This document summarizes a systematic review of 56 studies that validated online psychometric instruments for common mental health disorders. The following key points were reported:
- The studies evaluated 62 different online instruments for conditions like depression, anxiety disorders, stress, and suicidal ideation.
- Several instruments like the CES-D and MADRS-S for depression have accumulating evidence for adequate psychometric properties in online formats. However, evidence is scattered across different instruments and characteristics.
- Few studies included patient populations, with most recruiting samples from the general community or university students.
- The review provides an overview of validated online instruments but also indicates a need for more research, especially among clinical groups.
Evaluates a meta analysis of family therapy interventions for families facing physical illness.
The slide presentation and article is discussed in greater detail at http://jcoynester.wordpress.com/2013/08/12/interventions-for-the-family-in-chronic-illness-a-meta-analysis-i-like/
The document summarizes and analyzes two educational studies - a quantitative study on classroom support for at-risk students, and a qualitative case study on a teacher's learning in a multicultural curriculum course. It finds that while the individual studies were effective, combining quantitative and qualitative methods would have produced stronger conclusions. A mixture of methods allows statistical correlations to be shared alongside an in-depth analysis of behaviors and interactions. The document concludes that as a new researcher, qualitative research can complement any quantitative study by providing different perspectives to draw fewer gaps in conclusions.
This document provides guidance on selecting appropriate study designs for evaluating population health and health service interventions. It emphasizes that choosing an effective study design is critical for producing high-quality evaluations that can credibly demonstrate the impact of an intervention. The document outlines key initial steps in evaluation planning, such as developing a program logic model and generating evaluation questions. It then describes experimental, quasi-experimental, and non-experimental quantitative study designs and provides examples of when each may be suitable. The intended audience is NSW Health staff involved in planning, implementing, overseeing or using results from evaluations.
Meta analysis: Made Easy with Example from RevManGaurav Kamboj
This document provides an overview of meta-analysis, including:
1) Meta-analysis allows researchers to quantitatively combine the results of multiple studies on a topic to arrive at overall conclusions about the body of research.
2) The key steps of conducting a meta-analysis include developing a research protocol, performing a comprehensive literature search, selecting studies, assessing study quality, extracting data, analyzing data, and addressing heterogeneity and publication bias.
3) Funnel plots and statistical tests can be used to examine potential biases like publication bias in a meta-analysis. Addressing these biases helps ensure the meta-analysis provides an accurate summary of the evidence.
A complex intervention combining professional education, informatics, and financial incentives reduced high-risk prescribing of antiplatelet medications and NSAIDs in primary care practices in Scotland, according to a cluster-randomized controlled trial. The intervention led to a 37% reduction in the rate of high-risk prescribing, from 3.7% to 2.2% of patients. It also significantly reduced hospital admissions for gastrointestinal bleeding and heart failure but not for acute kidney injury. The trial provides evidence that multifaceted interventions can improve prescribing practices and clinical outcomes.
Fb11001 reliability and_validity_in_qualitative_research_summaryDr. Akshay S. Bhat
The document discusses reliability and validity in qualitative research. It begins by explaining quantitative research and how reliability and validity are defined and ensured in quantitative methods. It then explores how reliability and validity are approached differently in qualitative research since the goals of qualitative research are understanding rather than generalization. Specifically:
Reliability in qualitative research focuses on dependability and quality of explanation rather than replicability. Validity is more contingent on the research methodology and aims for understanding rather than truth. Researchers ensure validity in qualitative work through approaches like triangulation of data sources and analysis methods. Overall the document calls for refining definitions of reliability and validity for qualitative research.
The document provides guidance on how to critique scientific articles. It discusses evaluating the study design, methodology, population, interventions, outcomes, and statistics. Key aspects to examine include the randomization and blinding methods, comparison groups, sample sizes, narrowness of focus, and appropriate application of statistical tests. The goal is to determine if the evidence presented is sufficient to accept the study's conclusions by considering factors like biases, generalizability, and clinical significance of any findings.
This document provides an overview of randomized controlled trials (RCTs). It defines RCTs as studies that compare two interventions by randomly assigning participants into groups. The key aspects covered include the importance of randomization for minimizing bias, common types of bias in RCTs, techniques for randomization, and ethical considerations. RCTs are considered the gold standard for inferring causality between an intervention and outcomes.
Evaluates a meta analysis of family therapy interventions for families facing physical illness.
The slide presentation and article is discussed in greater detail at http://jcoynester.wordpress.com/2013/08/12/interventions-for-the-family-in-chronic-illness-a-meta-analysis-i-like/
The document summarizes and analyzes two educational studies - a quantitative study on classroom support for at-risk students, and a qualitative case study on a teacher's learning in a multicultural curriculum course. It finds that while the individual studies were effective, combining quantitative and qualitative methods would have produced stronger conclusions. A mixture of methods allows statistical correlations to be shared alongside an in-depth analysis of behaviors and interactions. The document concludes that as a new researcher, qualitative research can complement any quantitative study by providing different perspectives to draw fewer gaps in conclusions.
This document provides guidance on selecting appropriate study designs for evaluating population health and health service interventions. It emphasizes that choosing an effective study design is critical for producing high-quality evaluations that can credibly demonstrate the impact of an intervention. The document outlines key initial steps in evaluation planning, such as developing a program logic model and generating evaluation questions. It then describes experimental, quasi-experimental, and non-experimental quantitative study designs and provides examples of when each may be suitable. The intended audience is NSW Health staff involved in planning, implementing, overseeing or using results from evaluations.
Meta analysis: Made Easy with Example from RevManGaurav Kamboj
This document provides an overview of meta-analysis, including:
1) Meta-analysis allows researchers to quantitatively combine the results of multiple studies on a topic to arrive at overall conclusions about the body of research.
2) The key steps of conducting a meta-analysis include developing a research protocol, performing a comprehensive literature search, selecting studies, assessing study quality, extracting data, analyzing data, and addressing heterogeneity and publication bias.
3) Funnel plots and statistical tests can be used to examine potential biases like publication bias in a meta-analysis. Addressing these biases helps ensure the meta-analysis provides an accurate summary of the evidence.
A complex intervention combining professional education, informatics, and financial incentives reduced high-risk prescribing of antiplatelet medications and NSAIDs in primary care practices in Scotland, according to a cluster-randomized controlled trial. The intervention led to a 37% reduction in the rate of high-risk prescribing, from 3.7% to 2.2% of patients. It also significantly reduced hospital admissions for gastrointestinal bleeding and heart failure but not for acute kidney injury. The trial provides evidence that multifaceted interventions can improve prescribing practices and clinical outcomes.
Fb11001 reliability and_validity_in_qualitative_research_summaryDr. Akshay S. Bhat
The document discusses reliability and validity in qualitative research. It begins by explaining quantitative research and how reliability and validity are defined and ensured in quantitative methods. It then explores how reliability and validity are approached differently in qualitative research since the goals of qualitative research are understanding rather than generalization. Specifically:
Reliability in qualitative research focuses on dependability and quality of explanation rather than replicability. Validity is more contingent on the research methodology and aims for understanding rather than truth. Researchers ensure validity in qualitative work through approaches like triangulation of data sources and analysis methods. Overall the document calls for refining definitions of reliability and validity for qualitative research.
The document provides guidance on how to critique scientific articles. It discusses evaluating the study design, methodology, population, interventions, outcomes, and statistics. Key aspects to examine include the randomization and blinding methods, comparison groups, sample sizes, narrowness of focus, and appropriate application of statistical tests. The goal is to determine if the evidence presented is sufficient to accept the study's conclusions by considering factors like biases, generalizability, and clinical significance of any findings.
This document provides an overview of randomized controlled trials (RCTs). It defines RCTs as studies that compare two interventions by randomly assigning participants into groups. The key aspects covered include the importance of randomization for minimizing bias, common types of bias in RCTs, techniques for randomization, and ethical considerations. RCTs are considered the gold standard for inferring causality between an intervention and outcomes.
Research in Nursing: A Guide to Understanding Research Designs and TechniquesAJHSSR Journal
ABSTRACT : Nurses like any other professionals are expected to participate in research studies since nursing
is a science that is fast evolving. Research in nursing paves the way for high quality, evidence-based nursing
care. Findings from research highly informs quality nursing practice. Nursing practice needs to be research
based; hence, it is worth commending that all nurses understand research techniques and designs and be
involved in research. However, some bedside nurses are not aware of the relationship between research and the
quality of care provided to patients. Such nurses need to be aware of the importance of research in nursing and
get on board. There are different types of research designs and methods, and the type of design employed for a
particular study will determine the methods to be used for that study. Generally, the different types of study
designs include experimental and non-experimental research designs which can be used according to the need to
answer many questions in the field of nursing. Thus, this paper gives an overview of research designs and
methods in order to provide novice nurses with the basics of research methodology. This istoensure that nurses
have an understanding of the research process and participate in research activities. This will in turn ensure that
quality care which is evidenced-based is rendered to all patients.
This document provides an overview of research methods and biostatistics. It defines key terms like research, research methods, and statistical analysis. It describes different types of study designs including descriptive studies, analytical studies, experimental studies, and epidemiological study designs. It outlines the characteristics of observational studies like cross-sectional and case-control studies as well as experimental studies. It also discusses appropriate statistical tests to analyze different types of data and research problems. Finally, it lists some online resources and computer software that can be used in statistical analysis.
