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Journal articles on the topic 'SGIM'

Author: Grafiati
Published: 4 June 2025
Last updated: 15 July 2025

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1

Nuwa, Muhammad Saleh, and Stefanus Mendes Kiik. "Pengaruh Spiritual Guided Imagery and Music terhadap Kecemasan Pasien Kanker yang Menjalani Kemoterapi." Jurnal Ners dan Kebidanan (Journal of Ners and Midwifery) 7, no. 1 (2020): 095–106. http://dx.doi.org/10.26699/jnk.v7i1.art.p095-106.

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Latar Belakang : Kemoterapi membuat pasien yang didiagnosa menderita kanker memiliki perasaan gelisah, cemas dan takut akan bayang-bayang kematian yang menghantui setiap saat. Penelitian ini bertujuan untuk mengetahui Pengaruh Spiritual Guided imagery and music (SGIM) terhadap kecemasan pasien kanker yang menjalani kemoterapi. Metode : Jenis Penelitiani ini adalah Quasi experimental dengan rancangan one group pre and post test with control group design. Penelitian dilaksanakan diruangan kemoterapi pada 30 pasien yang mendapatkan terapi SGIM dan 30 sebagai kelompok kontrol. Skore kecemasan diukur sebanyak 3 kali yaitu 1 hari dan 30 menit sebelum kemoterapi serta 1 hari setelah kemoterapi. Kecemasan diukur menggunakaan skala HARS. Data dianalsisi dengan uji GLM repeated mesure dengan bantun SPSS 21. Hasil : sebanyak 73,3 % dan 26,7 pasien memiliki kecemasan berat dan sedang saat pre test menjadi kecemasan sedang (56,7%) dan kecemasan ringan (43,3%) pada pengukuran 30 menit sebelum kemoterapi dan pada akhir pengukuran berada pada kecemasan ringan (63,4 %) dan tidak ada kecemasan (33,3 %) dan sisanya 3,3 % masih mengalami kecemasan berat pada kelompok SGIM. Analisis uji GLM repeated measure post hock banferoni diketahui p value = 0,001, dengan nilai r square =0,29. Hal ini menunjukan ada pengaruh SGIM terhadap penurunan kecemasan pasien kanker yang menjalani kemoterapi dengan besar sumbangan pengaruh sebesar 29% sisanya dipengaruhi variabel yang lain. Kesimpulan : Pemberian Terapi SGIM menurunkan kecemasan pasien kanker yang menjalani kemoterapi. Rekomendasi penelitian adalah SGIM dapat diaplikasikan sebagai salah satu terapi komplementer dalam pemberian intervensi keperawatan di rumah sakit untuk menurunkan kecemaasan pasien terutama pada saat pertama kali melakukan kemoterapi. Background : Chemotherapy make patients who diagnosed with cancer have feelings of aneasy, anxiety and fear of the shadows of death that haunts every time. The aim of this study was to investigate the effect of Spiritual Guided imagery and music (SGIM) on the anxiety of cancer patients undergoing chemotherapy. Method : This is a Quasi experimental research study with one group pre and post test design with control group design. The study was conducted in a chemotherapy room in 30 patients who received SGIM therapy and 30 as a control group. Anxiety scores were measured 3 times: 1 day and 30 minutes before chemotherapy and 1 day after chemotherapy. Anxiety is measured using the Hamilton Anciety Rating Scale (HARS). Data were analyzed by GLM repeated mesure test with SPSS 21. Results : as many as 73.3% and 26.7 patients had severe and moderate anxiety during the pre test becoming moderate anxiety (56.7%) and mild anxiety (43.3%) at the measurement 30 minutes before chemotherapy and at the end of the measurement had mild anxiety (63.4%) and (33,3 %) had no anxiety, and the rest 3.3% still have experience of severe anxiety in the SGIM group. The Analysis of GLM repeated measure test with post hock banferoni known p value = 0.001, with the r sguare value = 0.29. This shows that there is an effect of SGIM on anxiety reduction of cancer patients during chemotherapy with a contribution of 29% ,and the rest are affected by other variables. Conclusion : SGIM Therapy reduces anxiety of cancer patients undergoing chemotherapy. The research recommendation is that SGIM can be applied as one of the complementary therapies in providing nursing interventions in hospitals to reduce patient anxiety, especially at the first time undergoing chemotherapy
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2

