University of Phoenix (UoPX) is a private for-profit university headquartered in Phoenix, Arizona.[a] Founded in 1976, the university confers certificates and degrees at the certificate, associate, bachelor's, master's, and doctoral degree levels. It is institutionally accredited by the Higher Learning Commission and has an open enrollment admissions policy, accepting all applicants with a high-school diploma, GED, or the equivalent as sufficient for admission. The school is owned by Apollo Global Management, an American private equity firm.
The widespread implementation of telebehavioral health (TBH) services, accelerated by the COVID-19 pandemic, created an urgent need for standardized competencies in counselor education. Many practitioners report minimal formal training, leading to significant variability in competence and a concerning gap between their confidence and actual preparedness. This article issues a call to action for counseling programs to adopt the interprofessional TBH competency framework from the Coalition for Technology in Behavioral Science (CTiBS). This structured, evidence-based roadmap outlines knowledge and skills for competent, ethical TBH practice across seven domains and three progressive levels: novice, proficient, and authority. Adopting this framework will enhance counselor development, align training with the Council for Accreditation of Counseling and Related Educational Programs (CACREP) standards and reimbursement policies, and ensure practitioners are prepared for modern service delivery. This approach fosters ethical, culturally responsive care and prepares counselors to meet future demands in an era of rapidly evolving technology.
BACKGROUND:For patients with atrial fibrillation, the use of oral anticoagulant therapy to prevent stroke is limited by the risk of bleeding. Left atrial appendage closure is considered for patients who are unsuitable candidates for long-term anticoagulation, but its role in patients who are eligible for anticoagulants has not been established. METHODS:In this ongoing, prospective, international, randomized trial involving patients with atrial fibrillation who were suitable candidates for anticoagulation, we randomly assigned patients in a 1:1 ratio to receive either device-based left atrial appendage closure (device group) or non-vitamin K antagonist oral anticoagulant (NOAC) therapy (anticoagulation group). The primary efficacy end point - a composite of death from cardiovascular causes, stroke, or systemic embolism - was tested for noninferiority (noninferiority margin, 4.8 percentage points) after 3 years of follow-up. The primary safety end point, non-procedure-related bleeding, was tested for superiority. RESULTS:Of the 3000 patients who underwent randomization, 1499 were assigned to the device group and 1501 to the anticoagulation group. The mean (±SD) age of the patients was 71.7±7.5 years, 31.9% of the patients were women, and the mean CHA2DS2-VASc score was 3.5±1.3. At 3 years, a primary efficacy end-point event had occurred in 81 patients (Kaplan-Meier estimate, 5.7%) in the device group and in 65 patients (Kaplan-Meier estimate, 4.8%) in the anticoagulation group (difference, 0.9 percentage points; 95% confidence interval [CI], -0.8 to 2.6; P<0.001 for noninferiority). Non-procedure-related bleeding occurred in 154 patients (Kaplan-Meier estimate, 10.9%) in the device group and in 260 patients (Kaplan-Meier estimate, 19.0%) in the anticoagulation group (hazard ratio, 0.55; 95% CI, 0.45 to 0.67; P<0.001 for superiority). CONCLUSIONS:Among patients with atrial fibrillation who were candidates for anticoagulation, device-based left atrial appendage closure was noninferior to NOAC therapy with respect to a composite of death from cardiovascular causes, stroke, or systemic embolism and was superior to NOAC therapy for non-procedure-related bleeding at 3 years. (Funded by Boston Scientific; CHAMPION-AF ClinicalTrials.gov number, NCT04394546.).
This study employs comprehensive panel data econometric analysis to investigate the determinants of population growth across 10 emerging economies over 61 annual periods (N=610). Utilizing multiple estimation techniques, including fixed effects, random effects, dynamic panel models, panel VAR, and seemingly unrelated regression, the research addresses endogeneity, unobserved heterogeneity, and dynamic interdependencies. Empirical results reveal robust findings: birth rates exhibit strong positive effects on population growth (coefficients 0.789-0.805, p<0.001), while death rates show significant negative associations (-0.340 to -0.375, p<0.001). Health expenditure emerges as a consistently positive determinant (0.106-0.141, p<0.001), whereas economic growth variables demonstrate statistically insignificant direct effects, suggesting more complex mediated relationships. The Hausman test supports the random effects model as preferred (χ²=6.127, p=0.409). Panel VAR analysis reveals dynamic interdependencies and system stability, while SUR estimation distinguishes demographic and economic determinants, with birth and death rates strongly predicting population growth (R²=0.907). Diagnostic tests confirm data quality, stationarity, and absence of severe multicollinearity. These findings contribute to demographic transition theory by demonstrating the continuing importance of traditional demographic factors alongside socioeconomic determinants in shaping population growth in emerging economies, supporting integrated policy approaches for sustainable demographic outcomes.
Behavioral healthcare leaders must expand access, stabilize a depleted workforce, preserve quality, and adopt artificial intelligence (AI) responsibly. This chapter presents the Behavioral Healthcare Integrated Leadership Model (BHILM), a practice-informed framework that integrates transactional, transformational, and compassionate leadership through cognitive resilience to guide AI adoption while protecting clinical judgment, staff well-being, and patient dignity. The Twin Heartbeats framing integrates two published frameworks, operationalizing the Personal Heartbeat through the Whole Person Triangle and the Collective Heartbeat through the Collaborative Synergy Learning Model's 70-20-10 architecture of experiential, social, and formal learning. A 90-day activation cycle and illustrative scenarios demonstrate application, and testable propositions guide research. The chapter offers a transferable lens for whole-person, AI-enabled leadership in knowledge-intensive organizations.
To effectively integrate AI into education, university instructors need to have AI-specific technological, pedagogical, and content knowledge (TPACK). Research indicates that university instructors often feel unprepared with these competencies; thus, they lack the knowledge to effectively integrate AI tools into their teaching practices. While substantial literature exists on AI in education, there remains a significant gap in faculty-centered research, particularly regarding instructor knowledge, comfort, and ethical understanding of AI implementation in higher educational contexts. This study examined faculty perceptions regarding their AI-TPACK knowledge, ethics-based AI knowledge, and then assessed whether those perceptions were influenced by training in AI tools. Using a quantitative descriptive cross-sectional research design that is based on Celik’s TPACK framework, this empirical research examined faculty members’ self-perceived competencies in each area. The study found that AI-focused professional development significantly enhanced faculty knowledge and comfort with AI integration among the 100 faculty members who participated in the study. Also, the findings revealed a critical gap in ethical AI knowledge, underscoring the urgent need for ethics-specific training to build faculty confidence in classroom implementation. Universities should leverage these findings to offer targeted professional development initiatives that close identified gaps and improve instructional effectiveness and faculty digital literacy skills.
