Effects of clients' attributional explanations and reasons for seeking help on counselors' impressions.

On the basis of an impression-management view of the counseling interaction, the effects of clients' initial self-presentations on counselors' impressions were investigated. It was predicted that a client offering personal attributional explanations (claiming responsibility for his/her problematic behavior) would be seen in a more favorable light than would one who attributed his/her predicament to others or to the situation. It was also expected that the client who expressed an intrinsic (self-improvement) reason for seeking help would make a better impression than would one who offered an extrinsic (nontherapeutic) reason. Ratings by 77 graduate psychology or social work students of stimulus tapes that manipulated these variables supported the hypotheses. The client who expressed personal attributions was viewed as more motivated and attractive than was the client who made situational attributions. When the reason for seeking help was intrinsic, Ss perceived the client to be more motivated and as having a better prognosis for change. There were, however, no uniquely significant effects on specific outcome expectancies or on judgments of problem severity. (16 ref) (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Constrained Codes as Networks of Relations

We address the well-known problem of determining the capacity of constrained coding systems. While the one-dimensional case is well understood to the extent that there are techniques for rigorously deriving the exact capacity, in contrast, computing the exact capacity of a two-dimensional constrained coding system is still an elusive research challenge. The only known exception in the two-dimensional case is an exact (however, not rigorous) solution to the -run-length limited (RLL) system on the hexagonal lattice. Furthermore, only exponential-time algorithms are known for the related problem of counting the exact number of constrained two-dimensional information arrays. We present the first known rigorous technique that yields an exact capacity of a two-dimensional constrained coding system. In addition, we devise an efficient (polynomial time) algorithm for counting the exact number of constrained arrays of any given size. Our approach is a composition of a number of ideas and techniques: describing the capacity problem as a solution to a counting problem in networks of relations, graph-theoretic tools originally developed in the field of statistical mechanics, techniques for efficiently simulating quantum circuits, as well as ideas from the theory related to the spectral distribution of Toeplitz matrices. Using our technique, we derive a closed-form solution to the capacity related to the Path-Cover constraint in a two-dimensional triangular array (the resulting calculated capacity is ). Path-Cover is a generalization of the well known one-dimensional -RLL constraint for which the capacity is known to be .     

A brief assessment of concerns associated with genetic testing for cancer: The multidimensional impact of cancer risk assessment (MICRA) questionnaire.

The Multidimensional Impact of Cancer Risk Assessment (MICRA) is a new tool to measure the specific impact of result disclosure after genetic testing. The authors compared its performance with that of questionnaires measuring general and cancer-specific distress. Participants (158 women) responded 1 month after they received genetic test results. The women were divided into 4 standard clinical test result groups: BRCA1/2 positive, BRCA1/2 negative, panel negative, and true negative. Factor analysis supported the formation of 3 subscales: Distress (6 items, α= .86), Uncertainty (9 items, α= .77), and Positive Experiences (4 items, α= .75). All 3 MICRA subscales differentiated participants who were BRCAI/2 positive from the other 3 groups. MICRA thus helps identify subgroups of vulnerable genetic testing participants. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Maximizing versus satisficing: Happiness is a matter of choice.

Can people feel worse off as the options they face increase? The present studies suggest that some people--maximizers--can. Study 1 reported a Maximization Scale, which measures individual differences in desire to maximize. Seven samples revealed negative correlations between maximization and happiness, optimism, self-esteem, and life satisfaction, and positive correlations between maximization and depression, perfectionism, and regret. Study 2 found maximizers less satisfied than nonmaximizers (satisficers) with consumer decisions, and more likely to engage in social comparison. Study 3 found maximizers more adversely affected by upward social comparison. Study 4 found maximizers more sensitive to regret and less satisfied in an ultimatum bargaining game. The interaction between maximizing and choice is discussed in terms of regret, adaptation, and self-blame. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

pH and Acid Anion Time Trends in Different Elevation Ranges in the Great Smoky Mountains National Park

Quarterly base flow water quality data collected from October, 1993 to November, 2002 at 90 stream sites in the Great Smoky Mountains National Park were used in step-wise multiple linear regression models to analyze pH, acid neutralizing capacity (ANC), and sulfate and nitrate long-term time trends. The potential predictor variables included cumulative Julian day, seasonality, elevation, basin slope, stream order, precipitation, surrogate streamflows, geology, and acid depositional fluxes. Modeling revealed statistically significant decreasing trends in pH and sulfate with time at lower elevations, but generally no long-term time trends in stream nitrate or ANC. The best forecasting models were chosen based on maximizing the r2 of a holdout data set. If conditions remain the same and past trends continue, the forecasting models suggest that 30.0% of the sampling sites will reach pH values less than 6.0 in less than 10?years, 63.3% in less than 25?years, and 96.7% in less than 50?years. The pH forecasting models explain 65% of the variability in the holdout data.