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Cognitive disengagement syndrome in pediatric spina bifida
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Skills Training in Affective and Interpersonal Regulation Narrative Therapy Delivered via Synchronous Telehealth: A Case Study of a Rural Woman Veteran With Complex Posttraumatic Stress Disorder
Clinical Case Studies, Volume 22, Issue 4, Page 420-435, August 2023.
The International Classification of Diseases–11th Revision (ICD-11) includes the diagnosis of complex posttraumatic stress disorder (CPTSD). Clinical practice guidelines support the use of phased care for individuals with CPTSD. This case study illustrates the use of synchronous telehealth to deliver phased treatment to a rural woman veteran with CPTSD. Mrs. A experienced sexual, physical, and emotional abuse throughout her life, perpetrated by family members, intimate partners, and military authority figures. She sought treatment for posttraumatic nightmares and body image issues; she also had pain related to fibromyalgia and chronic migraine headaches. Mrs. A participated in 19 sessions of Skills Training in Affective and Interpersonal Regulation (STAIR) Narrative therapy via synchronous telehealth. Trauma and eating disorder symptoms were assessed before and after treatment and the patient demonstrated clinically significant improvement on measures of these disorders. Patient-provider working alliance and quality of life were assessed post-treatment. Synchronous telehealth use drastically increased with the onset of COVID-19; however, little information on treating CPTSD via synchronous video teleconferencing is available. This case study illustrates an evidence-based, phased therapy for CPTSD while highlighting the feasibility and value of in-home delivery of psychotherapy for CPTSD via synchronous telehealth.
The International Classification of Diseases–11th Revision (ICD-11) includes the diagnosis of complex posttraumatic stress disorder (CPTSD). Clinical practice guidelines support the use of phased care for individuals with CPTSD. This case study illustrates the use of synchronous telehealth to deliver phased treatment to a rural woman veteran with CPTSD. Mrs. A experienced sexual, physical, and emotional abuse throughout her life, perpetrated by family members, intimate partners, and military authority figures. She sought treatment for posttraumatic nightmares and body image issues; she also had pain related to fibromyalgia and chronic migraine headaches. Mrs. A participated in 19 sessions of Skills Training in Affective and Interpersonal Regulation (STAIR) Narrative therapy via synchronous telehealth. Trauma and eating disorder symptoms were assessed before and after treatment and the patient demonstrated clinically significant improvement on measures of these disorders. Patient-provider working alliance and quality of life were assessed post-treatment. Synchronous telehealth use drastically increased with the onset of COVID-19; however, little information on treating CPTSD via synchronous video teleconferencing is available. This case study illustrates an evidence-based, phased therapy for CPTSD while highlighting the feasibility and value of in-home delivery of psychotherapy for CPTSD via synchronous telehealth.
Changes in mental health symptoms from April (COVID-19 outbreak) to December 2020 in Norway: A two-wave study
Social-cognitive correlates of expectant mothers’ safe communication behaviour: Applying an adapted HAPA model
Predicting academics’ job satisfaction from their perceived leadership styles: Evidence from Tanzania
Functional near-infrared spectroscopy is a sensitive marker of neurophysiological deficits on executive function tasks in young adults with a history of child abuse
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Neuropsychological functioning of homeless people in Paris: An exploratory study
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The Impact of Measurement Model Misspecification on Coefficient Omega Estimates of Composite Reliability
Educational and Psychological Measurement, Ahead of Print.
Coefficient omega indices are model-based composite reliability estimates that have become increasingly popular. A coefficient omega index estimates how reliably an observed composite score measures a target construct as represented by a factor in a factor-analysis model; as such, the accuracy of omega estimates is likely to depend on correct model specification. The current paper presents a simulation study to investigate the performance of omega-unidimensional (based on the parameters of a one-factor model) and omega-hierarchical (based on a bifactor model) under correct and incorrect model misspecification for high and low reliability composites and different scale lengths. Our results show that coefficient omega estimates are unbiased when calculated from the parameter estimates of a properly specified model. However, omega-unidimensional produced positively biased estimates when the population model was characterized by unmodeled error correlations or multidimensionality, whereas omega-hierarchical was only slightly biased when the population model was either a one-factor model with correlated errors or a higher-order model. These biases were higher when population reliability was lower and increased with scale length. Researchers should carefully evaluate the feasibility of a one-factor model before estimating and reporting omega-unidimensional.
Coefficient omega indices are model-based composite reliability estimates that have become increasingly popular. A coefficient omega index estimates how reliably an observed composite score measures a target construct as represented by a factor in a factor-analysis model; as such, the accuracy of omega estimates is likely to depend on correct model specification. The current paper presents a simulation study to investigate the performance of omega-unidimensional (based on the parameters of a one-factor model) and omega-hierarchical (based on a bifactor model) under correct and incorrect model misspecification for high and low reliability composites and different scale lengths. Our results show that coefficient omega estimates are unbiased when calculated from the parameter estimates of a properly specified model. However, omega-unidimensional produced positively biased estimates when the population model was characterized by unmodeled error correlations or multidimensionality, whereas omega-hierarchical was only slightly biased when the population model was either a one-factor model with correlated errors or a higher-order model. These biases were higher when population reliability was lower and increased with scale length. Researchers should carefully evaluate the feasibility of a one-factor model before estimating and reporting omega-unidimensional.
Correcting for Extreme Response Style: Model Choice Matters
Educational and Psychological Measurement, Ahead of Print.
Extreme response style (ERS), the tendency of participants to select extreme item categories regardless of the item content, has frequently been found to decrease the validity of Likert-type questionnaire results. For this reason, various item response theory (IRT) models have been proposed to model ERS and correct for it. Comparisons of these models are however rare in the literature, especially in the context of cross-cultural comparisons, where ERS is even more relevant due to cultural differences between groups. To remedy this issue, the current article examines two frequently used IRT models that can be estimated using standard software: a multidimensional nominal response model (MNRM) and a IRTree model. Studying conceptual differences between these models reveals that they differ substantially in their conceptualization of ERS. These differences result in different category probabilities between the models. To evaluate the impact of these differences in a multigroup context, a simulation study is conducted. Our results show that when the groups differ in their average ERS, the IRTree model and MNRM can drastically differ in their conclusions about the size and presence of differences in the substantive trait between these groups. An empirical example is given and implications for the future use of both models and the conceptualization of ERS are discussed.
Extreme response style (ERS), the tendency of participants to select extreme item categories regardless of the item content, has frequently been found to decrease the validity of Likert-type questionnaire results. For this reason, various item response theory (IRT) models have been proposed to model ERS and correct for it. Comparisons of these models are however rare in the literature, especially in the context of cross-cultural comparisons, where ERS is even more relevant due to cultural differences between groups. To remedy this issue, the current article examines two frequently used IRT models that can be estimated using standard software: a multidimensional nominal response model (MNRM) and a IRTree model. Studying conceptual differences between these models reveals that they differ substantially in their conceptualization of ERS. These differences result in different category probabilities between the models. To evaluate the impact of these differences in a multigroup context, a simulation study is conducted. Our results show that when the groups differ in their average ERS, the IRTree model and MNRM can drastically differ in their conclusions about the size and presence of differences in the substantive trait between these groups. An empirical example is given and implications for the future use of both models and the conceptualization of ERS are discussed.