Objective: To validate a model we developed while trying to understand why pregnant teens so often report that they did not want to become pregnant and could have obtained contraceptives before they conceived.
Method: The study enrolled a racially/ethnically diverse group of 351 teenagers. Participants completed a questionnaire that asked about teen pregnancy risk factors, the expected effects of childbearing, the desire to remain non-pregnant, deterrents to contraceptive use, and contraceptive plans.
Results: Most participants were capable of using contraceptives but at high risk for unintended conception because they exhibited numerous sociodemographic risk factors, were unsure that pregnancy would affect their lives adversely, and were ambivalent about remaining non-pregnant. Believing a boyfriend wanted a baby and the anticipated effect of childbearing on 5 specific aspects of life explained 63% of the variance in the desire to remain non-pregnant, which, in conjunction with fears about using contraceptives, explained 20.5% of the variance in future contraceptive plans.
Conclusions: Our new findings that expectations about the effect of childbearing explain the desire to remain non-pregnant may well help providers determine why teenagers who do not plan to conceive are often willing to allow themselves to do so by default. Further research is needed, as the model did not explain contraceptive decision-making adequately.
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http://dx.doi.org/10.1300/J013v42n01_04 | DOI Listing |
Sci Rep
January 2025
Department of Engineering, FH Campus Wien - University of Applied Sciences, Favoritenstraße 226, Vienna, 1100, Austria.
Meta-heuristic optimization algorithms are widely applied across various fields due to their intelligent behavior and fast convergence, but their use in optimizing engine behavior remains limited. This study addresses this gap by integrating the Design of Experiments-based Response Surface Methodology (RSM) with meta-heuristic optimization techniques to enhance engine performance and emissions characteristics using Tectona Grandi's biodiesel with Elaeocarpus Ganitrus as an additive. Advanced Machine Learning (ML) models, including Artificial Neural Networks (ANN), K-Nearest Neighbors (KNN), Extreme Gradient Boosting (XGB), and Random Trees (RT), were employed for predictive analysis, with ANN outperforming RSM in accuracy.
View Article and Find Full Text PDFDisabil Rehabil Assist Technol
January 2025
Centre for Human Movement and Rehabilitation, School of Health & Society, University of Salford, Salford, Greater Manchester, UK.
Purpose: Falls cost the NHS over £2 billion a year, with incidence increasing rapidly with age. Design of indoor walking frames remains limited, often needing to be lifted and not supporting sit-to-stand and turning manoeuvres, which can lead to falling. This study explored aspects of safety and satisfaction and potential for clinical use of a novel prototype walking frame.
View Article and Find Full Text PDFPLoS One
January 2025
Faculty of Medicine, Department of Health Sciences, Lund University, Lund, Sweden.
Despite the potential of smart home technologies (SHT) to support everyday activities, the implementation rate of such technology in the homes of older adults remains low. The overall aim of this study was to explore factors involved in the decision-making process in adopting SHT among current and future generations of older adults. We also aimed to identify and understand barriers and facilitators that can better support older adults' engagement in everyday activities.
View Article and Find Full Text PDFInt J Sports Physiol Perform
January 2025
Laboratory of the Metabolic Adaptations to Exercise Under Physiological and Pathological Conditions (AME2P) UPR 3533, CRNH Auvergne, Clermont Auvergne University, Clermont-Ferrand, France.
Purpose: The impact of weight cycling (WC)-successive weight loss (WL) and weight regain (WG)-on athlete performance is well documented, but effects on appetite are not. This study assessed the impact of a WC episode on dietary and appetitive profiles in athletes, considering sex and sport type.
Methods: Athletes (28 male, 20 female) from combat (n = 23), strength (n = 12), and endurance (n = 13) sports participated in 3 conditions during a WC episode (baseline, WL, WG).
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