Development and validation of the Telepsychology Facilitators Scale: A national study.

This study developed and validated the Telepsychology Facilitators Scale (TFS), a novel measure that uses the theory of reasoned action and technology acceptance model as frameworks to assess factors that influence psychologists' openness to using telepsychology. At the beginning of the COVID-19 pandemic, an online sample of 2,619 psychologists completed initial items considered for the TFS, along with a measure assessing their actual use of telepsychology. The sample was split in half, with a preliminary exploratory factor analysis ultimately revealing a 13-item general scale with four distinct subscales (Positive Attitudes, Facilitating Infrastructure, Organizational Support, and External Policies). Higher scores on each subscale positively correlated with psychologists' percentage of patient treatment conducted with telepsychology. The exploratory factor analysis subscale structure was subsequently supported via confirmatory factory analyses of a four-factor structure and bifactor structure (tested separately) with the other half of the sample, revealing adequate model fit for both models and similar convergent validity. The TFS may help the field assess the potential barriers and drivers of telepsychology use among psychologists and be used to inform future organizational and policy efforts to increase telepsychology implementation and use across health service settings. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Suicide Attempts and Ideation Among Veterans/Service Members and Non-Veterans Over 5 Years Following Traumatic Brain Injury: A Combined NIDILRR and VA TBI Model Systems Study.

OBJECTIVE: This study compared rates of suicide attempt (SA) and suicidal ideation (SI) during the first 5 years after traumatic brain injury (TBI) among veterans and service members (V/SMs) in the Veterans Affairs (VA) and the National Institute on Disability, Independent Living, and Rehabilitation Research (NIDILRR) Model Systems National Databases to each other and to non-veterans (non-Vs) in the NIDILRR database. SETTING: Twenty-one NIDILRR and 5 VA TBI Model Systems (TBIMS) inpatient rehabilitation facilities in the United States. PARTICIPANTS: Participants with TBI were discharged from rehabilitation alive, had a known military status recorded (either non-V or history of military service), and successful 1-, 2-, and/or 5-year follow-up interviews completed between 2009 and 2021. The year 1 cohort included 8737 unique participants (8347 with SA data and 3987 with SI data); the year 2 (7628 participants) and year 5 (4837 participants) cohorts both had similar demographic characteristics to the year 1 cohort. DESIGN: Longitudinal design with data collected across TBIMS centers at 1, 2, and 5 years post-injury. MAIN OUTCOMES AND MEASURES: History of SA in past year and SI in past 2 weeks assessed by the Patient Health Questionnaire-9 (PHQ-9). Patient demographics, injury characteristics, and rehabilitation outcomes were also assessed. RESULTS: Full sample rates of SA were 1.9%, 1.5%, and 1.6%, and rates of SI were 9.6%, 10.1%, and 8.7% (respectively at years 1, 2, and 5). There were significant differences among groups based on demographic, injury-related, mental/behavioral health, and functional outcome variables. Characteristics predicting SA/SI related to mental health history, substance use, younger age, lower functional independence, and greater levels of disability. CONCLUSIONS: Compared with participants with TBI in the NIDILRR system, higher rates of SI among V/SMs with TBI in the VA system appear associated with risk factors observed within this group, including mental/behavioral health characteristics and overall levels of disability.

A Network Analysis of the PART-O at 1 and 2 Years After TBI: A Veterans Affairs Model Systems Study.

OBJECTIVE: The construct of participation after traumatic brain injury (TBI) can be difficult to operationalize. Psychometric network analysis offers an empirical approach to visualizing and quantifying the associations between activities that comprise participation, elucidating the relations among the construct's components without assuming the presence of a latent common cause and generating a model to inform future measurement methods. The current research applied psychometric network analysis to the Participation Assessment with Recombined Tools-Objective (PART-O) within a sample of service members and veterans (SM/Vs) with a history of TBI at 1 and 2 years ( T1 and T2 ) postinjury. PARTICIPANTS: Participants ( N = 663) were SM/Vs with a history of TBI who completed comprehensive inpatient rehabilitation services at a Department of Veterans Affairs (VA) Polytrauma Rehabilitation Center (PRC). SETTING: Five VA PRCs. DESIGN: Cross-sectional, retrospective analysis of data from the VA TBI Model Systems study. MAIN MEASURES: PART-O. RESULTS: Network analysis demonstrated that the PART-O structure was generally consistent over time, but some differences emerged. The greatest difference observed was the association between "spending time with friends" and "giving emotional support" to others. This association was more than twice as strong at T2 as at T1 . The "out of the house" item was most central, as demonstrated by dense connections within its own subscale (Out and About) and items in other subscales (ie, Social Relations and Productivity). When examining items connecting the 3 subscales, the items related to giving emotional support, internet use, and getting out of the house emerged as the strongest connectors at T1 , and the internet was the strongest connector at T2 . CONCLUSION: Providing emotional support to others is associated with greater participation across multiple domains and is an important indicator of recovery. Being out and about, internet use, and engagement in productive activities such as school and work shared strong associations with Social Relations. Network analysis permits visual conceptualization of the dynamic constructs that comprise participation and has the potential to inform approaches to measurement and treatment.

Network Analysis of Neurobehavioral and Post-Traumatic Stress Disorder Symptoms One Year after Traumatic Brain Injury: A Veterans Affairs Traumatic Brain Injury Model Systems Study.

Traumatic brain injury (TBI) is often experienced under stressful circumstances that can lead to symptoms of post-traumatic stress disorder (PTSD) and neurobehavioral symptoms of brain injury. There is considerable symptom overlap in the behavioral expression of these conditions. Psychometric network analysis is a useful approach to investigate the role of specific symptoms in connecting these two disorders and is well suited to explore their interrelatedness. This study applied network analysis to examine the associations among PTSD and TBI symptoms in a sample of Service Members and Veterans (SM/Vs) with a history of TBI one year after injury. Responses to the Neurobehavioral Symptom Inventory (NSI) and PTSD Checklist-Civilian version (PCL-C) were obtained from participants who completed comprehensive inpatient rehabilitation services across five Veterans Affairs polytrauma rehabilitation centers. Participants (N = 612) were 93.1% male with an average age of 36.98 years at injury. The analysis produced a stable network. Within the NSI symptom groups, the frustration symptom was an important bridge between the affective and cognitive TBI symptoms. The PCL-C nodes formed their own small cluster with hyperarousal yielding connections with the affective, cognitive, and somatic symptom groups. Consistent with this observation, the hyperarousal node had the second strongest bridge centrality in the network. Hyperarousal appears to play a key role in holding together this network of distress and thus represents a prime target for intervention among individuals with elevated symptoms of PTSD and a history of TBI. Network analysis offers an empirical approach to visualizing and quantifying the associations among symptoms. The identification of symptoms that are central to connecting multiple conditions can inform diagnostic precision and treatment selection.