Background: With constrained health-care resources, there is a need to understand barriers to cost-effective medication use.
Objective: To study physician perceptions about generic medications.
Methods: Physicians used 5-point Likert scales to report perceptions about cost-related medication nonadherence, the efficacy and quality of generic medications, preferences for generic use, and the implications of dispensing medication samples. Descriptive statistics were used to assess physician perceptions and logistic regression models were used to evaluate predictors of physician perceptions.
Results: Among the invited sample, 839 (30.4%) responded and 506 (18.3%) were eligible and included in the final study population. Over 23% of physicians surveyed expressed negative perceptions about efficacy of generic drugs, almost 50% reported negative perceptions about quality of generic medications, and more than one quarter do not prefer to use generics as first-line medications for themselves or for their family. Physicians over the age of 55 years were 3.3 times more likely to report negative perceptions about generic quality, 5.8 times more likely to report that they would not use generics themselves, and 7.5 times more likely to state that they would not recommend generics for family members (p < 0.05 for all). Physicians reported that pharmaceutical company representatives are the most common (75%) source of information about market entry of a generic medication. Almost half of the respondents expressed concern that free samples may adversely affect subsequent affordability, yet two thirds of respondents provide free samples.
Conclusions: A meaningful proportion of physicians expressed negative perceptions about generic medications, representing a potential barrier to generic use. Payors and policymakers trying to encourage generic use may consider educational campaigns targeting older physicians.
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http://dx.doi.org/10.1345/aph.1P389 | DOI Listing |
J Integr Neurosci
January 2025
Laboratory for the Study of Tactile Communication, Pushkin State Russian Language Institute, 117485 Moscow, Russia.
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Nutrients
January 2025
Department of Nutrition and Dietetics Sciences, Hellenic Mediterranean University (HMU), Trypitos Area, 72300 Sitia, Greece.
Background/objectives: A reliable assessment of gluten-free diet (GFD) adherence is essential for managing celiac disease (CD). This study aimed to validate the Hellenic version of the Celiac Disease Adherence Test (H-CDAT) to evaluate adherence levels and explore the impact of dietary adherence on health-related quality of life (HRQoL)-both of which have never been objectively assessed in Greek CD patients.
Methods: The study included 102 adult CD patients who completed H-CDAT, diet-related questions, and the 36-Item Short Form Health Survey (SF-36).
Nutrients
January 2025
Department of Health Promotion, Faculty of Public Health in Bytom, Medical University of Silesia in Katowice, ul. Piekarska 18, 41-902 Bytom, Poland.
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January 2025
Department of Mechanical and Manufacturing Engineering, University of Calgary, Calgary, AB T2N 1N4, Canada.
This paper presents a comprehensive approach to evaluating the ability of multi-legged robots to traverse confined and geometrically complex unstructured environments. The proposed approach utilizes advanced point cloud processing techniques integrating voxel-filtered cloud, boundary and mesh generation, and dynamic traversability analysis to enhance the robot's terrain perception and navigation. The proposed framework was validated through rigorous simulation and experimental testing with humanoid robots, showcasing the potential of the proposed approach for use in applications/environments characterized by complex environmental features (navigation inside collapsed buildings).
View Article and Find Full Text PDFSensors (Basel)
January 2025
Department of Electronics and Electrical Engineering, Faculty of Science and Technology, Keio University, 3-14-1, Hiyoshi, Kohoku-ku, Yokohama 223-8522, Japan.
Traditional Vision-and-Language Navigation (VLN) tasks require an agent to navigate static environments using natural language instructions. However, real-world road conditions such as vehicle movements, traffic signal fluctuations, pedestrian activity, and weather variations are dynamic and continually changing. These factors significantly impact an agent's decision-making ability, underscoring the limitations of current VLN models, which do not accurately reflect the complexities of real-world navigation.
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