Systematic reviews employ rigorous systematic methods to identify and synthesize data from multiple studies to obtain a quantitative summary of the effects of an intervention. This involves formulating clear objectives and criteria for inclusion of studies, assessing methodological quality, extracting data, and presenting results both descriptively and through meta-analysis to obtain a pooled effect estimate. Conducting systematic reviews using these standardized methods helps establish whether research findings are consistent and generalizable across studies.
This document provides a summary of a meta-analysis presented by Preethi Rai on November 12, 2013. It defines meta-analysis as a quantitative approach that systematically combines the results of previous research studies in order to arrive at conclusions about the body of research. The summary explains that meta-analysis increases the overall sample size and statistical power to better understand treatment effects. It also addresses how meta-analysis can help resolve controversies, identify areas needing more research, and generalize study results. Limitations including publication bias and inability to improve original study quality are also noted.
1. The document discusses correlational and survey research methods. It defines correlational research as studying relationships between two or more variables without influencing them.
2. The basic steps in correlational research are outlined as problem selection, sampling, instrumentation, design and procedures, data collection, and data analysis and interpretation.
3. Survey research is defined as collecting data using questionnaires or interviews to answer questions about populations. Cross-sectional and longitudinal survey designs are described.
1. The document discusses correlational and survey research methods. It provides definitions and purposes of correlational research, including describing relationships between variables and using relationships to predict outcomes.
2. The basic steps of correlational research are outlined, including problem selection, sampling, instrumentation, design and procedures, data collection, and data analysis. Threats to internal validity like subject characteristics and mortality are also discussed.
3. Survey research is defined as collecting data using questionnaires to answer questions about populations. Different types of surveys like cross-sectional, longitudinal, trend, cohort and panel studies are explained. The key steps in conducting survey research are identified.
This document provides an overview of quantitative and qualitative research approaches. It discusses the key differences between the two approaches, including that quantitative research aims to measure predetermined variables and examine relationships numerically, while qualitative research seeks to understand human experiences through exploration. Some common data collection methods for quantitative research include questionnaires and structured observations, while qualitative research often relies on flexible methods like interviews and group discussions. Both approaches have benefits and limitations for understanding different aspects of human phenomena.
This document provides an overview of meta-analysis, including:
1) Meta-analysis is a statistical method for combining results from multiple studies to obtain a single estimate of effect. It provides a more precise estimate than individual studies.
2) Proper meta-analyses require a detailed protocol and eligibility criteria. Studies must be carefully selected and data extracted by multiple independent reviewers.
3) Results are typically reported as odds ratios, risk ratios, or mean differences along with confidence intervals. Forest plots visually display results and heterogeneity between studies.
This document discusses the research methodology used in a study comparing the emotional intelligence and personality of alcoholics/drug addicts and non-addicts. It outlines the objectives, hypotheses, variables, sampling methodology, and measuring tools used. The study uses a 2x2 factorial design to examine differences in emotional intelligence and personality scores between the two groups on factors like neuroticism, extraversion, and psychoticism. Mangal Emotional Intelligence Inventory and Eysenck Personality Questionnaire-Revised are used to measure emotional intelligence and personality.
This document provides an overview of different types of educational research categorized by purpose and method. The main types discussed are:
1. Basic research which aims to develop theories without focusing on practical applications.
2. Applied research which seeks to solve practical problems in fields like education, medicine, and psychology.
3. Action research which is conducted by teachers to diagnose and address issues in their classrooms.
The document also examines research methods including descriptive research, experimental research, case studies, surveys, correlation research, causal comparative studies, and historical research. It provides examples and discusses the characteristics, procedures, advantages, and limitations of each type of educational research method.
Data analysis involves systematically applying statistical and logical techniques to describe, condense, and evaluate data. Key considerations in data analysis include having the necessary analytic skills, selecting appropriate collection and analysis methods concurrently, drawing unbiased inferences, avoiding inappropriate subgroup analyses, following disciplinary norms, determining statistical significance, using clearly defined outcomes, providing honest analysis, and appropriately presenting results. Ensuring proper training, avoiding biases, and adhering to accepted practices are important for maintaining integrity throughout the analysis process.
Levels of evidence, systematic review and guidelinesAboubakr Elnashar
1) Evidence-based medicine requires integrating the best research evidence with clinical expertise and patient values and circumstances.
2) Levels of evidence are used to rank types of medical studies, with systematic reviews and randomized controlled trials ranked highest.
3) Systematic reviews use explicit and reproducible methods to identify, select, and critically appraise relevant research to answer a specific clinical question.
critique osteoarthritis and cartilagefinal4182016James Nichols
This document summarizes a research study that evaluated the efficacy of non-surgical treatment for pain and sensitization in patients with knee osteoarthritis. The study used a pre-defined ancillary analysis of a randomized controlled trial to compare outcomes between a treatment group receiving neuromuscular exercise, education, diet, insoles and pain medications (MEDIC-treatment) and a control group receiving usual care. Outcomes included measures of pain intensity, pain pattern, pain spreading, medication usage, and pain sensitization, which were assessed at baseline and 3-month follow up. The study found some improvements in pain outcomes in the MEDIC-treatment group compared to usual care, though limitations in generalizability and potential confounding factors
This document provides an overview of meta-analysis and summarizes its key aspects and statistical methods. It discusses how meta-analysis can combine results from multiple studies to obtain a single estimate of treatment effect. It also summarizes the steps involved in planning and conducting a meta-analysis, including defining the question, inclusion criteria, searching strategies, and statistical methods for analyzing different types of outcomes. Finally, it reviews several software options available for performing meta-analyses.
This study examined the effect of complementary music therapy on postoperative patients' anxiety, pain, and noise satisfaction. Researchers recruited 41 postoperative patients from a hospital and assigned them to music therapy or control groups. Patients in the music therapy group listened to 30 minutes of non-lyrical music after analgesia, while controls received usual care. Patients completed questionnaires measuring anxiety, pain, and noise satisfaction at various times. Results showed music therapy increased pain and noise satisfaction but not anxiety. Researchers concluded music therapy may improve some postoperative outcomes but limitations included the small sample size. They recommended replicating the study with a larger, more diverse sample.
演講-Meta analysis in medical research-張偉豪Beckett Hsieh
This document provides an overview of meta-analysis. It defines meta-analysis as a quantitative approach to systematically combining results from previous studies to arrive at conclusions about the body of research. It discusses key aspects of planning and conducting a meta-analysis such as defining the research question, searching for relevant literature, determining study eligibility, extracting data, analyzing effect sizes, assessing heterogeneity, and addressing publication bias. Software for performing meta-analyses and specific effect sizes like risk ratio and odds ratio are also mentioned.
Systematic reviews and meta-analyses aim to summarize all available evidence on a topic. A systematic review collects and analyzes results from relevant studies, while a meta-analysis uses statistical methods to combine results into a pooled estimate. Meta-analyses can determine if an effect exists and its direction, but are subject to biases from unpublished or missing studies. They provide more reliable conclusions than individual studies but also have limitations like heterogeneity between studies.
This study examined the relationship between organizational culture, leadership behavior, and job satisfaction among nurses in two hospitals in Taiwan. A survey was administered to 300 nurses to assess dimensions of organizational culture, leadership behavior, and job satisfaction. Results found that organizational culture was positively correlated with leadership behavior and job satisfaction. Leadership behavior was also positively correlated with job satisfaction. The study concluded that organizational culture and leadership behavior influence job satisfaction, and organizations should focus on encouraging subordinates and accomplishing organizational missions.
This document discusses research utilization and evidence-based practice. It defines research utilization as using research findings in practical applications unrelated to the original study, while evidence-based practice integrates research, clinical expertise, and patient values. The document outlines the difference between these concepts and barriers to their implementation, including lack of relevant research, skills to evaluate research, and organizational support. It proposes strategies for nurses to overcome these barriers and more effectively incorporate research into practice.
Prediction and Analysis of Mood Disorders Based On Physical and Social Health...Hannah Farrugia
Goal:
To understand the relationships between physical health and social aspects and whether they coincide with anxiety or mood disorders.
Objectives:
To achieve a deeper general understanding of the physical and social factors that potentially influence or are influenced by mental health
To understand identified relationships and patterns from a technical perspective in the data
To transform the data using techniques so that it is a suitable input for the models being used.
To create the basis for a machine learning model that can be used to predict the onset of mental disease and to ultimately answer the question of whether mental illness can be predicted based on a set of physical and social factors
Review on Psychology Research Based on Artificial Intelligence Methodologies....ElijahCruz6
This document summarizes several studies that applied artificial intelligence methodologies like machine learning and deep learning to psychology research. It discusses studies that used these techniques to predict outcomes for mental health disorders like anxiety, OCD, and depression. Specifically, it describes research that predicted anxiety disorder diagnoses and recovery, used brain imaging to predict OCD severity, and predicted depression development. The document notes common limitations like small sample sizes and issues with data and model structure, and suggests strategies like larger datasets and additional hidden layers could help address limitations.
Application Of Statistical Process Control In Manufacturing Company To Unders...Martha Brown
This document describes a systematic review of 57 studies on the application of statistical process control (SPC) in healthcare quality improvement. The review found that SPC was applied in a wide range of healthcare settings and specialties using 97 different variables. Benefits identified included helping various stakeholders manage change and improve processes. Limitations included the complexity of correctly applying SPC. Barriers were things like lack of staff training, while factors facilitating use included leadership support and data feedback. Overall, SPC was found to be a versatile tool for quality improvement when applied appropriately.
