The Influence of Cognitive Biases on Psychophysiological Vulnerability To
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(HCW) Surveys in Humanitarian Contexts in Lmics
Analytics for Operations working group GUIDANCE BRIEF Guidance for Health Care Worker (HCW) Surveys in humanitarian contexts in LMICs Developed by the Analytics for Operations Working Group to support those working with communities and healthcare workers in humanitarian and emergency contexts. This document has been developed for response actors working in humanitarian contexts who seek rapid approaches to gathering evidence about the experience of healthcare workers, and the communities of which they are a part. Understanding healthcare worker experience is critical to inform and guide humanitarian programming and effective strategies to promote IPC, identify psychosocial support needs. This evidence also informs humanitarian programming that interacts with HCWs and facilities such as nutrition, health reinforcement, communication, SGBV and gender. In low- and middle-income countries (LMIC), healthcare workers (HCW) are often faced with limited resources, equipment, performance support and even formal training to provide the life-saving work expected of them. In humanitarian contexts1, where human resources are also scarce, HCWs may comprise formally trained doctors, nurses, pharmacists, dentists, allied health professionals etc. as well as community members who perform formal health worker related duties with little or no trainingi. These HCWs frequently work in contexts of multiple public health crises, including COVID-19. Their work will be affected by availability of resources (limited supplies, materials), behaviour and emotion (fear), flows of (mis)information (e.g. understanding of expected infection prevention and control (IPC) measures) or services (healthcare policies, services and use). Multiple factors can therefore impact patients, HCWs and their families, not only in terms of risk of exposure to COVID-19, but secondary health, socio-economic and psycho-social risks, as well as constraints that interrupt or hinder healthcare provision such as physical distancing practices. -
Guidance for Health Care Worker (HCW) Surveys in Humanitarian
Analytics for Operations & COVID-19 Research Roadmap Social Science working groups GUIDANCE BRIEF Guidance for Health Care Worker (HCW) Surveys in humanitarian contexts in LMICs Developed by the Analytics for Operations & COVID-19 Research Roadmap Social Science working groups to support those working with communities and healthcare workers in humanitarian and emergency contexts. This document has been developed for response actors working in humanitarian contexts who seek rapid approaches to gathering evidence about the experience of healthcare workers, and the communities of which they are a part. Understanding healthcare worker experience is critical to inform and guide humanitarian programming and effective strategies to promote IPC, identify psychosocial support needs. This evidence also informs humanitarian programming that interacts with HCWs and facilities such as nutrition, health reinforcement, communication, SGBV and gender. In low- and middle-income countries (LMIC), healthcare workers (HCW) are often faced with limited resources, equipment, performance support and even formal training to provide the life-saving work expected of them. In humanitarian contexts1, where human resources are also scarce, HCWs may comprise formally trained doctors, nurses, pharmacists, dentists, allied health professionals etc. as well as community members who perform formal health worker related duties with little or no trainingi. These HCWs frequently work in contexts of multiple public health crises, including COVID-19. Their work will be affected -
Using a Smartphone App to Reduce Cognitive
