Does the Repressor Coping Style Predict Lower Posttraumatic Stress Symptoms?

We tested whether a continuous measure of repressor coping style predicted lower posttraumatic stress disorder (PTSD) symptoms in 122 health care professionals serving in Operation Iraqi Freedom. Zero-order correlational analyses indicated that predeployment repressor coping scores negatively predicted postdeployment PTSD symptoms, $r_s = -0.29, p = 0.001$, whereas predeployment Connor-Davidson Resilience Scale (CD-RISC) scores did not predict postdeployment PTSD symptoms, $r_s = -0.13, p = 0.14$. However, predeployment trait anxiety was chiefly responsible for the association between repressor coping and PTSD symptom severity, $r_s = 0.38, p = 0.001$. Four percent of the subjects qualified for a probable PTSD diagnosis. Although service members with relatively higher PTSD scores had lower repressor coping scores than did the other subjects, their level of predeployment anxiety was chiefly responsible for this relationship. Knowing someone's predeployment level of trait anxiety permits better prediction of PTSD symptoms among trauma-exposed service members than does knowing his or her level of repressive coping.Psycholog

Understanding genetic breast cancer risk: Processing loci of the BRCA Gist Intelligent Tutoring System

The BRCA Gist Intelligent Tutoring System helps women understand and make decisions about genetic testing for breast cancer risk. BRCA Gist is guided by Fuzzy-Trace Theory, (FTT) and built using AutoTutor LITE. It responds differently to participants depending on what they say. Seven tutorial dialogues requiring explanation and argumentation are guided by three FTT concepts: forming gist explanations in one\u27s own words, emphasizing decision-relevant information, and deliberating the consequences of decision alternatives. Participants were randomly assigned to BRCA Gist, a control, or impoverished BRCA Gist conditions removing gist explanation dialogues, argumentation dialogues, or FTT images. All BRCA Gist conditions performed significantly better than controls on knowledge, comprehension, and risk assessment. Significant differences in knowledge, comprehension, and fine-grained dialogue analyses demonstrate the efficacy of gist explanation dialogues. FTT images significantly increased knowledge. Providing more elements in arguments against testing correlated with increased knowledge and comprehension

The development and analysis of tutorial dialogues in AutoTutor Lite

The goal of intelligent tutoring systems (ITS) that interact in natural language is to emulate the benefits that a well-trained human tutor provides to students, by interpreting student answers and appropriately responding in order to encourage elaboration. BRCA Gist is an ITS developed using AutoTutor Lite, a Web-based version of AutoTutor. Fuzzy-trace theory theoretically motivated the development of BRCA Gist, which engages people in tutorial dialogues to teach them about genetic breast cancer risk. We describe an empirical method to create tutorial dialogues and fine-tune the calibration of BRCA Gist\u27s semantic processing engine without a team of computer scientists. We created five interactive dialogues centered on pedagogic questions such as What should someone do if she receives a positive result for genetic risk of breast cancer? This method involved an iterative refinement process of repeated testing with different texts and successively making adjustments to the tutor\u27s expectations and settings in order to improve performance. The goal of this method was to enable BRCA Gist to interpret and respond to answers in a manner that best facilitated learning. We developed a method to analyze the efficacy of the tutor\u27s dialogues. We found that BRCA Gist\u27s assessment of participants\u27 answers was highly correlated with the quality of the answers found by trained human judges using a reliable rubric. The dialogue quality between users and BRCA Gist predicted performance on a breast cancer risk knowledge test completed after exposure to the tutor. The appropriateness of BRCA Gist\u27s feedback also predicted the quality of answers and breast cancer risk knowledge test scores. © 2013 Psychonomic Society, Inc

Understanding genetic breast cancer risk: Processing loci of the BRCA Gist Intelligent Tutoring System

The BRCA Gist Intelligent Tutoring System helps women understand and make decisions about genetic testing for breast cancer risk. BRCA Gist is guided by Fuzzy-Trace Theory, (FTT) and built using AutoTutor Lite. It responds differently to participants depending on what they say. Seven tutorial dialogues requiring explanation and argumentation are guided by three FTT concepts: forming gist explanations in one’s own words, emphasizing decision-relevant information, and deliberating the consequences of decision alternatives. Participants were randomly assigned to BRCA Gist, a control, or impoverished BRCA Gist conditions removing gist explanation dialogues, argumentation dialogues, or FTT images. All BRCA Gist conditions performed significantly better than controls on knowledge, comprehension, and risk assessment. Significant differences in knowledge, comprehension, and fine-grained dialogue analyses demonstrate the efficacy of gist explanation dialogues. FTT images significantly increased knowledge. Providing more elements in arguments against testing correlated with increased knowledge and comprehension

The development and analysis of tutorial dialogues in AutoTutor Lite

The goal of Intelligent Tutoring Systems (ITS) that interact in natural language is to emulate the benefits a well-trained human tutor provides to students, by interpreting student answers and appropriately responding to encourage elaboration. BRCA Gist is an ITS developed using AutoTutor Lite, a web-based version of AutoTutor. Fuzzy-Trace Theory theoretically motivated the development of BRCA Gist, which engages people in tutorial dialogues to teach them about genetic breast cancer risk. We describe an empirical method to create tutorial dialogues and fine-tune the calibration of BRCA Gist’s semantic processing engine without a team of computer scientists. We created five interactive dialogues centered on pedagogic questions, such as “What should someone do if she receives a positive result for genetic risk of breast cancer?” This method involved an iterative refinement process of repeated testing with different texts, and successively making adjustments to the tutor’s expectations and settings to improve performance. The goal of this method was to enable BRCA Gist to interpret and respond to answers in a manner that best facilitates learning. We developed a method to analyze the efficacy of the tutor’s dialogues. We found that BRCA Gist’s assessment of participants’ answers was highly correlated with the quality of answers found by trained human judges using a reliable rubric. Dialogue quality between users and BRCA Gist, predicted performance on a breast cancer risk knowledge test completed after the tutor. The appropriateness of BRCA Gist feedback also predicted the quality of answers and breast cancer risk knowledge test scores