Eating disorders in a nonclinical adolescent population: implications for treatment

The present study investigates the prevalence of adolescent eating disorders across gender, cultural groupings, and socioeconomic status. The Eating Attitudes Test, the Binge-Eating Questionnaire, and a demographic questionnaire were administered to students at two high schools. The results indicate a high rate of eating disorders in a nonclinical adolescent population. The rates for binge eating are comparable to those found in studies of college students--found at a younger age. With regard to SES, cross-cultural and gender findings, it is clear that eating disorders cross socioeconomic class and ethnic groups, and have a higher rate of occurrence in males than previously reported. Given such high rates of eating problems in a nonclinical population, the issue of prevention and early detection must be addressed. Additionally, more specific information on male eating disorders is needed.     

Characteristics of adolescents at risk for compulsive overeating on a brief screening test

As part of a large survey of addictive behavior in high school students, 43% of a sample of 278 (26% of the males, 57% of the females) scored above the cutoff point set by Overeaters Anonymous on their scale for assessing compulsive overeating. While this at-risk group did not report poorer general adjustment, health, or school achievement than did the students not at risk, they did significantly more often perceive their life quality and relationship with the person closest to them as less positive. The at-risk subsample indicated the defensive effectiveness of overeating in their significantly more frequent report of dissociative experiences while eating, and less severe ratings of insecurity, worrying, and daydreaming. One of the most salient findings was the at-risk students' more frequent report of addictive problems in their parents (overeating, alcohol and drug use, and gambling).     

Psychoanalysis, psychoanalytic science, and psychoanalytic research--1986

The present standing of psychoanalysis as a science and the vitality of psychoanalytic research effort are reviewed. The two are interdependent, since the possibilities for empirical research rest on the necessary assumption that psychoanalysis is indeed enough a science to be susceptible to knowledge advance by the (research) methods of science. Concerning our status as a science, I review attacks on our scientific credentials (both from within our ranks and without) by the logical positivists, by the hermeneuticists (a rubric comprising a variety of hermeneutic, phenomenological, exclusively subjectivistic, and/or linguistically based conceptualizations of our field), and the most recent by the philosopher of science, Adolf Grünbaum. I try to demonstrate what I feel to be the failure of each of these assaults, and why I feel there is no reason to see psychoanalysis as anything other than a scientific psychology and, therefore, in theory amenable to empirical research approaches. I then review the history and the current status of these systematic research efforts in psychoanalysis, and the reasons why these have been far less in scope and in accomplishment than has been possible or than has been needed. Here I have focused especially on research involving technique and our theory of change and cure--i.e., research on the analytic process; on what changes take place (outcome) and how those changes come about or are brought about (process).     

On working through: a model from artificial intelligence

Working through is centrally important to clinical psychoanalysis. It is inadequately explained in analytic theory. An artificial intelligence model of the process is proposed. Models of problem solving show that the complexity of necessary computation is an important determinant of how a problem is solved. Not optimal, but only good enough solutions are usually found. The quality of solutions depends on the time and resources available. Generally it is far easier to use existing methods than to develop new approaches. When problems must be solved in an emergency fashion, as in trauma, poor solutions are likely to emerge. In studying the annealing of metals and other complex optimization problems, a process, the Boltzman algorithm, was discovered, which continues the search for better solutions while gradually developing a coherent structure of the overall solution. The algorithm provides a model both for psychoanalytic working through and for the normally ongoing process of psychological development and reworking whose deficiency is characteristic of much psychopathology. Working through in the analytic situation is the reactivation of this normal process, and a good analytic outcome is achieved when the process can continue without the analyst. Properties of the Boltzman algorithm clarify such concepts as "optimal" frustration and anxiety which correspond to working in the area where the stable but not rigid structures emerge in the algorithms operation. These studies are an example of how computer science and artificial intelligence are a potentially rich source for psychoanalytic theory.     

The MMPI and clinical diagnosis: a comparison of classification system outcomes with discharge diagnoses

This study examined the agreement or congruence rate between clinical-discharge diagnoses rendered by a psychiatrist, and admission and discharge MMPI-derived diagnoses from four diagnostic classification systems that have been developed for the MMPI. The four classification systems included a simple high-point code based on the most elevated clinical scale in the profile, the Henrichs revision of the Meehl-Dahlstrom rules, the Goldberg equations, and a system developed by Lachar. Subjects consisted of 150 patients selected from a larger pool of patients who had completed a 9-week adult residential treatment program. Overall, this study yielded modest hit rates between 26% and 34% for MMPI-derived diagnoses and psychiatric diagnoses across the various classification systems. In addition, stability of MMPI-based diagnoses from admission to discharge assessments ranged from 48% to 51% depending on the classification system employed. Findings are discussed in terms of their implications for the use of the MMPI in patient diagnosis. It is recommended that the MMPI be used in conjunction with other sources of clinical and test information in deriving clinical diagnoses.