Huang, Gang-Hai, Yu-Yong Jiao, Xiu-Li Zhang, Amoussou-Coffi Adoko, and Shu-Cai Li. "Generating Irregular Models for 3D Spherical-Particle-Based Numerical Methods." Journal of Applied Mathematics 2013 (2013): 1–7. http://dx.doi.org/10.1155/2013/302680.

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The realistic representation of an irregular geological body is essential to the construction of a particle simulation model. A three-dimensional (3D) sphere generator for an irregular model (SGIM), which is based on the platform of Microsoft Foundation Classes (MFC) in VC++, is developed to accurately simulate the inherent discontinuities in geological bodies. OpenGL is employed to visualize the modeling in the SGIM. Three key functions, namely, the basic-model-setup function, the excavating function, and the cutting function, are implemented. An open-pit slope is simulated using the proposed model. The results demonstrate that an extremely irregular 3D model of a geological body can be generated using the SGIM and that various types of discontinuities can be inserted to cut the model. The data structure of the model that is generated by the SGIM is versatile and can be easily modified to match various numerical calculation tools. This can be helpful in the application of particle simulation methods to large-scale geoengineering projects.
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3

AULT, ALICIA. "SGIM Launches Payment Reform Commission." Family Practice News 42, no. 5 (2012): 59. http://dx.doi.org/10.1016/s0300-7073(12)70266-9.

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AULT, ALICIA. "SGIM Launches Payment Reform Commission." Internal Medicine News 45, no. 5 (2012): 6–7. http://dx.doi.org/10.1016/s1097-8690(12)70224-x.

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5

Donohoe, Martin T. "SGIM use of news media." Journal of General Internal Medicine 10, no. 9 (1995): 532. http://dx.doi.org/10.1007/bf02602415.

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6

AULT, ALICIA. "SGIM Launches Payment Reform Commission." Clinical Endocrinology News 7, no. 4 (2012): 46. https://doi.org/10.1016/s1558-0164(12)70119-0.

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7

Skootsky, Samuel A., and Lee Goldman. "Office procedures in SGIM policy statement." Journal of General Internal Medicine 10, no. 3 (1995): 176. http://dx.doi.org/10.1007/bf02599681.

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8

McMahon, Laurence F., Rebecca Jennifer Beyth, Alfred Burger, et al. "Enhancing Patient-Centered Care: SGIM and Choosing Wisely." Journal of General Internal Medicine 29, no. 3 (2013): 432–33. http://dx.doi.org/10.1007/s11606-013-2617-8.

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9

Freund, Karen M. "Gender Equity in Leadership: SGIM, It’s Our Problem!" Journal of General Internal Medicine 35, no. 6 (2020): 1631–32. http://dx.doi.org/10.1007/s11606-020-05710-8.

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10

Koizumi, Shunzo. "A Report from the 31st SGIM Meeting in Pittsburgh." General Medicine 9, no. 2 (2008): 83–84. http://dx.doi.org/10.14442/general.9.83.

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11

Levings, Renee S., Steven P. Djordjevic, and Ruth M. Hall. "SGI2, a Relative of Salmonella Genomic Island SGI1 with an Independent Origin." Antimicrobial Agents and Chemotherapy 52, no. 7 (2008): 2529–37. http://dx.doi.org/10.1128/aac.00189-08.

