Expectancy priming of smoking cessation messages enhances the placebo effect of tailored interventions.

OBJECTIVE: Previous research (Webb, Simmons, & Brandon, 2005) suggested that smokers' reactions to self-help materials were more positive if they believed that the materials had been personally tailored to their individual characteristics and if they held expectancies that tailored interventions are superior to standard, or generic, interventions. The authors' objective in the current study was to replicate and extend this research by testing the efficacy expectancy priming before intervention delivery. DESIGN: In a 2 x 2 factorial experiment, 210 smokers (M = 23 cigarettes/day) recruited from the community (62% female; 92% Caucasian; mean age = 49) were randomly assigned to 1 of 4 conditions: placebo-tailored intervention/no priming, placebo-tailored intervention/priming, standard intervention/no priming, or standard intervention/priming. The tailoring-related expectancies of participants' in the priming conditions were primed before they were presented with the respective intervention booklets. MAIN OUTCOME MEASURES: Content evaluations, readiness to quit smoking, cessation self-efficacy, smoking-related knowledge, and progress toward quitting (behavior changes). Assessments occurred by mail at baseline and at 1-month postintervention. RESULTS: Similar to the earlier study, the placebo-tailored booklets produced superior evaluations and smoking-related cognitive and behavioral changes. Moreover, the pretreatment expectancy priming successfully altered participants' tailoring-related expectancies and also produced superior evaluations and outcomes. CONCLUSION: Findings support a causal role of tailoring-related expectancies on the efficacy of tailored interventions and suggest that interventions can be enhanced via expectancy priming.     

Religion,Poverty, and Development

This article serves two purposes. First, it introduces the forum that follows in this issue on religion and development. Second, it serves as a review of the small but quickly growing literature on how religion interacts with efforts by (often religious) people and organizations to ameliorate poverty worldwide. We address the need to define both “religion” and “development” with clarity and precision. We also call for further research in this area by sociologists, particularly at a time when the landscape of development practice is shifting, from changes in funding sources and priorities to competition between religious and secular organizations.     

Therapist experiences of equine-assisted psychotherapy in South Africa: A qualitative study

This study examined therapists’ perceptions and experiences of equine-assisted psychotherapy (EAP). Participants were 14 practising EAP therapists (females = 86%; male = 14%; age range 20 to 69 years) from across South Africa. They were interviewed utilising electronic interview media. Thematic analysis revealed therapists perceived effectiveness of EAP in providing emotional and interpersonal growth for clients. They also identified training, safety and ethical concerns with this therapeutic modality. Therapists’ experiences with EAP were influenced by their prior interaction with horses as horse owners or horse riders.     

Good Soldier or Good Actor? Supervisor Accuracy in Distinguishing Between Selfless and Self-Serving OCB Motives

Purpose

In line with findings that organizational citizenship behaviors (OCBs) may be driven by selfless and self-serving motives, we sought to determine supervisor effectiveness in distinguishing good soldiers from good actors.

Design/Methodology/Approach

Employing a sample of 197 supervisor-subordinate dyads, we collected self- and supervisor-reports of employees’ citizenship motives. Dominance analysis was used to determine supervisory accuracy in identifying and distinguishing among subordinates’ motives.

Findings

We found that the relationships between self- and supervisor-reports of corresponding motives were strongest, supporting our hypotheses that supervisors are able to accurately identify their subordinates’ OCB motives and that they are not fooled by good actors.

Implications

Our results address concerns raised in previous research that inaccuracy in supervisor attributions of motives might lead to unfair reward or punishment of their subordinates. In demonstrating their accuracy in identifying their subordinates’ motives, an important implication of our work is that supervisors’ preferences for selfless motives may relate to actual differences in their employees’ contribution to the organization.

Originality/Value

Our study contributes to existing research to more conclusively address the question of supervisors’ bias in their preference for selfless motives. Our results also underscore the importance of accounting for employee motives in research exploring the outcomes of OCBs.

     

An optimization approach for mapping and measuring the divergence and correspondence between paths

Many domains of empirical research produce or analyze spatial paths as a measure of behavior. Previously, approaches for measuring the similarity or deviation between two paths have either required timing information or have used ad hoc or manual coding schemes. In this paper, we describe an optimization approach for robustly measuring the area-based deviation between two paths we call ALCAMP (Algorithm for finding the Least-Cost Areal Mapping between Paths). ALCAMP measures the deviation between two paths and produces a mapping between corresponding points on the two paths. The method is robust to a number of aspects in real path data, such as crossovers, self-intersections, differences in path segmentation, and partial or incomplete paths. Unlike similar algorithms that produce distance metrics between trajectories (i.e., paths that include timing information), this algorithm uses only the order of observed path segments to determine the mapping. We describe the algorithm and show its results on a number of sample problems and data sets, and demonstrate its effectiveness for assessing human memory for paths. We also describe available software code written in the R statistical computing language that implements the algorithm to enable data analysis.     

An Integrative Approach to Conceptualizing and Treating Complex Trauma

Complex trauma (CT) often presents with polymorphous symptoms (i.e., emotional dysregulation, dissociation, somatic distress) resulting from repeated and chronic exposure to traumatic stressors. While the public is increasingly aware of posttraumatic stress disorder (PTSD) and its accompanying symptoms, the phenomena of complex trauma is less recognized and understood. Since the trauma reactions with CT typically occur during childhood, and the symptoms go well beyond PTSD, the authors of this article assert that an integrative approach is needed that synthesizes the most effective elements of cognitive-behavioral therapy (CBT) while blending a self-psychological approach. A review of the literature addresses the definition of complex trauma, cognitive-behavioral conceptualizations and treatment approaches, self-psychological models, current theories of attachment, and advances in neuropsychoanalysis. An integrative model is then proposed, supported by clinicians in the trauma field, identifying three intertwined phases of treatment.     

The dynamics of multimodal integration: The averaging diffusion model

We combine extant theories of evidence accumulation and multi-modal integration to develop an integrated framework for modeling multimodal integration as a process that unfolds in real time. Many studies have formulated sensory processing as a dynamic process where noisy samples of evidence are accumulated until a decision is made. However, these studies are often limited to a single sensory modality. Studies of multimodal stimulus integration have focused on how best to combine different sources of information to elicit a judgment. These studies are often limited to a single time point, typically after the integration process has occurred. We address these limitations by combining the two approaches. Experimentally, we present data that allow us to study the time course of evidence accumulation within each of the visual and auditory domains as well as in a bimodal condition. Theoretically, we develop a new Averaging Diffusion Model in which the decision variable is the mean rather than the sum of evidence samples and use it as a base for comparing three alternative models of multimodal integration, allowing us to assess the optimality of this integration. The outcome reveals rich individual differences in multimodal integration: while some subjects’ data are consistent with adaptive optimal integration, reweighting sources of evidence as their relative reliability changes during evidence integration, others exhibit patterns inconsistent with optimality.