Publication Search

71,387 articles from 644 journals · 2,111 citations tracked

Showing 1-3 of 3

Analytics

Fauziah Yulfitria

Journal of Health Sciences, Nursing and Nutrition 2025 International Forum of Researchers and Lecturers

A common mental health problem affecting millions of women all around is anxiety before pregnancy and after delivery. Untreated, it might cause early deliveries, reduced birth weights, bad infant bonding, and children's long-term developmental difficulties. Many mothers choose natural cures than from drugs for safety worries relating to their children. Recent research on non-drug approaches to lessen anxiety in pregnant women and new mothers is examined in this review. Focusing on multiple non-drug treatments, a comprehensive search was conducted in PubMed and Google Scholar. The results indicate that yoga and mindfulness-based stress reduction measures practically reduce anxiety. For treating anxiety, non-drug treatments are safe choices that healthcare professionals should integrate into maternal care. More research is needed for consistent methods and long-term studies to support clinical practices.

Laurensa, Frisca; Rahayu, Budi

Jurnal Kesehatan Medika Udayana 2025 Sekolah Tinggi Ilmu Kesehatan Kesdam IX/Udayana

Background: When entering the third trimester of pregnancy, many pregnant women have difficulty sleeping as they face physical and psychological changes, such as back pain, frequent urination, anxiety, and worries about the upcoming labor process. Almost 66% of pregnant women experience sleep disruptions that can jeopardize maternal and fetal health, including the risk of preeclampsia and low birth weight. Prenatal yoga can relieve stress and anxiety, thus contributing to improving the sleep quality of third trimester pregnant women. Purpose: To know the influence of prenatal yoga on sleep quality of third trimester pregnant women at PMB Appi Ammelia Kasihan Bantul Yogyakarta. Methods: This research utilizes the Pre-Experimental research type and One-Group Pretest-Posttest Design. The sample comprised of three trimester pregnant women totaling 24 respondents. The sampling method used total sampling. Sleep quality assessment was evaluated using the PSQI questionnaire and applying Wilcoxon test analysis. Results: Before treatment, 3 respondents (12.5%) of third trimester pregnant women had good sleep quality. After treatment, there was a change so that 18 (75%) respondents had good sleep quality. A p-value of 0.001 was found from the Wilcoxon test analysis. Conclusion: Prenatal yoga affects the sleep quality of third trimester pregnant women at PMB Appi Ammelia Kasihan Bantul Yogyakarta.

Ira Zulfa; Eliyin Eliyin; Firmansyah Firmansyah; Zikri Syah Dermawan

International Journal of Electrical Engineering, Mathematics and Computer Science 2025 Asosiasi Riset Teknik Elektro dan Infomatika Indonesia

The plan to offer birth control to teenagers, outlined in Government Regulation (PP) No. 28 of 2024, has sparked different responses in the public, especially on social media sites like Twitter. This research intends to look into how people feel about this plan by using the Naïve Bayes Classifier technique. Information was gathered from Twitter by using data collection methods with the snscrape tool and the Python coding language. A total of 1,000 tweets related to the topic of the policy were gathered and went through initial processing steps like cleaning, breaking into words, changing cases, and removing common words. The Naïve Bayes Classifier technique was employed to sort the public's feelings into three groups: positive, negative, and neutral. The findings showed that half of the tweets (50%) had a negative view on the policy, while 35% had a positive outlook, and 15% were neutral. The accuracy of the method used was 78%, with a precision of 74%, a recall of 79%, and an F1-score of 76%. The findings from this research offer a summary of how the public feels about the birth control policy for teenagers, which can help the government assess and create policies that better meet the community's needs and worries. Additionally, this research highlights how well the Naïve Bayes Classifier method works for analyzing sentiments on social media, even though there are some challenges when it comes to understanding language subtleties like sarcasm.