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ABSTRACT Multiply antibiotic-resistant Salmonella enterica serovar Emek strains isolated in Australia and the United Kingdom had similar features, suggesting that they all belong to a single clone. These strains all contain SGI2 (formerly SGI1-J), an independently formed relative of Salmonella genomic island SGI1. In SGI2, the complex class 1 integron which includes all of the resistance genes is not located between tnpR (S027) and S044 as in SGI1 and SGI1 variants. Instead, tnpR was found to be adjacent to S044, and the integron is located 6.9 kb away, within S023. In both SGI1 and SGI2, the 25-bp inverted repeats that mark the outer ends of class 1 integrons are flanked by a 5-bp duplication of the target, indicating that incorporation of the integron was by transposition. A small number of differences between the sequences of the backbones of SGI1 and SGI2 were also found. Hence, a class 1 integron has entered two different variants of the SGI backbone to generate two distinct lineages. Despite this, the integron in SGI2 has a complex structure that is very similar to that of In104 in SGI1. Differences are in the cassette arrays and in the gene which encodes the chloramphenicol and florfenicol efflux protein. The CmlA9 protein, encoded by InEmek, is only 92.8% identical to FloRc (also a CmlA family protein) from SGI1. A variant form of SGI2, SGI2-A, which has lost the tet(G) and cmlA9 resistance determinants, was found in one strain.
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12

Zhao, Yuying, Yunfei Hu, Pingpeng Yuan, and Hai Jin. "Maximizing Influence Over Streaming Graphs with Query Sequence." Data Science and Engineering 6, no. 3 (2021): 339–57. http://dx.doi.org/10.1007/s41019-021-00158-0.

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AbstractNow, with the prevalence of social media, such as Facebook, Weibo, how to maximize influence of individuals, products, actions in new media is of practical significance. Generally, maximizing influence first needs to identify the most influential individuals since they can spread their influence to most of others in the social media. Many studies on influence maximization aimed to select a subset of nodes in static graphs once. Actually, real graphs are evolving. So, influential individuals are also changing. In these scenarios, people tend to select influential individuals multiple times instead of once. Namely, selections are raised sequentially, forming a sequence (query sequence). It raises several new challenges due to changing influential individuals. In this paper, we explore the problem of Influence Maximization over Streaming Graph (SGIM). Then, we design a compact solution for storing and indexing streaming graphs and influential nodes that eliminates the redundant computation. The solution includes Influence-Increment-Index along with two sketch-centralized indices called Influence-Index and Reverse-Influence-Index. Computing influence set of nodes will incur a large number of redundant computations. So, these indices are designed to keep track of the nodes’ influence in sketches. Finally, with the indexing scheme, we present the algorithm to answer SGIM queries. Extensive experiments on several real-world datasets demonstrate that our method is competitive in terms of both efficiency and effectiveness owing to the design of index.
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13

Knight, Christopher L., Donna M. Windish, Steven A. Haist, et al. "The SGIM TEACH Program: A Curriculum for Teachers of Clinical Medicine." Journal of General Internal Medicine 32, no. 8 (2017): 948–52. http://dx.doi.org/10.1007/s11606-017-4053-7.

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14

Goroll, Allan H., Gail Morrison, Eric B. Bass, et al. "Reforming the Core Clerkship in Internal Medicine: The SGIM/CDIM Project." Annals of Internal Medicine 134, no. 1 (2001): 30. http://dx.doi.org/10.7326/0003-4819-134-1-200101020-00011.

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15

Simamora, Antonia Yunita, Putri Naysila Zahwa, Nurmala Sari, Ratnasari Laia, and Eva Latifah Nurhayati. "Efektivitas Spiritual Guided Imagery and Music Terhadap Kecemasan Pasien Kanker yang Menjalani Kemoterapi di RSU Royal Prima Medan." MAHESA : Malahayati Health Student Journal 4, no. 8 (2024): 3166–79. http://dx.doi.org/10.33024/mahesa.v4i8.14971.

