A one-credit hour, elective, professional development course was created at North Carolina State University to introduce pre-veterinary track students to the admissions process and the breadth of the veterinary profession. The course was designed to facilitate career exploration while building self-efficacy through vicarious learning, interacting with speakers in various veterinary subfields, and addressing misperceptions about veterinary admissions. To evaluate the student learning objectives and improve upon the current practices of the course, data from two pretest and posttest course surveys for 235 course participants between Spring 2014 and 2017 were analyzed. The results of the study showed that students experienced significant gains in self-appraisal (Cohen's ranged 1.88 to 2.53), gathering occupational information (Cohen's ranged 1.59 to 2.53), goal selection (Cohen's ranged 2.14 to 2.53), and planning and problem-solving (Cohen's ranged 1.88 to 2.77) as well as experienced a decrease in five misperceptions about veterinary admissions. This novel course is presented as a prospective course for other universities.
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http://dx.doi.org/10.1093/tas/txab106 | DOI Listing |
Importance: Cardiovascular health outcomes associated with noncigarette tobacco products (cigar, pipe, and smokeless tobacco) remain unclear, yet such data are required for evidence-based regulation.
Objective: To investigate the association of noncigarette tobacco products with cardiovascular health outcomes.
Design, Setting, And Participants: This cohort study was conducted within the Cross Cohort Collaboration Tobacco Working Group by harmonizing tobacco-related data and conducting a pooled analysis from 15 US-based prospective cohorts with data on the use of at least 1 noncigarette tobacco product ranging between 1948 and 2015.
J Pain Res
January 2025
Sword Health, Inc, Draper, Utah, USA.
Background: Obesity is a known risk factor and aggravator of musculoskeletal (MSK) conditions. The rising prevalence of obesity calls for scalable solutions to address MSK conditions in this population, given their complex clinical profile and barriers to accessing care.
Purpose: To evaluate the engagement and clinical outcomes of a fully remote digital care program in patients with MSK conditions, focusing on those with and without comorbid obesity.
J Indian Soc Pedod Prev Dent
October 2024
Department of Pediatric and Preventive Dentistry, PMS College of Dental Science and Research, Thiruvananthapuram, Kerala, India.
Purpose: The sleep disturbance scale for children (SDSC) is a well-regarded tool for assessing pediatric sleep disorders, covering areas such as sleep initiation, breathing issues, and arousal disorders. The SDSC, known for its reliability and validity, has been adapted for various age groups and languages and aligns with the Association of Sleep Disorders Centers classification system. This study aimed to translate and culturally adapt the SDSC into Malayalam language for use in Kerala, conducting a test with parents from the Trivandrum district.
View Article and Find Full Text PDFInt J Mol Sci
December 2024
Department of Physics and Astronomy, Rowan University, Glassboro, NJ 08028, USA.
Biocompatible materials fabricated from natural protein polymers are an attractive alternative to conventional petroleum-based plastics. They offer a green, sustainable fabrication method while also opening new applications in biomedical sciences. Available from several sources in the wild and on domestic farms, silk is a widely used biopolymer and one of the strongest natural materials.
View Article and Find Full Text PDFJpn J Radiol
January 2025
Department of Diagnostic Imaging and Nuclear Medicine, Graduate School of Medicine, Kyoto University, 54 Shogoin Kawahara-Cho, Sakyo-Ku, Kyoto, 606-8507, Japan.
Purpose: Magnetization prepared rapid gradient echo (MPRAGE) is a useful three-dimensional (3D) T1-weighted sequence, but is not a priority in routine brain examinations. We hypothesized that converting 3D MRI localizer (AutoAlign Head) images to MPRAGE-like images with deep learning (DL) would be beneficial for diagnosing and researching dementia and neurodegenerative diseases. We aimed to establish and evaluate a DL-based model for generating MPRAGE-like images from MRI localizers.
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