Research in Nursing: A Guide to Understanding Research Designs and TechniquesAJHSSR Journal
ABSTRACT : Nurses like any other professionals are expected to participate in research studies since nursing
is a science that is fast evolving. Research in nursing paves the way for high quality, evidence-based nursing
care. Findings from research highly informs quality nursing practice. Nursing practice needs to be research
based; hence, it is worth commending that all nurses understand research techniques and designs and be
involved in research. However, some bedside nurses are not aware of the relationship between research and the
quality of care provided to patients. Such nurses need to be aware of the importance of research in nursing and
get on board. There are different types of research designs and methods, and the type of design employed for a
particular study will determine the methods to be used for that study. Generally, the different types of study
designs include experimental and non-experimental research designs which can be used according to the need to
answer many questions in the field of nursing. Thus, this paper gives an overview of research designs and
methods in order to provide novice nurses with the basics of research methodology. This istoensure that nurses
have an understanding of the research process and participate in research activities. This will in turn ensure that
quality care which is evidenced-based is rendered to all patients.
This document provides an overview of research methods and biostatistics. It defines key terms like research, research methods, and statistical analysis. It describes different types of study designs including descriptive studies, analytical studies, experimental studies, and epidemiological study designs. It outlines the characteristics of observational studies like cross-sectional and case-control studies as well as experimental studies. It also discusses appropriate statistical tests to analyze different types of data and research problems. Finally, it lists some online resources and computer software that can be used in statistical analysis.
Systematic reviews employ rigorous systematic methods to identify and synthesize data from multiple studies to obtain a quantitative summary of the effects of an intervention. This involves formulating clear objectives and criteria for inclusion of studies, assessing methodological quality, extracting data, and presenting results both descriptively and through meta-analysis to obtain a pooled effect estimate. Conducting systematic reviews using these standardized methods helps establish whether research findings are consistent and generalizable across studies.
This document provides a summary of a meta-analysis presented by Preethi Rai on November 12, 2013. It defines meta-analysis as a quantitative approach that systematically combines the results of previous research studies in order to arrive at conclusions about the body of research. The summary explains that meta-analysis increases the overall sample size and statistical power to better understand treatment effects. It also addresses how meta-analysis can help resolve controversies, identify areas needing more research, and generalize study results. Limitations including publication bias and inability to improve original study quality are also noted.
1. The document discusses correlational and survey research methods. It defines correlational research as studying relationships between two or more variables without influencing them.
2. The basic steps in correlational research are outlined as problem selection, sampling, instrumentation, design and procedures, data collection, and data analysis and interpretation.
3. Survey research is defined as collecting data using questionnaires or interviews to answer questions about populations. Cross-sectional and longitudinal survey designs are described.
1. The document discusses correlational and survey research methods. It provides definitions and purposes of correlational research, including describing relationships between variables and using relationships to predict outcomes.
2. The basic steps of correlational research are outlined, including problem selection, sampling, instrumentation, design and procedures, data collection, and data analysis. Threats to internal validity like subject characteristics and mortality are also discussed.
3. Survey research is defined as collecting data using questionnaires to answer questions about populations. Different types of surveys like cross-sectional, longitudinal, trend, cohort and panel studies are explained. The key steps in conducting survey research are identified.
This document provides an overview of quantitative and qualitative research approaches. It discusses the key differences between the two approaches, including that quantitative research aims to measure predetermined variables and examine relationships numerically, while qualitative research seeks to understand human experiences through exploration. Some common data collection methods for quantitative research include questionnaires and structured observations, while qualitative research often relies on flexible methods like interviews and group discussions. Both approaches have benefits and limitations for understanding different aspects of human phenomena.
This document provides an overview of meta-analysis, including:
1) Meta-analysis is a statistical method for combining results from multiple studies to obtain a single estimate of effect. It provides a more precise estimate than individual studies.
2) Proper meta-analyses require a detailed protocol and eligibility criteria. Studies must be carefully selected and data extracted by multiple independent reviewers.
3) Results are typically reported as odds ratios, risk ratios, or mean differences along with confidence intervals. Forest plots visually display results and heterogeneity between studies.
This document discusses the research methodology used in a study comparing the emotional intelligence and personality of alcoholics/drug addicts and non-addicts. It outlines the objectives, hypotheses, variables, sampling methodology, and measuring tools used. The study uses a 2x2 factorial design to examine differences in emotional intelligence and personality scores between the two groups on factors like neuroticism, extraversion, and psychoticism. Mangal Emotional Intelligence Inventory and Eysenck Personality Questionnaire-Revised are used to measure emotional intelligence and personality.
This document provides an overview of different types of educational research categorized by purpose and method. The main types discussed are:
1. Basic research which aims to develop theories without focusing on practical applications.
2. Applied research which seeks to solve practical problems in fields like education, medicine, and psychology.
3. Action research which is conducted by teachers to diagnose and address issues in their classrooms.
The document also examines research methods including descriptive research, experimental research, case studies, surveys, correlation research, causal comparative studies, and historical research. It provides examples and discusses the characteristics, procedures, advantages, and limitations of each type of educational research method.
Data analysis involves systematically applying statistical and logical techniques to describe, condense, and evaluate data. Key considerations in data analysis include having the necessary analytic skills, selecting appropriate collection and analysis methods concurrently, drawing unbiased inferences, avoiding inappropriate subgroup analyses, following disciplinary norms, determining statistical significance, using clearly defined outcomes, providing honest analysis, and appropriately presenting results. Ensuring proper training, avoiding biases, and adhering to accepted practices are important for maintaining integrity throughout the analysis process.
Levels of evidence, systematic review and guidelinesAboubakr Elnashar
1) Evidence-based medicine requires integrating the best research evidence with clinical expertise and patient values and circumstances.
2) Levels of evidence are used to rank types of medical studies, with systematic reviews and randomized controlled trials ranked highest.
3) Systematic reviews use explicit and reproducible methods to identify, select, and critically appraise relevant research to answer a specific clinical question.
critique osteoarthritis and cartilagefinal4182016James Nichols
This document summarizes a research study that evaluated the efficacy of non-surgical treatment for pain and sensitization in patients with knee osteoarthritis. The study used a pre-defined ancillary analysis of a randomized controlled trial to compare outcomes between a treatment group receiving neuromuscular exercise, education, diet, insoles and pain medications (MEDIC-treatment) and a control group receiving usual care. Outcomes included measures of pain intensity, pain pattern, pain spreading, medication usage, and pain sensitization, which were assessed at baseline and 3-month follow up. The study found some improvements in pain outcomes in the MEDIC-treatment group compared to usual care, though limitations in generalizability and potential confounding factors
This document provides an overview of meta-analysis and summarizes its key aspects and statistical methods. It discusses how meta-analysis can combine results from multiple studies to obtain a single estimate of treatment effect. It also summarizes the steps involved in planning and conducting a meta-analysis, including defining the question, inclusion criteria, searching strategies, and statistical methods for analyzing different types of outcomes. Finally, it reviews several software options available for performing meta-analyses.
This study examined the effect of complementary music therapy on postoperative patients' anxiety, pain, and noise satisfaction. Researchers recruited 41 postoperative patients from a hospital and assigned them to music therapy or control groups. Patients in the music therapy group listened to 30 minutes of non-lyrical music after analgesia, while controls received usual care. Patients completed questionnaires measuring anxiety, pain, and noise satisfaction at various times. Results showed music therapy increased pain and noise satisfaction but not anxiety. Researchers concluded music therapy may improve some postoperative outcomes but limitations included the small sample size. They recommended replicating the study with a larger, more diverse sample.
演講-Meta analysis in medical research-張偉豪Beckett Hsieh
This document provides an overview of meta-analysis. It defines meta-analysis as a quantitative approach to systematically combining results from previous studies to arrive at conclusions about the body of research. It discusses key aspects of planning and conducting a meta-analysis such as defining the research question, searching for relevant literature, determining study eligibility, extracting data, analyzing effect sizes, assessing heterogeneity, and addressing publication bias. Software for performing meta-analyses and specific effect sizes like risk ratio and odds ratio are also mentioned.
Systematic reviews and meta-analyses aim to summarize all available evidence on a topic. A systematic review collects and analyzes results from relevant studies, while a meta-analysis uses statistical methods to combine results into a pooled estimate. Meta-analyses can determine if an effect exists and its direction, but are subject to biases from unpublished or missing studies. They provide more reliable conclusions than individual studies but also have limitations like heterogeneity between studies.
This study examined the relationship between organizational culture, leadership behavior, and job satisfaction among nurses in two hospitals in Taiwan. A survey was administered to 300 nurses to assess dimensions of organizational culture, leadership behavior, and job satisfaction. Results found that organizational culture was positively correlated with leadership behavior and job satisfaction. Leadership behavior was also positively correlated with job satisfaction. The study concluded that organizational culture and leadership behavior influence job satisfaction, and organizations should focus on encouraging subordinates and accomplishing organizational missions.
This document discusses research utilization and evidence-based practice. It defines research utilization as using research findings in practical applications unrelated to the original study, while evidence-based practice integrates research, clinical expertise, and patient values. The document outlines the difference between these concepts and barriers to their implementation, including lack of relevant research, skills to evaluate research, and organizational support. It proposes strategies for nurses to overcome these barriers and more effectively incorporate research into practice.
Prediction and Analysis of Mood Disorders Based On Physical and Social Health...Hannah Farrugia
Goal:
To understand the relationships between physical health and social aspects and whether they coincide with anxiety or mood disorders.
