HbA1c in the diagnosis and management of diabetes. There is an association of HbA1c with CVD which be mediated through modulation of CVD risk factors.

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