Family-Based Intervention with High-Risk Infants and Their Families

A model of family-based intervention for high-risk infants hospitalized in the neonatal intensive care unit and their families is presented. The theoretical underpinnings, principles, and nature of the intervention are described. The adaptation of the model from a clinical–research demonstration project to a clinical consultation service is highlighted, with particular emphasis on the practical application of the model. Domains of the intervention include infant behavior, family organization and functioning, caregiving environment, and home discharge/community resources. Three case illustrations are provided to demonstrate the breadth of the intervention and how it can be individualized based upon the presentation of the infant and family.     

Binary action based estimation of propensities

An observer is to make inference statements about a quantityp, called apropensity and bounded between 0 and 1, based on the observation thatp does or does not exceed a constantc. The propensityp may have an interpretation as a proportion, as a long-run relative frequency, or as a personal probability held by some subject. Applications in medicine, engineering, political science, and, most especially, human decision making are indicated. Bayes solutions for the observer are obtained based on prior distributions in the mixture of beta distribution family; these are then specialized to power-function prior distributions. Inference about logp and log odds is considered. Multiple-action problems are considered in which the focus of inference shifts to theprocess generating the propensitiesp, both in the case of a process parameter known to the subject and unknown. Empirical Bayes techniques are developed for observer inference aboutc when is known to the subject. A Bayes rule, a minimax rule and a beta-minimax rule are constructed for the subject when he is uncertain about.This research was partially supported by the Defense Advanced Research Projects Agency of the Department of Defense and was monitored by ONR under Contract No. N00014-77-C-0095. Any opinions, findings, conclusions or recommendations expressed herein are those of the author and do not necessarily reflect the views of the Defense Advanced Research Projects Agency, the Office of Naval Research, or Carnegie-Mellon University.     

Measuring nominal scale agreement between a judge and a known standard

Two designs for comparing a judge's ratings with a known standard are presented and compared. Design A pertains to the situation where the judge is asked to categorize each ofN subjects into one ofr (known) classes with no knowledge of the actual number in each class. Design B is employed when the judge is given the actual number in each class and is asked to categorize the individuals subject to these constraints. The probability distribution of the total number of correct choices is developed in each case. A power comparison of the two procedures is undertaken.     

Estimating the parameters of the latent population distribution

Under consideration is a test battery of binary items. The responses ofn individuals are assumed to follow a Rasch model. It is further assumed that the latent individual parameters are distributed within a given population in accordance with a normal distribution. Methods are then considered for estimating the mean and variance of this latent population distribution. Also considered are methods for checking whether a normal population distribution fits the data. The developed methods are applied to data from an achievement test and from an attitude test.     

A latent class approach to fitting the weighted Euclidean model,clascal

A weighted Euclidean distance model for analyzing three-way proximity data is proposed that incorporates a latent class approach. In this latent class weighted Euclidean model, the contribution to the distance function between two stimuli is per dimension weighted identically by all subjects in the same latent class. This model removes the rotational invariance of the classical multidimensional scaling model retaining psychologically meaningful dimensions, and drastically reduces the number of parameters in the traditional INDSCAL model. The probability density function for the data of a subject is posited to be a finite mixture of spherical multivariate normal densities. The maximum likelihood function is optimized by means of an EM algorithm; a modified Fisher scoring method is used to update the parameters in the M-step. A model selection strategy is proposed and illustrated on both real and artificial data.The second author is supported as Bevoegdverklaard Navorser of the Belgian Nationaal Fonds voor Wetenschappelijk Onderzoek.     

A latent class unfolding model for analyzing single stimulus preference ratings

A multidimensional unfolding model is developed that assumes that the subjects can be clustered into a small number of homogeneous groups or classes. The subjects that belong to the same group are represented by a single ideal point. Since it is not known in advance to which group of class a subject belongs, a mixture distribution model is formulated that can be considered as a latent class model for continuous single stimulus preference ratings. A GEM algorithm is described for estimating the parameters in the model. The M-step of the algorithm is based on a majorization procedure for updating the estimates of the spatial model parameters. A strategy for selecting the appropriate number of classes and the appropriate number of dimensions is proposed and fully illustrated on some artificial data. The latent class unfolding model is applied to political science data concerning party preferences from members of the Dutch Parliament. Finally, some possible extensions of the model are discussed.The first author is supported as Bevoegdverklaard Navorser of the Belgian Nationaal Fonds voor Wetenschappelijk Onderzoek. Part of this paper was presented at the Distancia meeting held in Rennes, France, June 1992.     