Objectives:
To achieve a deeper general understanding of the physical and social factors that potentially influence or are influenced by mental health
To understand identified relationships and patterns from a technical perspective in the data
To transform the data using techniques so that it is a suitable input for the models being used.
To create the basis for a machine learning model that can be used to predict the onset of mental disease and to ultimately answer the question of whether mental illness can be predicted based on a set of physical and social factors
Review on Psychology Research Based on Artificial Intelligence Methodologies....ElijahCruz6
This document summarizes several studies that applied artificial intelligence methodologies like machine learning and deep learning to psychology research. It discusses studies that used these techniques to predict outcomes for mental health disorders like anxiety, OCD, and depression. Specifically, it describes research that predicted anxiety disorder diagnoses and recovery, used brain imaging to predict OCD severity, and predicted depression development. The document notes common limitations like small sample sizes and issues with data and model structure, and suggests strategies like larger datasets and additional hidden layers could help address limitations.
Application Of Statistical Process Control In Manufacturing Company To Unders...Martha Brown
This document describes a systematic review of 57 studies on the application of statistical process control (SPC) in healthcare quality improvement. The review found that SPC was applied in a wide range of healthcare settings and specialties using 97 different variables. Benefits identified included helping various stakeholders manage change and improve processes. Limitations included the complexity of correctly applying SPC. Barriers were things like lack of staff training, while factors facilitating use included leadership support and data feedback. Overall, SPC was found to be a versatile tool for quality improvement when applied appropriately.
Thank You for referencing this work, if you find it useful!
Berrocal, A., Wac, K., (2018). Peer-ceived Well-Being: Exploring the Value of Peers for Human Stress Assessment in-Situ, ACM UBICOMP/ISWC Doctoral Colloquium, Singapore, October 2018.
Physical activity and the prevention of depressionGiliano Campos
The document summarizes a systematic review examining whether physical activity (PA) can prevent the onset of depression. The review analyzed 30 prospective studies that assessed the relationship between baseline PA and subsequent depression. Twenty-five studies found that higher baseline PA was associated with a lower risk of developing depression later, providing evidence that PA may have a protective effect against future depression. Most of these studies were considered high quality. The review also found that as little as 10-29 minutes of daily PA or 150 minutes per week may help prevent depression. Greater amounts of PA provided even more protection. The majority of the evidence suggests PA can serve as a useful strategy for reducing depression risk from a public health perspective.
Amanda WattenburgThursdayJul 26 at 724pmManage Discussioncheryllwashburn
Amanda Wattenburg
ThursdayJul 26 at 7:24pm
Manage Discussion Entry
Link to screen cast-o-matic:
https://screencast-o-matic.com/watch/cFitVbFMms (Links to an external site.)Links to an external site.
Script:
A brief introduction
Studying cognitive functioning is important as these processes impact individual’s behavior and emotions (Heeramun-Aubeeluck et al., 2015). Various factors can impact cognitive functioning. A disorder known to impact cognition is psychosis. Thus, it is essential to examine psychosis and how these psychotic experiences effect cognitive functioning over time.
Devise a specific research question related to the topic you chose in Week One.
How does psychosis effect cognitive functioning over time in patients who have experienced first-episode psychosis?
Explain the importance of the topic and research question.
Psychosis is a mental state in which individuals experience a loss of touch with reality(Boychuk, Lysaght, & Stuart, 2018). Psychosis may lead to additional occurrences or may indicate signs of a mental health disorder. It is important to examine the cognitive impairment that is caused as a result of psychotic episodes. In addition, this would unfold information that may lead to the importance of treating psychosis when the first signs are noticed in hopes of decreasing the chances of psychosis leading to a mental disorder.
A brief literature review
Zaytseva, Korsokava, Agius, & Gurovich (2013) and Bora & Murray (2014) discovered altered cognitive functioning exists prior to onset or before the prodrome stage. In addition, Bohus & Miclutia (2014) indicate that cognitive functioning at first-episode psychosis was not as strong. Thus, it can be concluded that cognitive functioning impairment occurs prior to first-episode onset however, there is varying research that indicates the impact on cognitive functioning as time goes on. Popolo, Vinci, & Balbi (2010) conducted a year-long study on neurocognitive functioning amongst children and adolescent patients with first-episode psychosis. Cognitive impairment is indicated in early psychosis onset thus the study focused on examining cognitive impairments. Several cognitive assessments were given to patients and the results were evaluated. The results of the cognitive assessments indicated that adolescents with first-episode psychosis (FEP) have neurocognitive impairments. In addition, psychotic patient’s cognitive deficiencies do not decline over the course of the psychotic disorder. However, according to the article
Neurocognitive functioning before and after the first psychotic episode: does psychosis result in cognitive deterioration? (2010)
, the results indicated that there is no decline in cognitive functioning during the first psychotic episode. This indicates a gap in research of the effect psychotic episodes has on cognitive functioning.
Evaluate published research studies on your topic found during your work on the Weeks One, Two, and ...
Narrative review | Prisma systematic review | Medical writingPubrica
At Pubrica, we collect data from a wide range of sources and perform semantic annotation based on the research questions that you wanted to solve. Pubrica has the vast majority of the data in doctor’s notes; electronic medical records, prescriptions, and similar information are available. Although therein lies the golden possibility of big data in medical care, it’s challenging to yield valuable insights due to complex, unstructured, longitudinal, and voluminous data.
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This document discusses research on developing an intelligent depression detection system using natural language processing of social media posts. It summarizes several previous studies that have used Facebook and Twitter data to predict depression by analyzing language and behavior. Specifically, some key studies are highlighted that have successfully predicted depression levels based on survey responses and self-reported diagnoses on social media, with prediction accuracy rates up to 89% in some cases. The document also reviews approaches that have used online forum membership and posts to classify mental health conditions. Overall, the research suggests social media can provide insights into users' mental states and has potential for early detection of depression.
1
Methods and Statistical Analysis
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United State University
Course xxx
Professor xxxx
Date xxx
The Evaluative Criteria
The process of analyzing a healthcare plan to see if it meets its goals takes some time. Because it promotes an evidence-based approach, assessment is crucial in practice consignment. Evaluation can be used to assess the effectiveness of the research. It helps determine what changes could be recommended to improve service delivery and the study's persuasiveness. An impact evaluation analyzes the intervention's direct and indirect, positive and negative, planned and unplanned consequences. If an evaluation fails to deliver fresh recognition regularly, it may result in inaccurate results and conclusions. A healthcare practitioner can utilize the indicators or variables to evaluate programs and determine whether they are legal or not (Dash et al., 2019). The variables are also used to assess if the mediation is on track to meet its objectives and obligations. Participation rates, prevalence, and individual behaviors are among the measures to be addressed.
Individual behaviors are actions taken by individuals to improve their health. People have been denied the assistance and resources they seek because of ethics and plans. In addition, different people have varied perspectives about pressure ulcers treatment. Relevance refers to how the study may contribute to a worthwhile cause (Li et al., 2019). Quality variables give statistics on the precariously rising service consignment while also attempting to provide information on the part of the care that may be changed. The participation rate refers to the total number of people participating in the study.
On the other hand, individuals may be unable to engage in the study due to a lack of cultural knowledge and ineffective consent processes. The overall number of persons in a population who have a health disease at a given time is referred to as prevalence (Li et al., 2019). Although prevalence shows the rate at which new facts arrive, it aids in determining the suitable, complete outcome-positive prestige of people.
Research Approaches
The word "research approaches" refers to techniques and procedures to draw general conclusions concerning data collection, analysis, and explanation methods. In my research, I'll employ both quantitative and qualitative methods. A qualitative research technique will reveal deterrents and hindrances to practicing change by rationalizing the reasons behind specific demeanors (Li et al., 2019). Qualitative research will collect and evaluate non-numerical data to comprehend perspectives or opinions. It will also be utilized to learn everything there is to know about a subject or to develop new research ideologies.
The quantitative method focuses on goal data and statistical or numerical analysis of data collected through a questionnaire. In the healthcare field, quantitative research may develop and execute new or enhanced work meas ...
Guide for conducting meta analysis in health researchYogitha P
This document discusses meta-analysis and its role in evidence-based dentistry. It defines meta-analysis as the statistical analysis and synthesis of data from multiple scientific studies. Meta-analysis enhances the reliability of conclusions by increasing statistical power and limiting bias compared to individual studies. It can help resolve scientific controversies by establishing whether findings are consistent across studies. The document reviews the steps in conducting a meta-analysis, including developing a clear question and protocol, performing comprehensive literature searches, assessing study quality, extracting outcome data, conducting statistical analyses, and drawing conclusions. It also discusses potential biases and strengths and limitations of meta-analysis.
What is machine learning? Machine learning is a branch of artificial intelligence (AI) and computer science which focuses on the use of data and algorithms to imitate the way that humans learn, gradually improving its accuracy.
Rationale: Biostatistics continues to play an essential role in cardiovascular investigations, but successful implementation can be complex.
Objective: To present the rationale behind statistical applications and review useful tools for cardiology research.
Methods and Results: Prospective declaration of the research question, clear methodology, and adherence to protocol serve as the critical foundation. Parametric and distribution-free measures are presented along with t-testing, ANOVA, regression analyses, survival analysis, logistic regression, and interim monitoring. Finally, common weaknesses are considered.