Giosan et al. Trials (2016) 17:609 DOI 10.1186/s13063-016-1740-3 STUDYPROTOCOL Open Access Using a smartphone app to reduce cognitive vulnerability and mild depressive symptoms: Study protocol of an exploratory randomized controlled trial Cezar Giosan1,2,3* ,OanaCobeanu1, Cristina Mogoaşe1, Aurora Szentágotai Tătar1,VladMureşan1 and Rareș Boian1 Abstract Background: Depression is a major challenge worldwide, with significant increasing personal, economic, and societal costs. Although empirically supported treatments have been developed, they are not always available for patients in routine clinical care. Therefore, we need effective and widely accessible strategies to prevent the onset of the very first depressive symptoms. Mental health apps could prove a valuable solution for this desideratum. Although preliminary research has indicated that such apps can be useful in treating depression, no study has attempted to test their utility in preventing depressive symptoms. The aim of this exploratory study is to contrast the efficacy of a smartphone app in reducing cognitive vulnerability and mild depressive symptoms, as risk factors for the onset of depression, against a wait-list condition. More specifically, we aim to test an app designed to (1) decrease general cognitive vulnerability and (2) promote engagement in protective, adaptive activities, while (3) counteracting (through gamification and customization) the tendency of premature dropout from intervention. Methods/design: Romanian-speaking adults (18 years and older) with access to a computer and the Internet and who own a smartphone are included in the study. Two parallel randomized clinical trials are conducted: in the first one, 50 participants free of depressive symptoms (i.e., who obtain scores ≤4 on the Patient Health Questionnaire, PHQ-9) will be included, while in the second one 50 participants with minimal depressive symptoms (i.e., who obtain PHQ-9 scores between 5 and 9) will be included. -
Report on the First Workshop on Bias in Automatic Knowledge Graph Construction at AKBC 2020
EVENT REPORT Report on the First Workshop on Bias in Automatic Knowledge Graph Construction at AKBC 2020 Tara Safavi Edgar Meij Fatma Ozcan¨ University of Michigan Bloomberg Google [email protected] [email protected] [email protected] Miriam Redi Gianluca Demartini Wikimedia Foundation University of Queensland [email protected] [email protected] Chenyan Xiong Microsoft Research [email protected] Abstract We report on the First Workshop on Bias in Automatic Knowledge Graph Construction (KG- BIAS), which was co-located with the Automated Knowledge Base Construction (AKBC) 2020 conference. Identifying and possibly remediating any sort of bias in knowledge graphs, or in the methods used to construct or query them, has clear implications for downstream systems accessing and using the information in such graphs. However, this topic remains relatively unstudied, so our main aim for organizing this workshop was to bring together a group of people from a variety of backgrounds with an interest in the topic, in order to arrive at a shared definition and roadmap for the future. Through a program that included two keynotes, an invited paper, three peer-reviewed full papers, and a plenary discussion, we have made initial inroads towards a common understanding and shared research agenda for this timely and important topic. 1 Introduction Knowledge graphs (KGs) store human knowledge about the world in structured relational form. Because KGs serve as important sources of machine-readable relational knowledge, extensive re- search efforts have gone into constructing and utilizing knowledge graphs in various areas of artificial intelligence over the past decade [Nickel et al., 2015; Xiong et al., 2017; Dietz et al., 2018; Voskarides et al., 2018; Shinavier et al., 2019; Safavi and Koutra, 2020]. -
1 Encoding Information Bias in Causal Diagrams Eyal Shahar, MD, MPH
* Manuscript Encoding information bias in causal diagrams Eyal Shahar, MD, MPH Address: Eyal Shahar, MD, MPH Professor Division of Epidemiology and Biostatistics Mel and Enid Zuckerman College of Public Health The University of Arizona 1295 N. Martin Ave. Tucson, AZ 85724 Email: [email protected] Phone: 520-626-8025 Fax: 520-626-2767 1 Abstract Background: Epidemiologists usually classify bias into three main categories: confounding, selection bias, and information bias. Previous authors have described the first two categories in the logic and notation of causal diagrams, formally known as directed acyclic graphs (DAG). Methods: I examine common types of information bias—disease related and exposure related—from the perspective of causal diagrams. Results: Disease or exposure information bias always involves the use of an effect of the variable of interest –specifically, an effect of true disease status or an effect of true exposure status. The bias typically arises from a causal or an associational path of no interest to the researchers. In certain situations, it may be possible to prevent or remove some of the bias analytically. Conclusions: Common types of information bias, just like confounding and selection bias, have a clear and helpful representation within the framework of causal diagrams. Key words: Information bias, Surrogate variables, Causal diagram 2 Introduction Epidemiologists usually classify bias into three main categories: confounding bias, selection bias, and information (measurement) bias. Previous authors have described the first two categories in the logic and notation of directed acyclic graphs (DAG).1, 2 Briefly, confounding bias arises from a common cause of the exposure and the disease, whereas selection bias arises from conditioning on a common effect (a collider) by unnecessary restriction, stratification, or “statistical adjustment”. -
