scholarly journals Parent–youth informant disagreement: Implications for youth anxiety treatment

2017 ◽  
Vol 23 (1) ◽  
pp. 42-56 ◽  
Author(s):  
Emily M Becker-Haimes ◽  
Amanda Jensen-Doss ◽  
Boris Birmaher ◽  
Philip C Kendall ◽  
Golda S Ginsburg
Keyword(s):  
Treatment Outcomes ◽  
Polynomial Regression ◽  
Behavioral Therapy ◽  
Parental Psychopathology ◽  
Placebo Treatment ◽  
Adolescent Anxiety ◽  
Anxiety Treatment ◽  
Using Data ◽  
Treatment Prognosis ◽  
Longitudinal Growth Modeling

Greater parent–youth disagreement on youth symptomatology is associated with a host of factors (e.g., parental psychopathology, family functioning) that might impede treatment. Parent–youth disagreement may represent an indicator of treatment prognosis. Using data from the Child/Adolescent Anxiety Multimodal Study, this study used polynomial regression and longitudinal growth modeling to examine whether parent–youth agreement prior to and throughout treatment predicted treatment outcomes (anxiety severity, youth functioning, responder status, and diagnostic remission, rated by an independent evaluator). When parents reported more symptoms than youth prior to treatment, youth were less likely to be diagnosis-free post-treatment; this was only true if the youth received cognitive-behavioral therapy (CBT) alone, not if youth received medication, combination, or placebo treatment. Increasing concordance between parents and youth over the course of treatment was associated with better treatment outcomes across all outcome measures ( ps < .001). How parents and youth “co-report” appears to be an indicator of CBT outcome. Clinical implications and future directions are discussed.

scholarly journals Forecasting COVID-19 cases in Algeria using logistic growth and polynomial regression models

2021 ◽  
Author(s):  
Mohamed LOUNIS ◽  
Babu Malavika
Keyword(s):  
Regression Model ◽  
Logistic Model ◽  
Polynomial Regression ◽  
Logistic Growth ◽  
The Novel ◽  
Logistic Growth Model ◽  
Polynomial Regression Models ◽  
Using Data ◽  
Novel Coronavirus ◽  
Polynomial Regression Model

Abstract The novel Coronavirus respiratory disease 2019 (COVID-19) is still expanding through the world since it started in Wuhan (China) on December 2019 reporting a number of more than 84.4 millions cases and 1.8 millions deaths on January 3rd 2021.In this work and to forecast the COVID-19 cases in Algeria, we used two models: the logistic growth model and the polynomial regression model using data of COVID-19 cases reported by the Algerian ministry of health from February 25th to December 2nd, 2020. Results showed that the polynomial regression model fitted better the data of COVID-19 in Algeria the Logistic model. The first model estimated the number of cases on January, 19th 2021 at 387673 cases. This model could help the Algerian authorities in the fighting against this disease.

The “What If” and “Yes But” Syndrome

2021 ◽  
pp. 79-86
Author(s):  
Daniel M. Doleys ◽  
Nicholas D. Doleys
Keyword(s):  
Chronic Pain ◽  
Treatment Outcomes ◽  
Pain Catastrophizing ◽  
Behavioral Therapy ◽  
Office Visit ◽  
Positive Outcome ◽  
Definitive Diagnosis ◽  
Critical Area ◽  
Pain Medications ◽  
Frustration Level

Repeated requests for a definitive diagnosis, prognosis, and reassurance as to the positive outcome of a therapy made by some patients with chronic pain can be very exhausting to both patient and clinician—especially when it is clear that no amount of information will be satisfactory. The practitioner can easily feel like be asked to be a psychic or fortune teller. Pain catastrophizing (PC) has emerged as critical area of study. PC has been linked pain intensity, decreased function, and treatment outcomes, including the effect of pain medications such as opioids. It is most effectively addressed by the use of cognitive-behavioral therapy procedures. Learning how to apply these strategies in the context of the typical office visit can reduce the frustration level of the clinician and patient. In more severe cases, referral to behavioral specialist may to advisable.

scholarly journals COGNITIVE BEHAVIORAL THERAPY AGE EFFECTS IN CHILD AND ADOLESCENT ANXIETY: AN INDIVIDUAL PATIENT DATA METAANALYSIS

2013 ◽  
Vol 30 (9) ◽  
pp. 829-841 ◽  
Author(s):  
Kathryn Bennett ◽  
Katharina Manassis ◽  
Stephen D. Walter ◽  
Amy Cheung ◽  
Pamela Wilansky-Traynor ◽  
...  
Keyword(s):  
Cognitive Behavioral Therapy ◽  
Individual Patient Data ◽  
Behavioral Therapy ◽  
Child And Adolescent ◽  
Patient Data ◽  
Age Effects ◽  
Cognitive Behavioral ◽  
Adolescent Anxiety

scholarly journals A Daily Covid-19 Cases Prediction System using Data Mining and Machine Learning Algorithm

2021 ◽  
Author(s):  
Yiqi Jack Gao ◽  
Yu Sun
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Hospital Admissions ◽  
Polynomial Regression ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Policy Makers ◽  
Diverse Range ◽  
Using Data

The start of 2020 marked the beginning of the deadly COVID-19 pandemic caused by the novel SARS-COV-2 from Wuhan, China. As of the time of writing, the virus had infected over 150 million people worldwide and resulted in more than 3.5 million global deaths. Accurate future predictions made through machine learning algorithms can be very useful as a guide for hospitals and policy makers to make adequate preparations and enact effective policies to combat the pandemic. This paper carries out a two pronged approach to analyzing COVID-19. First, the model utilizes the feature significance of random forest regressor to select eight of the most significant predictors (date, new tests, weekly hospital admissions, population density, total tests, total deaths, location, and total cases) for predicting daily increases of Covid-19 cases, highlighting potential target areas in order to achieve efficient pandemic responses. Then it utilizes machine learning algorithms such as linear regression, polynomial regression, and random forest regression to make accurate predictions of daily COVID-19 cases using a combination of this diverse range of predictors and proved to be competent at generating predictions with reasonable accuracy.

Organisational culture and entrepreneurial orientation: An orthogonal perspective of individualism and collectivism

2019 ◽  
Vol 37 (2) ◽  
pp. 125-152 ◽  
Author(s):  
Younggeun Lee ◽  
Michael Howe ◽  
Patrick M Kreiser
Keyword(s):  
Entrepreneurial Orientation ◽  
Polynomial Regression ◽  
Organisational Culture ◽  
Cultural Dimensions ◽  
Firm Level ◽  
Individualism And Collectivism ◽  
Key Dimensions ◽  
Using Data ◽  
The Relationship

This study contributes to the existing literature regarding the relationship between culture and entrepreneurship. Building upon the precepts of institutional theory, we examine the influence of organisational culture on firm-level entrepreneurial orientation. While entrepreneurship researchers have emphasised the importance of entrepreneurial orientation for firms, the influence of organisational culture in supporting the incidence of entrepreneurial orientation has not been adequately studied. In an effort to contribute to this emergent area of inquiry, we consider the role of two key dimensions of organisational culture − individualism and collectivism – in facilitating entrepreneurial orientation. In doing so, we illustrate the utility of adopting an orthogonal conceptualisation of these cultural dimensions rather than the commonly held unidimensional formulation. We use polynomial regression and response surface methodology to investigate the effects of both dimensions of organisational culture on entrepreneurial orientation. Using Korea as the main context of the study, we support our hypotheses using data collected from 406 Korean small- and medium-sized enterprises.

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