Conclusions: Biostatistics can be productively applied to cardiovascular research if investigators (1) develop and rely on a well-written protocol and analysis plan, (2) consult bi
A systematic review is a literature review focused on answering a specific question by identifying, appraising, selecting, and synthesizing high-quality research evidence relevant to that question. It follows a rigorous methodology to overcome bias, including formulating a research question, conducting a comprehensive literature search, applying inclusion/exclusion criteria, assessing study quality, and analyzing results. The results are often combined using meta-analysis to provide a quantitative summary of effects across multiple studies.
Retrospective versus | Meta analysis | Systematic literature reviewPubrica
Systematic review for prospective studies is a meticulous and essential process ensuring research findings’ reliability and validity. The key to success lies in adhering to a well-structured methodology that includes defining the research question, developing a comprehensive search strategy, screening studies based on pre-defined criteria, and critically appraising the selected articles.
https://pubrica.com/academy/manuscript-editing/conduct-a-systematic-review-for-prospective-studies/
Poster: Test-Retest Reliability and Equivalence of PRO MeasuresCRF Health
This literature review examined administration intervals used in test-retest reliability and equivalence studies for patient-reported outcome measures. The review found a large variance in intervals, ranging from immediate to 7 years for test-retest studies and from immediate to 1 month for equivalence studies. The most common intervals were 2 weeks for test-retest studies and 1 hour or less for equivalence studies. Intervals varied depending on the medical condition and type of study, with shorter intervals used for equivalence studies compared to test-retest studies for the same conditions.
This study conducted a systematic review and meta-analysis of randomized controlled trials on the use of therapeutic clowns in pediatrics. It found 19 eligible studies and included 16 in the meta-analysis. The main findings were:
1) Clown therapy significantly reduced anxiety in children based on a pooled standardized mean difference of -0.83 favoring clown therapy.
2) Clown therapy significantly reduced state anxiety in parents based on a pooled standardized mean difference of -0.46 favoring clown therapy.
3) Clown therapy did not significantly reduce pain levels in children or the need for anxiolytic drugs.
MEASUREMENT AND STATISTICS 1
MEASUREMENT AND STATISTICS 2
Measurement and Statistics
Student Name:
Institution Name:
Instructor Name:
Submission Date:
Introduction
There is excessive use of internet today both by the old and the young. It common to see people spending most of their time behind computers or using their mobile phones browsing the internet and assessing social media. Internet is beneficial for various reasons such as entertainment, social interaction, education and even work. There have been concerns as to whether this excessive use of internet by individuals has a negative impact on their psychological health. Some of the aspects that internet has been seen to affect one’s psychological health include; anxiety, sleep, stress, depression and even social life. Some researchers have conducted studies which link excessive use of internet to extreme cases of low self-esteem and suicidal thoughts. The focus of this paper is to analyze the statistics and measurements that will be used in the research which will be conducted to confirm if excessive internet use has a negative impact on psychological health.
Type of Research
The research to find out if excessive use of internet affects psychological health will be quantitative. This is where the variables will be assigned numerical date in scales and during data collection. After obtaining results from the date collection process, the collected data will be analyzed using various statistical analysis tests. The use of quantitative research in this case will be important to the test in various ways. Quantitative research tends to be more objective and reliable compared to qualitative research (Balnaves, 2001). It is also significant as statistics can be used to conclude a finding. The complex problem in a research study can be restructured and reduced to a smaller number of variables. It also focusses association and relationship between variables and can describe cause and effect easily. After results have been analyzed, quantitative research can easily test hypothesis and theories. It is important through its assumption that a sample represents the whole population. There is less recognition of the researcher’s subjectivity and therefore there is less biasness in this kind of research.
Variables
The qualitative study will be relational as it will analyze the relationship between various variables. There will be collection of data among university students who will be selected randomly and they will fill a questionnaire. The variables in the research study can be generally divided into two categories; internet use and psychological health. The data on internet use will be collected using Online Cognition Scale (OCS) ...
Systematic reviews, rapid reviews, and scoping reviews are all types of literature reviews but differ in their methods and objectives. Systematic reviews have a narrow question and use comprehensive searches and selection criteria to minimize bias. They analyze available studies to answer a specific question. Rapid reviews have time constraints and omit some systematic review stages to provide timely information. Scoping reviews have broader questions and identify the nature and scope of research on a topic, including identifying gaps. They involve iterative searches and selection and usually do not critically appraise studies.
Similar to Validation of Psychometric Properties (20)
2. to provide an overview of the psychometric characteris-
tics of these instruments, the evidence for these charac-
teristics, and an indication of how these findings can be
generalised to various populations.
Methods
This systematic review was conducted in accordance with
the PRISMA Statement [9]. See Additional file 1. The ex-
traction of psychometric data was based on the
COnsensus-based Standards for the selection of health sta-
tus Measurement Instruments (COSMIN) Checklist. [10]
Study selection
We conducted a comprehensive literature search in
PubMed and PsycInfo, which is updated up to January
1st 2014. For the PubMed search we applied a previously
developed search string for psychometric studies [11]
and additional key words to focus on online assessment
and common mental health disorders (Additional file 2).
The PsycInfo search was a translation of the PubMed
search, with additional keywords unique to PsycInfo and
the omission of generic terms such as ‘methods’ and ‘in-
strumentation’, to increase the specificity of the search
(Additional file 2).
Study inclusion
After excluding studies that were not written in English,
studies were included in three a priori defined steps, as
depicted in the flow chart (Fig. 1). The first inclusion
step was to select all studies that applied online self-
report assessments, i.e. data were collected using
internet-connected devices that individuals used to fill in
questions about themselves. We excluded assessments
through stand-alone devices (e.g. in a clinic), or other
self-report measurement within a clinic, in order to re-
tain comparability between results. We also excluded
studies on assessments through unique devices specific-
ally developed for the study, face-to-face interviews con-
ducted by videoconference, and interactive voice
response measures by telephone. As second inclusion
step, we included only those studies that aimed at asses-
sing psychometrics and that provided data of at least
one psychometric variable. The third and final inclusion
step included studies that described instruments for
assessing symptoms of common mental health disorders
[1]. These disorders include ICD-10 [12] and DSM-5
[13] unipolar depressive disorders, social phobia, panic
disorder with or without agoraphobia (PD/A), agorapho-
bia without panic, specific phobia, generalised anxiety
disorder (GAD), post-traumatic stress disorder (PTSD)
and obsessive-compulsive disorder (OCD). We also in-
cluded instruments that assessed specific symptoms of
these disorders or general distress that can accompany
these disorders, i.e. psychological stress (only when un-
related to physical disorders), worrying, suicidal ideation
and self-harm.
Data extraction
First, we coded the data that are relevant for generalising
a study’s findings, which are the sample size, characteris-
tics of the participants (age, gender, disease characteris-
tics), population (e.g. patients or general population),
recruitment method, country in which the study was
conducted, language of the measurement instrument,
Literature search
PubMed: 2644 hits
PsycINFO: 762 hits
2839 papers assessed for eligibility
Excluded:
392 duplicates
145 not in English
30 errata, conference proceedings, etc.
1159 papers applied online measurement
194 papers on psychometrics
56 papers included
62 papers on instruments that measure
depression, anxiety, stress, self-harm, or
suicide ideation
Excluded:
6 papers did not report relevant data
Fig. 1 Flow chart of included studies
van Ballegooijen et al. BMC Psychiatry (2016) 16:45 Page 2 of 12
3. any subgroups the results were reported for, and amount
of missing data. Next, we extracted the psychometric data
provided in the study. The following variables were en-
tered into the tables (Tables 1, 2, 3 and 4; Additional file
3): internal consistency (usually expressed as Cronbach’s
alpha); test-retest reliability (usually kappa); measurement
error; factor structure, including type of analysis (explora-
tory or confirmatory factor analysis, or principal compo-
nent analysis) and model fit or variance explained;
equivalence of paper and online versions of the instru-
ment (usually a correlation); difference in mean scores be-
tween online and paper versions; convergent validity, i.e.
the relation with an instrument that measures the same
construct (usually a correlation); criterion validity in terms
of sensitivity, specificity (for the optimal cut-off point),
Area Under the Receiver Operating Characteristic Curve
(AUC), and other criterion-related outcomes (e.g. kappa);
and responsiveness, i.e. the degree to which the instru-
ment can measure change. These variables were extracted
for each instrument reported in the study. When an in-
strument was investigated in multiple samples, e.g. when
two studies on one instrument were described in one
paper, we listed the sample characteristics and outcomes
for each sample separately.
Criterion validity requires a criterion such as a diagno-
sis that can be objectively measured, but there is no
exact method to ascertain any of the included disorders.
Nevertheless, some psychometric studies do aim to as-
sess criterion validity, and the criterion is established by
an interview conducted face-to-face or by telephone by a
clinician or a trained interviewer. We report these data,
because it is not within the scope of the present review
to discuss the validity of the used criteria.
Quality assessment
Quality assessment was conducted in two ways. First, we
coded variables that affect the generalisability and risk of
bias of the findings, which are sample size, sample char-
acteristics, recruitment method and amount of missing
data. Second, we used the COSMIN Checklist with a 4-
point scale [10, 14–17]. This checklist contains quality
criteria for the psychometric variables described above.
For each variable, a quality score is obtained by taking
the lowest rating of any item in that list of criteria [10].