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ABSTRACT Cancer is a disease or disorder caused by anomalies in initially healthy tissue that attack the body. One method of treatment for cancer is chemotherapy, but it tends to cause anxiety in cancer patients, patiens often experience emotional strees due to uncertainty about the future due to the disease they suffer from. Therefore, providing Spritual Guided Imagery and Music (SGIM) as an intervention. This research applies a single pre-and post design and also a quasi-experimental design. In this study, there were 30 patients who would represent 76 patients who were undergoing chemotherapy. The results obtained after using Guided Imagery and Music were 0.03 < 0.05. This shows that there is a difference in anxiety before and after Guided Imagery and Music in cancer patients undergoing chemotherapy. Keywords: Cancer, Chemoterapy, Guided Imagery and Music ABSTRAK Kanker adalah salah satu penyakit atau kelainan yang disebabkan oleh anomaly pada jaringan yang awalnya sehat berbalik menyerang tubuh. Salah satu metode pengobatan dalam penyakit kanker adalah kemoterapi, namun cenderung menyebabkan kecemasan pada pasien kanker, pasien sering mengalami stress emosional disebabkan ketidakpastian mengenai masa depan akibat penyakit yang dideritanya. Oleh karena itu, pemberian Spritual Guided Imagery and Music (SGIM) sebagai interverensi. Penelitian ini menerapkan design single pre-and post dan juga design quasi eksperimental. Pada penelitian ini sebanyak 30 pasien yang akan mewakili dari 76 pasien yang sedang menjalani kemoterapi. Hasil yang didapatkan setelah dilakukan Guided Imagery and Music 0,03 < 0,05. Hal ini menunjukkan bahwa adanya perbedaan kecemasan sebelum dan sesudah dilakukan Guided Imagery and Music pada pasien kanker yang menjalani kemoterapi. Kata Kunci: Kanker, Kemoterapi, Guided Imagery and Music
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16

Rubenstein, Lisa V., and Nancy A. Rigotti. "The SGIM Policy Analysis: Supporting the Generalist Voice for Participation in Policymaking." Journal of General Internal Medicine 24, no. 7 (2009): 888–89. http://dx.doi.org/10.1007/s11606-009-1025-6.

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17

Wilson, Neil L., and Ruth M. Hall. "Unusual Class 1 Integron Configuration Found in Salmonella Genomic Island 2 from Salmonella enterica Serovar Emek." Antimicrobial Agents and Chemotherapy 54, no. 1 (2009): 513–16. http://dx.doi.org/10.1128/aac.00895-09.

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ABSTRACT Salmonella genomic island 2 (SGI2) is an independently derived genomic island related to SGI1 with the integron in a different position. The integron in SGI2 was found to include an additional 2.1 kb derived from the tni module of Tn5058, Tn502, or Tn512 that was not detected previously. Independent evolution of the backbone was confirmed with 21 single base differences found in over 11.5 kb, representing 40% of the 27.4-kb SGI2 backbone.
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18

Shin, Myeung-Ryeul, and Yong-Seob Lee. "Effects of the Astronomical Learning Program using SGIM on Metacognition and Science Process Skills in the Elementary Scientific Gifted." Journal of Gifted/Talented Education 21, no. 3 (2011): 719–39. http://dx.doi.org/10.9722/jgte.2011.21.3.719.

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19

de Curraize, Claire, Eliane Siebor, Catherine Neuwirth, and Ruth M. Hall. "SGI0, a relative of Salmonella genomic islands SGI1 and SGI2, lacking a class 1 integron, found in Proteus mirabilis." Plasmid 107 (January 2020): 102453. http://dx.doi.org/10.1016/j.plasmid.2019.102453.

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20

Fazio, Sara B., Shobhina Chheda, Susan Hingle, et al. "The Challenges of Teaching Ambulatory Internal Medicine: Faculty Recruitment, Retention, and Development: An AAIM/SGIM Position Paper." American Journal of Medicine 130, no. 1 (2017): 105–10. http://dx.doi.org/10.1016/j.amjmed.2016.09.004.

