Background: There is a steady growth in the Spanish-speaking population in the United States. Language may be a barrier in accessing nonprescription medication information for the non-English-speaking population.
Objective: The objective of this study was to compare consumer-reported ease of use, product knowledge, and intention to purchase over-the-counter (OTC) medications using bilingual Product Information Labels (PILs) and currently available label formats in a sample of Spanish-speaking consumers.
Methods: Participants were randomly selected from Spanish-speaking consumers shopping for OTC medications in pharmacy or grocery stores in Houston, TX. Participants viewed 3 label formats (old, new, and PILs) for acetaminophen, ibuprofen, and aspirin in a random order. Questionnaires in English and in Spanish were provided to consumers after they viewed each label format. Domains measured in the questionnaires included ease of use, product knowledge, and purchase intention. All responses were measured on a 7-point Likert-type scale. Data were recoded and analyzed using SAS (version 9.0) (SAS Institute Inc, Cary, NC) to obtain mean scores for each domain. Participants were classified according to language proficiency into "Spanish only" and bilinguals. Comparative statistics were computed to compare mean scores between label formats in each consumer category.
Results: A total of 225 questionnaires were collected. The mean (+/-standard deviation) age of participants was 38.91 (+/-11.95) years. A majority of respondents were Hispanic (97.75%), female (60.54%), and married (62.44%). Mean scores from viewing PILs on ease of use, product knowledge, and purchase intention were higher than those from viewing the other label formats. In the category of consumers who spoke Spanish only, mean scores of PILs were significantly higher as compared to those of old and new label formats (P<.05).
Conclusion: Information provided in Spanish on PILs may be very helpful to the Spanish-speaking community when selecting nonprescription medications. Policy makers and health care providers should consider PILs as an effective means of reducing language barriers and providing OTC medication information to the Spanish-speaking population in the United States.
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http://dx.doi.org/10.1016/j.sapharm.2006.12.001 | DOI Listing |
Cell Rep Methods
January 2025
Department of Neurology, University of California San Francisco, San Francisco, CA 94158, USA. Electronic address:
We develop a data harmonization approach for C. elegans volumetric microscopy data, consisting of a standardized format, pre-processing techniques, and human-in-the-loop machine-learning-based analysis tools. Using this approach, we unify a diverse collection of 118 whole-brain neural activity imaging datasets from five labs, storing these and accompanying tools in an online repository WormID (wormid.
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January 2025
Internal Medicine Department, Endocrine Division (SEMPR), Universidade Federal do Paraná, Curitiba, Brazil.
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Data Brief
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North Carolina Agricultural and Technical State University, 1601 E Market St, Greensboro, NC 27411, United States.
Contemporary research in 3D object detection for autonomous driving primarily focuses on identifying standard entities like vehicles and pedestrians. However, the need for large, precisely labelled datasets limits the detection of specialized and less common objects, such as Emergency Medical Service (EMS) and law enforcement vehicles. To address this, we leveraged the Car Learning to Act (CARLA) simulator to generate and fairly distribute rare EMS vehicles, automatically labelling these objects in 3D point cloud data.
View Article and Find Full Text PDFData Brief
February 2025
Institute of Agricultural Sciences, Spanish National Research Council (ICA-CSIC), Serrano 115b, 28006 Madrid, Spain.
Identifying weed species at early-growth stages is critical for precision agriculture. Accurate classification at the species-level enables targeted control measures, significantly reducing pesticide use. This paper presents a dataset of RGB images captured with a Sony ILCE-6300L camera mounted on an unmanned aerial vehicle (UAV) flying at an altitude of 11 m above ground level.
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Global Biometrics and Data Management, Pfizer Research and Development, New York, New York, USA.
The pharmaceutical industry constantly strives to improve drug development processes to reduce costs, increase efficiencies, and enhance therapeutic outcomes for patients. Model-Informed Drug Development (MIDD) uses mathematical models to simulate intricate processes involved in drug absorption, distribution, metabolism, and excretion, as well as pharmacokinetics and pharmacodynamics. Artificial intelligence (AI), encompassing techniques such as machine learning, deep learning, and Generative AI, offers powerful tools and algorithms to efficiently identify meaningful patterns, correlations, and drug-target interactions from big data, enabling more accurate predictions and novel hypothesis generation.
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