Double coding
The inclusion process was conducted by two independ-
ent raters (WvB and a research assistant). Differences
Table 1 Transdiagnostic online self-report instruments and the number of studies that report psychometric characteristics (between
parentheses)
Instrument Purpose N
studies
Population
/ setting
Countries in
which the studies
were conducted
Internal
consistency
(alpha)
Test-retest
reliability
Factor
structure
Mean score
difference with
paper version
Convergent
validity
Criterion
validity
(AUC)
Anxiety
BAI Symptom
severity
3 G1, P3 SE .88–.89 (2) 4 factors (1) None, lower (2)
Depression and anxiety
CIDI-SF Diagnosis
&
screening
1 G1 SE (1)
DASS Symptom
severity
1 G5 US .93–.95 (1) (1) (1)
HADS Screening
& symptom
severity
5 G1, G5, P5,
P7
NZ, SE, UK .76–.88 (5) (1) 3 factors (2) None (2) (1)
SQ-48 Screening
& symptom
severity
1 G1, P1 NL .84–.93 (1) 9 factors (1) (1) .75–.91 (1)
WB-DAT Screening 1 P8 CA (1)
WSQ Screening 1 G1 NL .65–.81 (1)
Depression and anxiety
(postpartum)
PDM Screening 1 G1 US .84–.88 (1) 2 factors (1) (1)
G1: General population; G2: Adult females; G3: Adult males; G4: General teenage population; G5: Student population; G6: General young adult population; G7:
Veteran population; P1: Patient population; P2: Adult psychiatric outpatients; P3: Adult social phobia patients; P4: Adult GP patients; P5: Hearing impaired clinical
population; P6: Deaf population; P7: Adults with chronic fatigue syndrome; P8: Participants in studies of the Centre for Addiction and Mental Health; AU: Australia;
CA: Canada; DK: Denmark; ES: Spain; NL: Netherlands; NO: Norway; NZ: New Zealand; SE: Sweden; TW: Taiwan; UK: United Kingdom; US: United States; AUC: Area
Under the Receiver Operating Characteristic Curve
van Ballegooijen et al. BMC Psychiatry (2016) 16:45 Page 3 of 12
4. between raters were solved by discussion and by confer-
ring with the co-authors. Three of the authors of this
study (WvB, JHS, PvO) and three other raters (research
assistants) participated in the data extraction and quality
assessment of the included studies. We double coded all
extracted data, including the four variables that may
affect generalisability and risk of bias. The COSMIN
quality ratings were mostly single coded, where 18 % of
the included studies were double coded.
Data synthesis
All extracted data (Additional file 3) were sorted on dis-
order or symptom and on instrument name, thereby cre-
ating a table of instruments for each disorder (Tables 1,
2, 3 and 4). When a study investigated multiple instru-
ments for multiple disorders (e.g. one instrument that
measures depression and one that measures anxiety), we
reported the instruments in the table for the appropriate
disorder. We created a separate table for instruments
that measured multiple disorders or general symptoms.
It was not possible to synthesise the data in a quantita-
tive analysis, such as a meta-analysis, because the in-
cluded studies investigated a variety of instruments,
applying various methods to obtain psychometric data
and reporting various outcome measures.
Results
Study selection
The PubMed search yielded 2644 results and the Psy-
cINFO search added 370 unique studies (Fig. 1). After
excluding studies that were not in English and search re-
sults that were irrelevant studies, we assessed 2839 stud-
ies for eligibility (Fig. 1). Step one of the inclusion, i.e.
assessment was conducted using online self-report in-
struments, left 1159 studies. Of these, 194 investigated
and reported psychometric data (step 2). Next, we in-
cluded 62 studies that investigated instruments for
assessing common mental health disorders (step 3). Fi-
nally, we excluded 6 studies that did not report psycho-
metric data that were relevant for our overview and
synthesis, so we included 56 studies in our review. See
Fig. 1 for a flow chart.
Study characteristics
The details of the 56 included studies and their results
are presented in Additional file 3. Combined, these stud-
ies described psychometric data for 62 different instru-
ments. These studies and instruments are presented in
Additional file 3. The data are summarised in Tables 1,
2, 3 and 4. The samples of most studies (48 of 56) con-
tained a larger percentage of women (range 0 % to
100 %; Additional file 3). Seven studies included a sam-
ple with an average age below 20. Most studies recruited
their samples from the general population using
advertisements or links on websites (i.e. self-referral).
Also common were studies among university students.
Patient populations were less common, as 14 of the 62
instruments were investigated among patient popula-
tions. See Tables 1, 2, 3 and 4 and Additional file 3. All
56 of the included studies investigated internet-based in-
struments that were completed on a desktop, laptop or
tablet computer, while none of the studies reported that
their instruments were completed on cellular phones or
smartphones.
Outcomes
We found instruments for all of the included mental
health disorders. An average of 2.5 psychometric charac-
teristics were reported for each instrument. None of the
studies reported measurement error or responsiveness of
instruments. We left the empty columns of these two
outcomes in Additional file 3, but omitted them in Ta-
bles 1, 2, 3 and 4. Of the 62 investigated instruments, 29
assessed depressive symptoms. Of these, the CES-D and
the Montgomery–Åsberg Depression Rating Scale Self
Report (MADRS-S) were most frequently studied (6
studies each). Least studied were instruments for meas-
uring suicidal ideation (1 study on 2 single items), self-
harm (1 study) and stress (1 study).
Transdiagnostic online instruments
Seven instruments assessed both depressive and anxiety
symptoms, or anxiety symptoms that apply to several
disorders, such as the Beck Anxiety Inventory (BAI).
These can be roughly divided in short instruments that
screen for disorders, e.g. the Web Screening Question-
naire (WSQ) [18] and the Web-Based Depression and
Anxiety Test (WB-DAT) [8], and scales that assess
symptom severity, e.g. the Hospital Anxiety and Depres-
sion Scale (HADS) [19] and the Depression Anxiety
Stress Scales (DASS) [20]. The short screening question-
naires had poor to adequate criterion validity for screen-
ing individual disorders [8, 18, 21]. Of the symptom
severity scales, the HADS was investigated in 5 studies
[19, 22–25]. These 5 studies showed a fair to good in-
ternal consistency. The online HADS is the only instru-
ment we found that was investigated among several
patient populations [19, 23, 24]. Although the factor
structure may be different from how the measure was
designed [19, 23], there is mounting evidence that sup-
port adequate validity of the online HADS.
Online assessment of depression
Our review includes 29 instruments that measure de-
pressive symptoms. These consist of 22 instruments that
measure depression alone and 7 transdiagnostic instru-
ments. The 22 studies on instruments for depression
generally reported recruiting their samples from the
van Ballegooijen et al. BMC Psychiatry (2016) 16:45 Page 4 of 12
5. Table 2 Online self-report instruments for depression and the number of studies that report psychometric characteristics (between
parentheses)
Instrument Purpose N
studies
Population
/ setting
Countries in
which the
studies were
conducted
Internal
consistency
(alpha)
Test-
retest
reliability
Factor
structure
Mean score
difference
with paper
version
Convergent
validity
Criterion
validity
(AUC)
Depression
BDI Symptom
severity
2 G1 SE .88 (1) 3 factors (1) Higher (1)
BDI-II Symptom
severity
2 G1 SE .87–.95 (2) None, lower
(2)
CES-D Screening
&
symptom
severity
6 G1, G2, G4,
G5
NL, TW, US .89–.93 (5) 2–4 factors (2) None (2) (2) .84–.90 (2)
CES-D (7-item) Symptom
severity
1 G5 ES .82 (1) 1 factors (1) None (1) (1)
CES-D (10-item) Symptom
severity
1 G1 US .86 (1)
CUDOS Screening
&
symptom
severity
1 P2 US .93 (1) None (1) (1)
EDS Screening 1 G1 NL .87 (1) (1)
HSCL-10 Screening 1 G4 DK + NO .87 (1) .79 (1)
ISP-D Screening 2 G1 TW (1) (1)
K-10 Screening
&
symptom
severity
1 G1 NL .90 (1) (1) .81 (1)
K&D mood
scale
Symptom
severity
1 G5 US .75–.79 (1) (1) (1)
MADRS-S Symptom
severity
6 G1, P3 SE .73–.90 (5) 3 factors (1) None (4)
MDI Screening
&
symptoms
severity
1 G4 NL .82 (1) (1) .89 (1)
MDRS-22 Screening
&
symptom
severity
1 G3 AU 6 factors (1) (1)
Moodscope Symptom
severity
1 P4 UK (1)
PDI MDD Diagnosis
&
screening
1 P1 US (1) (1)
PHQ-9 BSL Symptom
severity
1 P6 UK .81 (1) 2 factors (1) (1)
Single item
depr. scale
Screening
&
symptom
severity
1 G1 NL (1) .71 (1)
USDI Symptom
severity
1 G5 AU .95 (1) 3 factors (1) (1)
ZDS Symptom
severity
1 G5 UK .89 (1) (1)
van Ballegooijen et al. BMC Psychiatry (2016) 16:45 Page 5 of 12
6. general population. Five studies investigated instruments
for depression among patient populations [3, 6, 26–28],
each investigating a different instrument.
The full version of the CES-D has been evaluated in 6
studies [5, 29–33], and 5 characteristics were each re-
ported by at least 2 studies (Table 2). Moreover, all 6
studies recruited their samples among non-patients, so
the results can be considered complementary. The in-
ternal consistency was investigated in 5 of these studies,
reporting a Cronbach’s alpha of .89 to .93. Factor ana-
lysis showed that the CES-D consists of 2, 3 or 4 factors
[32, 33]. The 2-factor solution was among an English
speaking population, the 3-factor solution among a
Spanish speaking and the 4-factor solution among a
Chinese speaking population [32, 33]. Adequate psycho-
metric characteristics were found for the CES-D regard-
ing equivalence of mean scores with the paper version
[31, 33], convergent validity [5, 30] and criterion validity
[5, 30]. One study [33] conducted a full measurement in-
variance analysis using confirmatory factor analysis,
comparing paper and online formats, and found only a
negligible difference in the latent mean score of one fac-
tor. Overall, it can be concluded that the online CES-D
has good psychometric characteristics among non-
patient populations, and that a start has been made to
investigate its intercultural validity.
