scholarly journals Does the influence of risk factors on accident occurrence change over time?

2016 ◽  
Vol 91 ◽  
pp. 91-102 ◽  
Author(s):  
Rune Elvik
Keyword(s):  
Risk Factors ◽  
Change Over Time ◽  
Over Time

scholarly journals Do Feelings of Defeat and Entrapment Change over Time? An Investigation of the Integrated Motivational—Volitional Model of Suicidal Behaviour Using Ecological Momentary Assessments

2020 ◽  
Vol 17 (13) ◽  
pp. 4685
Author(s):  
Jana-Sophie Stenzel ◽  
Inken Höller ◽  
Dajana Rath ◽  
Nina Hallensleben ◽  
Lena Spangenberg ◽  
...  
Keyword(s):  
Risk Factors ◽  
Suicidal Ideation ◽  
Real Time ◽  
Clinical Samples ◽  
Short Term ◽  
Change Over Time ◽  
Longitudinal Association ◽  
Ecological Momentary ◽  
Over Time ◽  
Short Term Variability

(1) Background. Defeat and entrapment have been highlighted as major risk factors of suicidal ideation and behavior. Nevertheless, little is known about their short-term variability and their longitudinal association in real-time. Therefore, this study aims to investigate whether defeat and entrapment change over time and whether defeat predicts entrapment as stated by the integrated motivational–volitional model of suicidal behavior. (2) Methods. Healthy participants (n = 61) underwent a 7-day smartphone-based ecological momentary assessment (EMA) on suicidal ideation/behavior and relevant risk factors, including defeat and entrapment and a comprehensive baseline (T0) and post (T2) assessment. (3) Results. Mean squared successive differences (MSSD) and intraclass correlations (ICC) support the temporal instability as well as within-person variability of defeat and entrapment. Multilevel analyses revealed that during EMA, defeat was positively associated with entrapment at the same measurement. However, defeat could not predict entrapment to the next measurement (approximately two hours later). (4) Conclusion. This study provides evidence on the short-term variability of defeat and entrapment highlighting that repeated measurement of defeat and entrapment—preferably in real time—is necessary in order to adequately capture the actual empirical relations of these variables and not to overlook significant within-person variability. Further research—especially within clinical samples—seems warranted.

Using behavioural manifestations of dynamic risk factors to assess change in incarcerated sexual offenders

2021 ◽  
Vol 2 (1) ◽  
pp. 1-20
Author(s):  
Tamara Sweller ◽  
◽  
Stuart Thomas ◽  
Michael Daffern ◽  
◽  
...  
Keyword(s):  
Risk Factors ◽  
Psychological Adjustment ◽  
Sexual Offenders ◽  
Social Adjustment ◽  
Self Report ◽  
Change Over Time ◽  
Dynamic Risk Factors ◽  
Dynamic Risk ◽  
Measure Change ◽  
Over Time

This study investigated change in behavioural manifestations of dynamic risk factors, in a sample of adult male sexual offenders who completed a custody-based treatment program. A checklist was developed to monitor and determine change in the frequency of behavioural manifestations of dynamic risk factors and prosocial equivalent behaviours. Offenders and custodial staff completed the checklist once each week for the duration of the offender’s period of treatment (range = 26-69 weeks, M = 45). Checklist scores were aggregated into domains based on the organisation of the Risk for Sexual Violence Protocol (Psychological Adjustment; Social Adjustment; Mental Disorder; Manageability). Change over time for individuals and the group was evaluated. Results showed an increase in positive behaviour in all domains, but there was only a decrease in risk-related manifestations in Psychological Adjustment, and this was only according to offenders. Offender self-report and staff observations were compared, revealing different perceptions of change. Using a behavioural checklist that incorporates self-report and observer data to measure change may provide a comprehensive measure of change over time.

scholarly journals COVID-19 impact on mental health

2022 ◽  
Vol 22 (1) ◽  
Author(s):  
Jingyu Cui ◽  
Jingwei Lu ◽  
Yijia Weng ◽  
Grace Y. Yi ◽  
Wenqing He
Keyword(s):  
Public Health ◽  
Mental Health ◽  
United States ◽  
Risk Factors ◽  
Health Care ◽  
Public Mental Health ◽  
The United States ◽  
Public Confidence ◽  
Change Over Time ◽  
Over Time