Videoconference to supervise early intensive behavioral intervention: A preliminary evaluation of acceptability

At present, early intensive behavioral intervention (EIBI) for children with autism spectrum disorders is the intervention that has the strongest empirical support. EIBI requires frequent supervision by qualified professionals. Norway, like many other countries, has a shortage of qualified supervisors, particularly in rural regions. This study used a reversal design to investigate how supervisors and supervisees perceive the quality of EIBI supervision and the local team's preparedness when supervision was provided either on-site or via videoconference. Calculations were made on how much time could be saved on travel when part of the supervision was provided via videoconference. There were no significant differences in the supervisors' and supervisees' ratings of on-site and videoconference supervision. Moreover, the supervisors found the local EIBI teams to be better prepared when supervision was provided via videoconference, and the videoconference supervision meetings were shorter. The study discusses the implications of these findings for the accessibility of EIBI and case capacity and proposes some areas for further research.     

Intensive Cognitive Processing Therapy Associated With Reduced PTSD Treatment Dropout in a Case-Controlled Study of Treatment-Seeking Veterans

Cognitive Processing Therapy (CPT) is an empirically supported psychotherapy for posttraumatic stress disorder (PTSD). The complex issue of treatment attrition is a frequently cited concern regarding CPT and other evidence-based psychotherapies for PTSD. The current study investigated the feasibility and effectiveness of “intensive” CPT, a treatment-consistent modification of CPT in which sessions are conducted more frequently than standard protocol. Fifty-four military veterans (20% female; M_age = 46; 80% Caucasian) seeking outpatient treatment for PTSD were included in this study. Patients who elected to receive intensive CPT (n = 27) were matched with archival records of individuals who received standard CPT (n = 27). Patients across treatment conditions were matched based on baseline symptom severity as measured by the PTSD Checklist for DSM-5 (PCL-5; M_PCL-5 = 52.61). Treatment outcomes were compared and longitudinal multilevel modeling was used to compare rate of symptom change over time. Patients who elected to engage in intensive CPT were twice as likely to complete treatment (88.9%) as those who received standard CPT (44.4%), X²(1, N = 54) = 12.00, p = .001. On average, intensive CPT patients completed treatment in about 25% of the time as standard CPT patients (33.8 days vs. 125.7 days). Patients in intensive CPT also demonstrated greater benefit: 81.4% reported a clinically significant improvement in PCL-5 scores compared to 51.8% of those in standard CPT, X²(1, N = 54) = 5.33, p = .020, φ = −0.314. Intensive and standard CPT performed comparably in regard to final PCL-5 score, change over time, and screening cutoff. In addition, there were no differences in treatment outcomes based on licensure status of the provider nor whether treatment was delivered in person or virtually. Intensive CPT represents a novel, treatment-consistent adaptation that was utilized to treat a group of veterans with PTSD with minimal treatment dropout. Patients who elected to engage in more frequent treatment were more likely to complete treatment, did so in one quarter of the time, and reported similar to better treatment outcomes. Providers may consider encouraging their patients to participate in treatment as frequently as they are able. However, preliminary findings are based on a nonrandom sample and design limitations temper conclusions.     

Intuitions about sample size: the empirical law of large numbers

According to Jacob Bernoulli, even the ‘stupidest man’ knows that the larger one's sample of observations, the more confidence one can have in being close to the truth about the phenomenon observed. Two-and-a-half centuries later, psychologists empirically tested people's intuitions about sample size. One group of such studies found participants attentive to sample size; another found participants ignoring it. We suggest an explanation for a substantial part of these inconsistent findings. We propose the hypothesis that human intuition conforms to the ‘empirical law of large numbers’ and distinguish between two kinds of tasks–one that can be solved by this intuition (frequency distributions) and one for which it is not sufficient (sampling distributions). A review of the literature reveals that this distinction can explain a substantial part of the apparently inconsistent results. © 1997 John Wiley & Sons, Ltd.