Another commonly investigated instrument was the
MADRS-S [3, 4, 34–37]. Five of these studies reported
Cronbach’s alpha, which is adequate to excellent (.73 to
.90, Table 2) [3, 4, 34–36]. Thorndike and colleagues
[37] found that the scale consists of 3 factors. Four stud-
ies found that the mean score of the MADRS-S does not
differ significantly between the online and the paper ver-
sion [3, 4, 35, 36].
Online assessment of GAD
The GAD-7 and two shorter versions were studied
among a sample recruited from the general population
[38]. The scale showed promising internal consistency,
convergent validity and predictive validity. The psycho-
metrics of the GAD-7 were similar among a population
of people with hearing loss [6].
Online assessment of panic disorder and agoraphobia
Internet interventions for PD/A, such as self-help courses,
have been relatively extensively researched. Therefore,
Austin and colleagues [39] and Carlbring and colleagues
[4] studied the online questionnaires usually employed for
such research. They focussed on equivalence of mean
scores with paper versions of the same instruments. This
equivalence could generally be assumed due to high corre-
lations, but the study of Carlbring [4] found that online
versions yield significantly lower mean scores for the Body
Sensations Questionnaire (BSQ) and Agoraphobic Cogni-
tions Questionnaire (ACQ) and higher scores for the Mo-
bility Inventory (MI) subscale Alone. Finally, an
agoraphobia screening item augmented with images was
found to have adequate criterion validity (AUC .73) [7].
All these studies recruited their samples from the general
population.
Online assessment of social phobia
Two studies [3, 40] independently investigated the
equivalence between online and paper versions of
the online versions of the Social Interaction Anxiety
Scale (SIAS) and Social Phobia Scale (SPS). Both did
not find a difference between formats in mean score,
but the factor structure did differ between formats
[40], indicating that scores cannot be compared
across formats. Adequate to good internal
consistency of these scales has also been found in
three studies [3, 40, 41], and adequate convergent
validity of the SIAS in two [40, 41]. Lindner and col-
leagues revised item 14 of the SIAS, because the ori-
ginal item only applied to heterosexual people. This
change did not alter the internal consistency or con-
vergent validity of the scale [41]. The study of Hed-
man and colleagues [3] recruited people classified
Table 2 Online self-report instruments for depression and the number of studies that report psychometric characteristics (between
parentheses) (Continued)
Postpartum depression
EPDS Screening,
symptom
severity
1 G2 unclear .90 (1) 3 factors (1) (1)
PDSS Screening
&
symptom
severity
1 G2 US .97 (1) (1)
G1: General population; G2: Adult females; G3: Adult males; G4: General teenage population; G5: Student population; G6: General young adult population; G7:
Veteran population; P1: Patient population; P2: Adult psychiatric outpatients; P3: Adult social phobia patients; P4: Adult GP patients; P5: Hearing impaired clinical
population; P6: Deaf population; P7: Adults with chronic fatigue syndrome; P8: Participants in studies of the Centre for Addiction and Mental Health; AU: Australia;
CA: Canada; DK: Denmark; ES: Spain; NL: Netherlands; NO: Norway; NZ: New Zealand; SE: Sweden; TW: Taiwan; UK: United Kingdom; US: United States; AUC: Area
Under the Receiver Operating Characteristic Curve
van Ballegooijen et al. BMC Psychiatry (2016) 16:45 Page 6 of 12
7. Table 3 Online self-report instruments for GAD, panic disorder and agoraphobia, social phobia, specific phobia, OCD and PTSD, and the number of studies that report psychometric
characteristics (between parentheses)
Instrument Purpose N
studies
Population/
setting
Countries in which
the studies were
conducted
Internal consistency
(alpha)
Test-retest
reliability
Factor
structure
Mean score difference
with paper version
Convergent
validity
Criterion
validity (AUC)
GAD
GAD-1 Screening 1 G1 NL .78 (1)
GAD-2 Screening 1 G1 NL .76 (1)
GAD-7 Screening & symptom
severity
1 G1 NL .86 (1) 1 factor (1) (1) .77 (1)
GAD-7 BSL Symptom severity 1 P6 UK .88 (1) 1 factor (1) (1)
PDI GAD Diagnosis & screening 1 P1 US (1) (1)
Panic disorder and
agoraphobia
ACQ Symptom severity 2 G1 AU, SE .81–.84 (2) None, lower (2)
BSQ Symptom severity 2 G1 AU, SE .84–.86 (2) None, lower (2)
CIDI-Panic subscale Diagnosis & screening 1 G1 SE (1)
MI Symptom severity 2 G1 AU, SE .94–.97 (2) None, higher (2)
PDSS-SR item 4 Screening 1 G1 NL .68 (1)
PDSS-SR item 5 Screening 1 G1 NL .61 (1)
VS-CMD-agoraphobia Screening 1 G1 NL .73 (1)
Social phobia
LSAS-SR Symptom severity 2 G1, P3 SE .93–.94 (2) None (1)
SIAS Symptom severity 3 G5, P3 SE, US .86–.93 (3) 1 factor (1) None (2) (2)
SPIN Symptom severity 1 G1 TW .94 (1) (1) 3 factors (1) .87 (1)
SPS Symptom severity 2 G5, P3 SE, US .89–.93 (2) 1 factor (1) None (2) (1)
Specific phobia
(aviophobia)
FAS Screening & symptom
severity
1 G1 + G5 US .94–.99 (1) .99 (1)
OCD
C-FOCI Screening 1 G4 US .73 (1)
OBQ-44 Symptom severity 1 G5 US .97 (1) None (1) (1)
OCI Symptom severity 1 G5 US .94 (1) None (1) (1)
PI Symptom severity 1 G1 US (mainly) 4 factors (1)
vanBallegooijenetal.BMCPsychiatry(2016)16:45Page7of12
8. Table 3 Online self-report instruments for GAD, panic disorder and agoraphobia, social phobia, specific phobia, OCD and PTSD, and the number of studies that report psychometric
characteristics (between parentheses) (Continued)
PTSD
NSES Diagnosis & symptom
severity
1 G1, G7 US 4 factors (1)
PCL-C Symptom severity 1 G5 US .91 (1) None (1)
PSS Screening & symptom
severity
1 G5 US .92–.94 (1) 4 factors (1) (1)
TSS Symptom severity 1 G5 US .95–.96 (1) None (1)
PTSD (peripartum)
PPQ (modified) Screening & symptom
severity
1 G2 US .90 (1) 3 factors (1) (1) (1)
G1: General population; G2: Adult females; G3: Adult males; G4: General teenage population; G5: Student population; G6: General young adult population; G7: Veteran population; P1: Patient population; P2: Adult
psychiatric outpatients; P3: Adult social phobia patients; P4: Adult GP patients; P5: Hearing impaired clinical population; P6: Deaf population; P7: Adults with chronic fatigue syndrome; P8: Participants in studies of the
Centre for Addiction and Mental Health; AU: Australia; CA: Canada; DK: Denmark; ES: Spain; NL: Netherlands; NO: Norway; NZ: New Zealand; SE: Sweden; TW: Taiwan; UK: United Kingdom; US: United States; AUC: Area
Under the Receiver Operating Characteristic Curve
vanBallegooijenetal.BMCPsychiatry(2016)16:45Page8of12
9. with social phobia, but more research among patient
groups is recommended.
Online assessment of specific phobia
Two of the transdiagnostic screening measures [18, 21]
included specific phobia. These showed poor criterion
validity for specific phobia. One instrument, the Flight
Anxiety Situations Questionnaire (FAS), has been stud-
ied for aviophobia [42]. This study showed near perfect
criterion validity (AUC .99). Considering aviophobia is
only one of many different specific phobias, much more
development is needed in this area.
Online assessment of OCD
Four instruments for OCD have been studied, all in the
US and among the general population [43–45]. Each in-
strument was studied only once. Williams and col-
leagues [45] investigated differential item functioning
between black and white Americans, finding significant
differences for the Padua Inventory (PI). Next to these 4
instruments, the WSQ [18] and the CIDI-SF [21] also
screen for OCD.
Online assessment of PTSD
Like instruments for OCD, 4 instruments for PTSD have
been studied, all in the US and among the general popu-
lation [31, 46–48]. The transdiagnostic WSQ [18] also
screens for PTSD. One additional study investigated an
instrument for perinatal PTSD [49]. Miller and col-
leagues [47] checked the factor structure of their meas-
ure for PTSD (National Stressful Events Survey) using
item-response theory. The factor structure was con-
firmed, but the items of the instrument may cover too
narrow a range of the latent factors.
Online assessment of worry and stress
The PSWQ, assessing worry, was studied twice [20, 50].
These studies found slightly differing values for internal
consistency (.73 and .88). We found one study on an in-
strument that assesses stress [51].
Online assessment of suicidal ideation and self-harm
We found one study on an instrument that assesses self-
harm. [52] This study used Rasch analysis to further con-
firm the factors of the Inventory of Statements About
Self-injury (ISAS), obtained by factor analysis, and their
unidimensionality. Furthermore, we found two single-item
measures for suicidal ideation, being item 9 of the BDI-II
and item 9 of the MADRS-S [36]. Item 9 of the online
BDI-II yielded lower scores than item 9 of the paper ver-
sion of the BDI-II [36]. The WSQ [18] also contains an
item that screens for suicidal ideation, but the validity of
this item was not investigated (also see [53]).