Abstract Background The coronavirus disease 2019 (COVID-19) pandemic has posed a significant influence on public mental health. Current efforts focus on alleviating the impacts of the disease on public health and the economy, with the psychological effects due to COVID-19 relatively ignored. In this research, we are interested in exploring the quantitative characterization of the pandemic impact on public mental health by studying an online survey dataset of the United States. Methods The analyses are conducted based on a large scale of online mental health-related survey study in the United States, conducted over 12 consecutive weeks from April 23, 2020 to July 21, 2020. We are interested in examining the risk factors that have a significant impact on mental health as well as in their estimated effects over time. We employ the multiple imputation by chained equations (MICE) method to deal with missing values and take logistic regression with the least absolute shrinkage and selection operator (Lasso) method to identify risk factors for mental health. Results Our analysis shows that risk predictors for an individual to experience mental health issues include the pandemic situation of the State where the individual resides, age, gender, race, marital status, health conditions, the number of household members, employment status, the level of confidence of the future food affordability, availability of health insurance, mortgage status, and the information of kids enrolling in school. The effects of most of the predictors seem to change over time though the degree varies for different risk factors. The effects of risk factors, such as States and gender show noticeable change over time, whereas the factor age exhibits seemingly unchanged effects over time. Conclusions The analysis results unveil evidence-based findings to identify the groups who are psychologically vulnerable to the COVID-19 pandemic. This study provides helpful evidence for assisting healthcare providers and policymakers to take steps for mitigating the pandemic effects on public mental health, especially in boosting public health care, improving public confidence in future food conditions, and creating more job opportunities. Trial registration This article does not report the results of a health care intervention on human participants.

Severity, change over time, and risk factors of anxiety in children with cancer depend on anxiety instrument used

2019 ◽  
Vol 28 (4) ◽  
pp. 710-717 ◽  
Author(s):  
Tanya Lazor ◽  
Jason D. Pole ◽  
Claire De Souza ◽  
Tal Schechter ◽  
Ronald Grant ◽  
...  
Keyword(s):  
Risk Factors ◽  
Children With Cancer ◽  
Change Over Time ◽  
Over Time

Annex I. The Open Arbitrage Forecast: or Why the Dollar Must Drop

1989 ◽  
Vol 129 ◽  
pp. 38-38
Keyword(s):  
Risk Factors ◽  
Exchange Rates ◽  
World Economy ◽  
List Type ◽  
Type Number ◽  
Adequate Description ◽  
Change Over Time ◽  
The World ◽  
Over Time

We have adopted the practice of constructing our forecasts by assuming:- (1)Exchange rates follow the open arbitrage path, with some allowance for risk factors which may change over time.(2)Our model is an adequate description of the world economy, and that the views embedded in it are shared by participants in the market.

scholarly journals 710: Trends of change over time in the specific contribution of different risk factors for PPH

2020 ◽  
Vol 222 (1) ◽  
pp. S448-S449
Author(s):  
Shanny sade ◽  
Yael Baumfeld ◽  
Reut Rotem ◽  
David Yohai ◽  
Gali Pariente ◽  
...  
Keyword(s):  
Risk Factors ◽  
Change Over Time ◽  
Specific Contribution ◽  
Over Time

Do coronary heart disease risk factors change over time?

Metabolism ◽  
2002 ◽  
Vol 51 (8) ◽  
pp. 1022-1026 ◽  
Author(s):  
Ivana Zavaroni ◽  
Diego Ardigo ◽  
Paola Massironi ◽  
Paola Gasparini ◽  
Angela Luciana Barilli ◽  
...  
Keyword(s):  
Risk Factors ◽  
Coronary Heart Disease ◽  
Heart Disease ◽  
Coronary Heart Disease Risk ◽  
Disease Risk ◽  
Change Over Time ◽  
Heart Disease Risk Factors ◽  
Heart Disease Risk ◽  
Over Time
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