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21

Lindquist, Lee A., Rachel K. Miller, Wayne S. Saltsman, et al. "SGIM-AMDA-AGS Consensus Best Practice Recommendations for Transitioning Patients’ Healthcare from Skilled Nursing Facilities to the Community." Journal of General Internal Medicine 32, no. 2 (2016): 199–203. http://dx.doi.org/10.1007/s11606-016-3850-8.

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22

Levine, Rachel B., Rebecca A. Harrison, Hilit F. Mechaber, Christopher Phillips, and Thomas H. Gallagher. "Professional Characteristics and Job Satisfaction Among SGIM Members: A Comparison of Part-time and Full-time Physician Members." Journal of General Internal Medicine 23, no. 8 (2008): 1218–21. http://dx.doi.org/10.1007/s11606-008-0635-8.

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23

Zheng, Maoji, Ziyu Xu, Qiming Xia, Hai Wu, Chenglu Wen, and Cheng Wang. "Seg2Box: 3D Object Detection by Point-Wise Semantics Supervision." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 10 (2025): 10591–98. https://doi.org/10.1609/aaai.v39i10.33150.

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LIDAR-based 3D object detection and semantic segmentation are critical tasks in 3D scene understanding. Traditional detection and segmentation methods supervise their models through bounding box labels and semantic mask labels. However, these two independent labels inherently contain significant redundancy. This paper aims to eliminate the redundancy by supervising 3D object detection using only semantic labels. However, the challenge arises due to the incomplete geometry structure and boundary ambiguity of point cloud instances, leading to inaccurate pseudo-labels and poor detection results. To address these challenges, we propose a novel method, named Seg2Box. We first introduce a Multi-Frame Multi-Scale Clustering (MFMS-C) module, which leverages the spatio-temporal consistency of point clouds to generate accurate box-level pseudo-labels. Additionally, the Semantic-Guiding Iterative-Mining Self-Training (SGIM-ST) module is proposed to enhance the performance by progressively refining the pseudo-labels and mining the instances without generating pseudo-labels. Experiments on the Waymo Open Dataset and nuScenes Dataset show that our method significantly outperforms other competitive methods by 23.7% and 10.3% in mAP, respectively. The results demonstrate the great label-efficient potential and advancement of our method.
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24

Vasiu, Ioana, and Lucian Vasiu. "Cyber Extortion and Threats: Analysis of the United States Case Law." Masaryk University Journal of Law and Technology 14, no. 1 (2020): 3–28. https://doi.org/10.5817/MUJLT2020-1-1.

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This article presents an analysis of the cyber extortion and threats cases brought to the United States courts. The inquiry employed content analysis to identify important elements and attributes and answers research questions concerning essential attributes, legal elements, and how do the courts interpret these offenses. The article extends the understanding of this phenomenon by providing a thorough discussion of the conceptual issues and characteristics and an analysis of the most important litigation aspects, such as intent, true threats, sentencing, and the insanity defense. The article concludes with recommendations for stakeholders, to more effectively address the phenomenon.
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25

Jackson, Angela, Robert B. Baron, Jeffrey Jaeger, Mark Liebow, Margaret Plews-Ogan, and Mark D. Schwartz. "Addressing the Nation’s Physician Workforce Needs: The Society of General Internal Medicine (SGIM) Recommendations on Graduate Medical Education Reform." Journal of General Internal Medicine 29, no. 11 (2014): 1546–51. http://dx.doi.org/10.1007/s11606-014-2847-4.

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Bandara, H. M. Ravindu T., K. S. Priyanayana, A. G. Buddhika P. Jayasekara, D. P. Chandima, and R. A. R. C. Gopura. "An Intelligent Gesture Classification Model for Domestic Wheelchair Navigation with Gesture Variance Compensation." Applied Bionics and Biomechanics 2020 (January 30, 2020): 1–11. http://dx.doi.org/10.1155/2020/9160528.