Table 4 Online self-report instruments for stress, worrying, suicidal ideation and self-harm, and the number of studies that report
psychometric characteristics (between parentheses)
Instrument Purpose N
studies
Population/
setting
Countries in which
the studies were
conducted
Internal
consistency
(alpha)
Test-retest
reliability
Factor
structure
Mean score
difference with
paper version
Convergent
validity
Criterion
validity
(AUC)
Stress
PSS Symptom
severity
1 G5 ES .72 (1) 1 factor (1) None/lower
(1)
(1)
Worry
PSWQ Symptom
severity
2 G1, G5 NL, US .73–.88 (2) (1) 1 factor (1) (1)
Worry
(postpartum)
PWS-R Symptom
severity
1 G2 US .64–.88 (1) 4 factors (1) (1)
Self-harm
ISAS Symptom
severity
1 G6 US + UK + CA +
AU
.77–.87 (1) 2 factors (1) (1)
Suicidal ideation
BDI-II item 9 Screening 1 G1 SE Lower (1)
MADRS-S item 9 Screening 1 G1 SE None (1)
G1: General population; G2: Adult females; G3: Adult males; G4: General teenage population; G5: Student population; G6: General young adult population; G7:
Veteran population; P1: Patient population; P2: Adult psychiatric outpatients; P3: Adult social phobia patients; P4: Adult GP patients; P5: Hearing impaired clinical
population; P6: Deaf population; P7: Adults with chronic fatigue syndrome; P8: Participants in studies of the Centre for Addiction and Mental Health; AU: Australia;
CA: Canada; DK: Denmark; ES: Spain; NL: Netherlands; NO: Norway; NZ: New Zealand; SE: Sweden; TW: Taiwan; UK: United Kingdom; US: United States; AUC: Area
Under the Receiver Operating Characteristic Curve
van Ballegooijen et al. BMC Psychiatry (2016) 16:45 Page 9 of 12
10. Generalisability and risk of bias
The sample sizes of the included studies were generally
adequate for analysing psychometric properties. Nine
studies contained over 1000 participants. The other
studies in the tables (n = 46) had an average sample size
of 261 participants. A sample size below 100 was found
in 10 studies, which generally gives too little statistical
power for psychometric analyses [54]. It should be noted
that required sample sizes differ per number of items
and type of analysis. Most results could be biased due to
selectively missing data. Two studies reported missing
data and included numbers. In 33 studies, the amount of
missing data was not specifically reported, but could be
deduced or estimated. Missing data were not reported
by or could not be deduced in 21 studies (see Additional
file 3). Overall, COSMIN quality ratings of ‘Excellent’
were rare and ‘Poor’, ‘Fair’ and ‘Good’ ratings were
equally common. Instead of adding the COSMIN ratings
to the tables and Additional file 3, we decided to report
the characteristics the ratings are based on, because the
ratings do not always do justice to a study’s quality. The
study characteristics give an objective and interpretable
indication of the robustness and generalisability of a
study’s findings. Lastly, 47 of the 62 instruments were
investigated in only one study (Tables 1, 2, 3 and 4), so
the robustness of the psychometric properties of these
instruments relies heavily on the aspects of the individ-
ual studies and cannot be easily generalised to other
populations or settings.
Discussion
This review systematically studied the scientific literature
on the psychometrics of online instruments that meas-
ure common mental health disorders. We report charac-
teristics of 62 instruments. Most of these instruments
were investigated among samples recruited from the
general population. We found at least one online meas-
ure for each of the included mental health disorders and
symptoms. The results are scattered over different in-
struments and different characteristics and, therefore,
can be synthesised for only a few instruments. We found
few instruments that measure specific phobia, stress,
worry self-harm and suicidal ideation. There were no
studies that reported that the questionnaires were com-
pleted on cellular phones or smartphones.
The CES-D is the most well-studied online instrument
and there is evidence for adequate psychometric proper-
ties among samples recruited from the general popula-
tion. The MADRS-S has been well-studied as well,
mostly showing mean score equivalence between online
and paper versions. Finally, the HADS is the only instru-
ment that was investigated among both the general
population and two patient populations, showing ad-
equate psychometric properties.
Ideally, two or more online instruments would be avail-
able for each disorder, with all of their characteristics ex-
amined in several studies, among various populations.
There are clear gaps in the tables presented in this study,
which warrant further research and development. The
psychometric properties measurement error and respon-
siveness were not reported for any instrument. Further-
more, while there is an abundance of online instruments
for depressive symptoms, there is a shortage of instru-
ments for other disorders. Although a few new instru-
ments have been developed in the meantime, e.g. for
suicidal ideation [55], more instruments are needed.
Equivalence between paper and online versions of an in-
strument has mostly been studied in the form of equiva-
lence of mean scores by correlations and t-tests. We can
conclude that correlations are high and differences are
small. However, mean score equivalence is only one aspect
of measurement invariance. Two studies conducted a
broad range of measurement invariance tests [33, 40].
While Yu and Yu [33] found only a negligible difference in
the mean score of the somatic factor of the CES-D, Hirai
and colleagues [40] found that factor structures of the
SIDAS and SPS differ between formats. Differing factor
structures indicate that different constructs are assessed
and scores cannot be compared across formats. It is im-
portant to note that possibly not only the format differs
between paper and online versions, but the setting as well.
Online questionnaires can be completed at the partici-
pant’s home on a device (s) he is familiar with. In the study
of Yu and Yu [33], participants completed the paper ques-
tionnaires at home, while in the study of Hirai and col-
leagues [40], participants completed the paper
questionnaires in a lab. It is recommended to study inter-
format equivalence in one setting, and to use a broad
range of measurement invariance aspects, e.g. using
multiple-group confirmatory factor analysis [56].
This systematic review has some limitations. Firstly, we
may not have included all studies on psychometrics of on-
line instruments for common mental health disorders, be-
cause there may be studies that applied online assessment
without mentioning it in the title or abstract. Online assess-
ment is increasingly common and increasingly less import-
ant to mention. Secondly, we decided not to label the
quality of the included studies, even though a quality as-
sessment is common practice in systematic reviews. Be-
cause psychometric properties are dependent on study
characteristics, it is more insightful to inspect these charac-
teristics in order to decide whether an instrument has been
investigated well enough for the purpose, population and
setting one wants to use it for. Thirdly, our search has been
updated up to January 1st
2014 and several psychometric
studies on online instruments have been published since.
Finally, our search strings (Additional file 2) can be made
more comprehensive by adding ‘distress’, ‘mhealth’ and
van Ballegooijen et al. BMC Psychiatry (2016) 16:45 Page 10 of 12
11. ‘response processes’. The omission of these terms have not
impacted our results, however.
Future psychometric studies are encouraged to investi-
gate and explore different devices, formats and media.
Only one study in our review [37] investigated the effects
of different formats of online questionnaires and the pref-
erences of the participants. An instrument’s format, e.g.
the layout, design, font type and the number of items per
page, interacts with its content and with the characteris-
tics of the individual who completes the items. [57] Differ-
ent formats could also include other media than text, such
as audio, images and video, see e.g. [6] and [7]. Another
area to explore is measurement by smartphones, which
we did not encounter in the included studies. The validity
of measurement by smartphone applications has been
studied in other fields, such as psychotic symptoms. [58]
An advantage of measurement by smartphones is that it
enables momentary assessment, opposed to retrospective
assessment, because an individual can have access to his/
her smartphone all day long.
Conclusions
We found at least one online measure for each of
the included mental health disorders and symptoms,
and there is mounting evidence for adequate psycho-
metric properties of common instruments such as
the CES-D, MADRS-S and HADS. Overall, the re-
sults are scattered over different instruments and
different characteristics, and much work still has to
be done in this field. With this systematic review we
provide a framework for future research into psycho-
metrics of online instruments. Furthermore, our
overview of instruments can guide professionals
when choosing an instrument for assessing common
mental health disorders online. The tables (Add-
itional file 3) provided with this systematic review
are free to use and expand. We encourage re-
searchers to fill in the missing data and to add in-
novative instruments.
Additional files
Additional file 1: PRISMA Checklist. (DOC 60 kb)
Additional file 2: Search strings. (DOCX 18 kb)
Additional file 3: Included studies and extracted data. (XLS 117 kb)
Competing interests
The authors declare that they have no competing interests.
Authors’ contributions
WvB, HR and JHS designed the study. WvB conducted the literature searches.
WvB, JHS and PvO extracted the data. WvB drafted the manuscript. WvB, HR,
PC, PvO and JHS critically revised the manuscript and approved the final
version.
Acknowledgements
The authors would like to thank the research assistants who helped with
study inclusion and data extraction: Sumeyye Pinar, Chrysanthi Karampetsi,
Lisa Hegelmaier and Stefania Vacaru. No external funding sources were
involved in this study.
Author details
1
Department of Psychiatry, VU Medical Centre / GGZ inGeest, Amsterdam,
Netherlands. 2
Department of Clinical Psychology, Vrije Universiteit
Amsterdam, Amsterdam, Netherlands. 3
EMGO Institute for Health and Care
Research, VU Medical Centre, Amsterdam, Netherlands.
Received: 16 April 2015 Accepted: 4 February 2016
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