Notes on Psychoanalysis and Technology,the Psyche and the Social

Whatever difficulty might arise in discussing the relationship of psychoanalysis and technology, the discussion inevitably will touch on another relationship that is difficult to discuss: that between the psyche and the social. There will be no easy way to simply establish the distinctiveness of or the relationship between the terms. They are so profoundly interimplicated in each other that some approach other than “establishing a relationship” is needed. This essay pursues this relationship via a discussion of psychic mechanism with attention to historical shifts in media and social technologies of control.     

COVID Health Structure Index: The Vulnerability of Brazilian Microregions

Social Indicators Research - Many developing countries have highly unequal health systems across their regions. The pandemic of COVID-19 brought an additional challenge, as hospital structures...     

Social network analysis for assessing college-aged adults' health: A systematic review

Objective: Social network analysis (SNA) is a useful, emerging method for studying health. College students are especially prone to social influence when it comes to health. This review aimed to identify network variables related to college student health and determine how SNA was used in the literature. Participants: A systematic review of relevant literature was conducted in October 2015. Methods: Studies employing egocentric or whole network analysis to study college student health were included. We used Garrard's Matrix Method to extract data from reviewed articles (n = 15). Results: Drinking, smoking, aggression, homesickness, and stress were predicted by network variables in the reviewed literature. Methodological inconsistencies concerning boundary specification, data collection, nomination limits, and statistical analyses were revealed across studies. Conclusions: Results show the consistent relationship between network variables and college health outcomes, justifying further use of SNA to research college health. Suggestions and considerations for future use of SNA are provided.     

Risk Management Solutions for Climate Change–Induced Disasters

In honor of the 40th anniversary of Risk Analysis, this article suggests ways of linking risk assessment and risk perception in developing risk management strategies that have a good chance of being implemented, focusing on the problem of reducing losses from natural hazards in the face of climate change. Following a checklist for developing an implementable risk management strategy, Section 2 highlights the impact that exponential growth of CO₂ emissions is likely to have on future disaster losses as assessed by climate and social scientists. Section 3 then discusses how people perceive the risks of low-probability adverse events and the cognitive biases that lead them to underprepare for future losses. Based on this empirical evidence, Section 4 proposes a risk management strategy for reducing future losses using the principles of choice architecture to communicate the likelihood and consequences of disasters, coupled with economic incentives and well-enforced regulations.     

Digital remediation: social support and online learning communities can help offset rural digital inequality

ABSTRACT

While all students enter college with varying levels of digital skills, those from rural areas may face extra challenges because their own skills and those of their pre-college networks may be underdeveloped. Without some type of intervention, digital deficits can perpetuate further educational disadvantages. We developed an online learning community (OLC) in two sections of an introductory college course and integrated collaborative learning into students’ weekly activities. Regression analysis of survey data (N?=?373) shows three impacts: access to social support is associated with higher skill assessment and improvements in digital skills; rural status is associated with a clear and significant disadvantage in digital skill assessment; and that involvement in an OLC contributed substantially to improvements in digital skills. We conclude with limitations and considerations for future research.     

Using a Lag Schedule of Reinforcement to Increase Response Variability in Children With Autism Spectrum Disorders

Lag reinforcement schedules have been shown in previous research to be an effective intervention for teaching verbal and nonverbal response variability to individuals with developmental disabilities. In more recent research, variability itself has been considered a reinforceable behavior in its own right (Susa & Schlinger, The Analysis of Verbal Behavior, 18, 125–130, 2012). Lag x schedules of reinforcement can be used to teach variability by using contingencies that require responses to differ from previous responses. The present study extended Susa and Schlinger’s, The Analysis of Verbal Behavior, 18, 125–130, (2012) research by using 3 social questions instead of 1 in a random rotation and included probes to test for generality. A changing-criterion design was used to evaluate the results with one 11-year-old female participant diagnosed with autism. During baseline, the participant provided little variability, with rote responses. During the Lag 1 and Lag 2 phases, appropriate variable verbal responding increased with the use of echoic prompts, visual aids, and an error correction procedure. Further, the results also showed that the participant learned to vary her responses by demonstrating the ability to emit 11 novel prompted responses and 13 spontaneous responses.In addition, the participant was able to retain the skills learned in a maintenance probe conducted 4 weeks postintervention.     

Comparison of Past Year Substance Use Estimates by Age,Sex, and Race/Ethnicity Between Two Representative Samples of the U.S. Adult Population

Population Research and Policy Review - Comparative evaluations of national survey data can improve future survey design and sampling strategies thereby enhancing our ability to detect important...     

Usefulness of Asymptotic Distributions in the Classical Occupancy Problem

Approximations to the distribution of a discrete random variable originating from the classical occupancy problem are explored. The random variable X of interest is defined to be how many of N elements selected by or assigned to K individuals when each of the N elements is equally likely to be chosen by or assigned to any of the K individuals. Assuming that N represents the number of cells and each of the K individuals is placed in exactly one of the cells, X can also be defined as the number of cells occupied by the Kindividuals. In the literature, various asymptotic results for the distributions of X and (N ? X) are given; however, no guidelines are specified with respect to their utilization. In this article, these approximations are explored for various values of K and N, and rules of thumb are given for their appropriate use.     

Exact Bootstrap Variances of the Area Under ROC Curve

The area under the Receiver Operating Characteristic (ROC) curve (AUC) and related summary indices are widely used for assessment of accuracy of an individual and comparison of performances of several diagnostic systems in many areas including studies of human perception, decision making, and the regulatory approval process for new diagnostic technologies. Many investigators have suggested implementing the bootstrap approach to estimate variability of AUC-based indices. Corresponding bootstrap quantities are typically estimated by sampling a bootstrap distribution. Such a process, frequently termed Monte Carlo bootstrap, is often computationally burdensome and imposes an additional sampling error on the resulting estimates. In this article, we demonstrate that the exact or ideal (sampling error free) bootstrap variances of the nonparametric estimator of AUC can be computed directly, i.e., avoiding resampling of the original data, and we develop easy-to-use formulas to compute them. We derive the formulas for the variances of the AUC corresponding to a single given or random reader, and to the average over several given or randomly selected readers. The derived formulas provide an algorithm for computing the ideal bootstrap variances exactly and hence improve many bootstrap methods proposed earlier for analyzing AUCs by eliminating the sampling error and sometimes burdensome computations associated with a Monte Carlo (MC) approximation. In addition, the availability of closed-form solutions provides the potential for an analytical assessment of the properties of bootstrap variance estimators. Applications of the proposed method are shown on two experimentally ascertained datasets that illustrate settings commonly encountered in diagnostic imaging. In the context of the two examples we also demonstrate the magnitude of the effect of the sampling error of the MC estimators on the resulting inferences.