Disner-Dissertation
THE DISSERTATION COMMITTEE FOR SETH GORDON DISNER CERTIFIES THAT THIS IS THE APPROVED VERSION OF THE FOLLOWING DISSERTATION: A TRANSLATIONAL APPROACH FOR MODELING THE COGNITIVE SUBSTRATES OF DEPRESSION: IDENTIFYING BIOLOGICAL PREDICTORS AND NEUROCOGNITIVE INTERVENTIONS FOR BIASED COGNITION Committee: ________________________________ Christopher Beevers, Supervisor ________________________________ Francisco Gonzalez-Lima ________________________________ Stephanie S. Rude ________________________________ David Schnyer ________________________________ Michael Telch A TRANSLATIONAL APPROACH FOR MODELING THE COGNITIVE SUBSTRATES OF DEPRESSION: IDENTIFYING BIOLOGICAL PREDICTORS AND NEUROCOGNITIVE INTERVENTIONS FOR BIASED COGNITION by Seth Gordon Disner, BA DISSERTATION Presented to the Faculty of the Graduate School of The University of Texas at Austin in Partial Fulfillment of the Requirements for the Degree of DOCTOR OF PHILOSOPHY THE UNIVERSITY OF TEXAS AT AUSTIN August, 2015 Dedication For MLG, SFD, & EGD A TRANSLATIONAL APPROACH FOR MODELING THE COGNITIVE SUBSTRATES OF DEPRESSION: IDENTIFYING BIOLOGICAL PREDICTORS AND NEUROCOGNITIVE INTERVENTIONS FOR BIASED COGNITION Publication No. _______________ Seth Gordon Disner, Ph.D. The University of Texas at Austin, 2015 Supervisor: Christopher G. Beevers Abstract: Major Depressive Disorder (MDD) is a pervasive, debilitating condition that affects roughly 16% of Americans in their lifetime. However, treatments for MDD are considered adequate in only 21% of cases. Although biological and cognitive -
Building Representative Corpora from Illiterate Communities: a Review of Challenges and Mitigation Strategies for Developing Countries
Building Representative Corpora from Illiterate Communities: A Review of Challenges and Mitigation Strategies for Developing Countries Stephanie Hirmer1, Alycia Leonard1, Josephine Tumwesige2, Costanza Conforti2;3 1Energy and Power Group, University of Oxford 2Rural Senses Ltd. 3Language Technology Lab, University of Cambridge [email protected] Abstract areas where the bulk of the population lives (Roser Most well-established data collection methods and Ortiz-Ospina(2016), Figure1). As a conse- currently adopted in NLP depend on the as- quence, common data collection techniques – de- sumption of speaker literacy. Consequently, signed for use in HICs – fail to capture data from a the collected corpora largely fail to repre- vast portion of the population when applied to LICs. sent swathes of the global population, which Such techniques include, for example, crowdsourc- tend to be some of the most vulnerable and ing (Packham, 2016), scraping social media (Le marginalised people in society, and often live et al., 2016) or other websites (Roy et al., 2020), in rural developing areas. Such underrepre- sented groups are thus not only ignored when collecting articles from local newspapers (Mari- making modeling and system design decisions, vate et al., 2020), or interviewing experts from in- but also prevented from benefiting from de- ternational organizations (Friedman et al., 2017). velopment outcomes achieved through data- While these techniques are important to easily build driven NLP. This paper aims to address the large corpora, they implicitly rely on the above- under-representation of illiterate communities mentioned assumptions (i.e. internet access and in NLP corpora: we identify potential biases literacy), and might result in demographic misrep- and ethical issues that might arise when col- resentation (Hovy and Spruit, 2016). -
Self-Serving Assessments of Fairness and Pretrial Bargaining
