Objective: To analyze child safety seat usage errors among children enrolled at daycare.
Methods: This was a cross-sectional, observational study with prospective data collection and a retrospective analytical axis.
Results: Overall, 42.7% of the children studied were in incorrectly used seats. A logistic regression model showed that the likelihood of usage errors was higher if there were two or more children in the vehicle (odds ratio = 5.10, p = 0.007) and was dependent on parents' educational level and income (medium income and educational level: odds ratio = 7.00, p = 0.003; low income and educational level: odds ratio = 3.40, p = 0.03).
Conclusion: The results of this study are in line with findings reported in international publications.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.2223/JPED.2189 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Assessing the accuracy and quality of artificial intelligence (AI) chatbot-generated responses in making patient-specific drug-therapy and healthcare-related decisions.
BMC Med Inform Decis Mak
December 2024
School of Pharmacy, University of Washington, 1959 NE Pacific Street, Box 357630, Seattle, WA, 98195, USA.
Background: Interactive artificial intelligence tools such as ChatGPT have gained popularity, yet little is known about their reliability as a reference tool for healthcare-related information for healthcare providers and trainees. The objective of this study was to assess the consistency, quality, and accuracy of the responses generated by ChatGPT on healthcare-related inquiries.
Methods: A total of 18 open-ended questions including six questions in three defined clinical areas (2 each to address "what", "why", and "how", respectively) were submitted to ChatGPT v3.
A boundedly rational model for category learning.
Front Psychol
December 2024
Department of Psychology, University of Oregon, Eugene, OR, United States.
The computational modeling of category learning is typically evaluated in terms of the model's accuracy. For a model to accurately infer category membership of stimuli, it has to have sufficient representational precision. Thus, many category learning models infer category representations that guide decision-making and the model's fitness is evaluated by its ability to accurately choose.
View Article and Find Full Text PDFmHealth Engagement for Antiretroviral Medication Adherence Among People With HIV and Substance Use Disorders: Observational Study.
J Med Internet Res
December 2024
School of Journalism and Mass Communication, University of Wisconsin-Madison, Madison, WI, United States.
Background: Despite the increasing popularity of mobile health (mHealth) technologies, little is known about which types of mHealth system engagement might affect the maintenance of antiretroviral therapy among people with HIV and substance use disorders.
Objective: This study aimed to use longitudinal and detailed system logs and weekly survey data to test a mediation model, where mHealth engagement indicators were treated as predictors, substance use and confidence in HIV management were treated as joint mediators, and antiretroviral therapy adherence was treated as the outcome. We further distinguished the initiation and intensity of system engagement by mode (expression vs reception) and by communication levels (intraindividual vs dyadic vs network).
Appropriate use of single-dose injectable aripiprazole lauroxil in the treatment of schizophrenia.
Ment Health Clin
December 2024
(Corresponding author) Associate Professor, Department of Clinical and Administrative Pharmacy, College of Pharmacy, University of Georgia, Athens, GA,
Introduction: Single-dose injectable aripiprazole lauroxil (SDIAL) is used with long-acting injectable (LAI) aripiprazole lauroxil in the treatment of schizophrenia. SDIAL can be used to either initiate treatment or supplement during maintenance when follow-up doses are not given within labeling recommendations. The primary objective was to determine the usage and appropriateness of SDIAL between the initiation and the maintenance supplementation use in a Medicaid database.
View Article and Find Full Text PDFSleep Apnea Events Recognition Based on Polysomnographic Recordings: A Large-Scale Multi-Channel Machine Learning approach.
IEEE Open J Eng Med Biol
November 2024
The gold standard for detecting the presence of apneic events is a time and effort-consuming manual evaluation of type I polysomnographic recordings by experts, often not error-free. Such acquisition protocol requires dedicated facilities resulting in high costs and long waiting lists. The usage of artificial intelligence models assists the clinician's evaluation overcoming the aforementioned limitations and increasing healthcare quality.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!