SELF-SERVING ASSESSMENTS OF FAIRNESS AND PRETRIAL BARGAINING GEORGE LOEWENSTEIN, SAMUEL ISSACHAROFF, COLIN CAMERER, and LINDA BABCOCK* In life it is hard enough to see another person's view of things; in a law suit it is impossible. [JANET MALCOM, The Journalist and the Murderer, 1990] I. INTRODUCTION A PERSISTENTLY troubling question in the legal-economic literature is why cases proceed to trial. Litigation is a negative-sum proposition for the litigants-the longer the process continues, the lower their aggregate wealth. Although civil litigation is resolved by settlement in an estimated 95 percent of all disputes, what accounts for the failure of the remaining 5 percent to settle prior to trial? The standard economic model of legal disputes posits that settlement occurs when there exists a postively valued settlement zone-a range of transfer amounts from defendant to plaintiff that leave both parties better off than they would be if they went to trial. The location of the settlement zone depends on three factors: the parties' probability distributions of award amounts, the litigation costs they face, and their risk preferences. * Loewenstein is Professor of Economics, Carnegie Mellon University; Issacharoff is Assistant Professor of Law, University of Texas at Austin School of Law; Camerer is Professor of Organizational Behavior and Strategy, University of Chicago Graduate School of Business; Babcock is Assistant Professor of Economics, the Heinz School, Carnegie Mellon University. We thank Douglas Laycock for his helpful comments. Jodi Gillis, Na- than Johnson, Ruth Silverman, and Arlene Simon provided valuable research assistance. Loewenstein's research was supported by the Russell Sage Foundation. -
Recall Bias: a Proposal for Assessment and Control
International Journal of Epidemiology Vol. 16, No. 2 ©International Epidemiological Association 1987 Printed in Great Britain Recall Bias: A Proposal for Assessment and Control KAREN RAPHAEL Probably every major text on epidemiology offers at Whether the source of the bias is underreporting of least some discussion of recall bias in retrospective true exposures in controls or overreporting of true research. A potential for recall bias exists whenever exposures in cases, the net effect of the bias is to historical self-report information is elicited from exaggerate the magnitude of the difference between respondents. Thus-, the potential for its occurrence is cases and controls in reported rates of exposure to risk greatest in case-control studies or cross-sectional factors under investigation. Consequently, recall bias studies which include retrospective components. leads to an inflation of the odds ratio. It leads to the Downloaded from Recall bias is said to occur when accuracy of recall likelihood thai-significant research findings based upon regarding prior exposures is different for cases versus retrospective data can be interpreted in terms of a controls. methodological artefact rather than substantive The current paper will describe the process and con- theory. sequences of recall bias. Most critically, it will suggest methods for assessment and adjustment of its effects. It is important to note that recall bias is not equiva- http://ije.oxfordjournals.org/ lent to memory failure itself. If memory failure regard- ing prior events is equal in case and control groups, WHAT IS RECALL BIAS? recall bias will not occur. Rather, memory failure itself Generally, recall bias has been described in terms of will lead to measurement error which, in turn, will 'embroidery' of personal history by those respondents usually lead to a loss of statistical power. -
A Framework to Address Cognitive Biases of Climate Change Jiaying
1 [in press at Neuron] A framework to address cognitive biases of climate change Jiaying Zhao1,2 and Yu Luo1 1Department of Psychology, University of British Columbia 2Institute for Resources, Environment and Sustainability, University of British Columbia Please address correspondence to: Jiaying Zhao Department of Psychology Institute for Resources, Environment and Sustainability University of British Columbia Vancouver, B.C., Canada, V6T 1Z4 Email: [email protected] 2 Abstract We propose a framework that outlines several predominant cognitive biases of climate change, identifies potential causes, and proposes debiasing tools, with the ultimate goal of depolarizing climate beliefs and promoting actions to mitigate climate change. Keywords: decision making, cognition, behavior change, polarization, debias 3 Introduction Climate change is an urgent crisis facing humanity. To effectively combat climate change, we need concerted efforts not only from policymakers and industry leaders to institute top-down structural changes (e.g., policies, infrastructure) but also from individuals and communities to instigate bottom-up behavior changes to collectively reduce greenhouse gas emissions. To this end, psychology has offered useful insights on what motivates people to act on climate change, from social psychology investigating the underlying beliefs and attitudes of climate change, to cognitive psychology uncovering the attentional, perceptual, and decision processes of climate actions, and more recently neuroscience pinpointing the neural circuitry on motivated cognition. Insights gained from these fields have started to generate interventions to shift beliefs and promote behavior change to mitigate climate change. In the search for behavioral climate solutions, a stubborn challenge remains. That is, a sizeable portion of the public still hold disbelief on climate change and refuse to take actions, despite the fact that the vast majority of climate scientists have reached a consensus on anthropogenic causes of climate change. -
The Role of Cognitive Vulnerability in Emersion of Fear
J. Basic. Appl. Sci. Res., 2(8)8196-8202, 2012 ISSN 2090-4304 Journal of Basic and Applied © 2012, TextRoad Publication Scientific Research www.textroad.com The Role of Cognitive Vulnerability in Emersion of Fear Ali Asghar Cheginia, M.A Mohammad Reza Jalalia, phD Gholam Reza Saramib, phD Mohsen siavashic, M.A aDepartment of psychology, Imam Khomeini International University,Qazvin,Iran bDepartment of psychology, Allame Tabatabaei University, Tehran, Iran cDepartment of psychology, Azad University, Arak, Iran ABSTRACT This study is done in order to evaluate the fears of boys and girls and study the role of cognitive vulnerability in fear acquisition. Methodological approach was an ex post facto and causal-comparative type. The sample was 300 high school students. Fear and cognitive vulnerability was evaluated with FSSC-R and CVS. Results of t-test show that there is a significant difference between fears of boys and girls (P<0. 01) and the most current fears of boy and girl students were: Failing a test, Deep water or the ocean, beetle, Not being able to breathe, Getting a cut or injury, Cemeteries, Germs or getting a serious illness, Falling from high places, Bombing attacks, Getting a shock from electricity and Earthquakes. As well analyses of variance and regression recommend that cognitive vulnerability and its factors (uncontrollability, unpredictability, disgusting and dangerousness) have significant effect on fear acquisition (P<0.001). The result of this study is that there is a large difference between fear of boys and girls. And a greater effect on fear for disgusting compared to other factors. KEYWORDS: Danger; Controllability; Fear; Disgust; Predictability. -
Investigating the Role of Cognitive Biases As a Risk Factor for Depression
Loyola University Chicago Loyola eCommons Dissertations Theses and Dissertations 2015 Investigating the Role of Cognitive Biases as a Risk Factor for Depression Daniel Aaron Dickson Loyola University Chicago Follow this and additional works at: https://ecommons.luc.edu/luc_diss Part of the Clinical Psychology Commons Recommended Citation Dickson, Daniel Aaron, "Investigating the Role of Cognitive Biases as a Risk Factor for Depression" (2015). Dissertations. 1471. https://ecommons.luc.edu/luc_diss/1471 This Dissertation is brought to you for free and open access by the Theses and Dissertations at Loyola eCommons. It has been accepted for inclusion in Dissertations by an authorized administrator of Loyola eCommons. For more information, please contact [email protected]. This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 License. Copyright © 2015 Daniel Aaron Dickson LOYOLA UNIVERSITY CHICAGO INVESTIGATING THE ROLE OF COGNITIVE BIASES AS A RISK FACTOR FOR DEPRESSION A DISSERTATION SUBMITTED TO THE FACULTY OF THE GRADUATE SCHOOL IN CANDIDACY FOR THE DEGREE OF DOCTOR OF PHILOSOPHY PROGRAM IN CLINICAL PSYCHOLOGY BY DANIEL A. DICKSON CHICAGO, IL MAY, 2015 Copyright by Daniel A. Dickson, 2015 All rights reserved. ACKNOWLEDGMENTS I am immeasurably grateful for the support, guidance, and hard work of Dr. Rebecca Silton throughout every stage of this project. Without your care and devotion, this entire project would not have been completed. This project also benefited from the incredibly thoughtful, constructive feedback of Dr. Robin Nusslock, Dr. Scott Leon, and Dr. Fred Bryant. I would also like to thank Devin Carey, Catherine Lee, Alisha Miller, Edna Romero, Lea Travers, and Mandy Ward for their support throughout graduate school.