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Elderly and disabled population is rapidly increasing. It is important to uplift their living standards by improving the confidence towards daily activities. Navigation is an important task, most elderly and disabled people need assistance with. Replacing human assistance with an intelligent system which is capable of assisting human navigation via wheelchair systems is an effective solution. Hand gestures are often used in navigation systems. However, those systems do not possess the capability to accurately identify gesture variances. Therefore, this paper proposes a method to create an intelligent gesture classification system with a gesture model which was built based on human studies for every essential motion in domestic navigation with hand gesture variance compensation capability. Experiments have been carried out to evaluate user remembering and recalling capability and adaptability towards the gesture model. Dynamic Gesture Identification Module (DGIM), Static Gesture Identification Module (SGIM), and Gesture Clarifier (GC) have been introduced in order to identify gesture commands. The proposed system was analyzed for system accuracy and precision using results of the experiments conducted with human users. Accuracy of the intelligent system was determined with the use of confusion matrix. Further, those results were analyzed using Cohen’s kappa analysis in which overall accuracy, misclassification rate, precision, and Cohen’s kappa values were calculated.
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27

Weiner, Saul J., Jeffrey L. Jackson, and Sarajane Garten. "Measuring Continuing Medical Education Outcomes: A Pilot Study of Effect Size of Three CME Interventions at an SGIM Annual Meeting." Journal of General Internal Medicine 24, no. 5 (2009): 626–29. http://dx.doi.org/10.1007/s11606-009-0902-3.

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28

"Ausserordentliche Generalversammlungen SGAM/SGIM und Gründungsversammlung SGAIM." PrimaryCare 15, no. 21 (2015): 370. http://dx.doi.org/10.4414/pc-d.2015.01129.

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"Nachvollzug Zusammenführung SGAM und SGIM." Schweizerische Ärztezeitung 98, no. 06 (2017): 178. http://dx.doi.org/10.4414/saez.2017.05368.

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"Eine Fusion von SGIM und SGAM Stellungsnahme der Chefärztevereinigung der SGIM." PrimaryCare 7, no. 16 (2007): 259. http://dx.doi.org/10.4414/pc-d.2007.07560.

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"Eine Fusion von SGIM und SGAM Stellungsnahme der Chefärztevereinigung der SGIM." PrimaryCare 7, no. 16 (2007): 259. http://dx.doi.org/10.4414/pc-f.2007.07560.

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"SGIM-Foundation." Schweizerische Ärztezeitung 94, no. 45 (2013): 1706. http://dx.doi.org/10.4414/saez.2013.02049.

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"SGIM-Foundation." Bulletin des Médecins Suisses 94, no. 45 (2013): 1706. http://dx.doi.org/10.4414/bms.2013.02049.

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"SGIM-Foundation." Bulletin des Médecins Suisses 96, no. 48 (2015). http://dx.doi.org/10.4414/bms.2015.04201.

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"SGIM Foundation." Bulletin des Médecins Suisses 97, no. 4950 (2016). http://dx.doi.org/10.4414/bms.2016.05215.

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"SGIM-Foundation." Bulletin des Médecins Suisses 99, no. 0102 (2018): 16. http://dx.doi.org/10.4414/bms.2018.06338.

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"SGIM Foundation." Schweizerische Ärztezeitung 97, no. 4950 (2016). http://dx.doi.org/10.4414/saez.2016.05215.

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"SGIM-Foundation." Schweizerische Ärztezeitung 99, no. 0102 (2018): 16. http://dx.doi.org/10.4414/saez.2018.06338.

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"SGIM website." Journal of General Internal Medicine 12, no. 12 (1997): 790. http://dx.doi.org/10.1007/bf02696918.

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"SGIM website." Journal of General Internal Medicine 12, no. 9 (1997): 585. http://dx.doi.org/10.1007/bf02719208.

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"SGIM-Foundation." Schweizerische Ärztezeitung 96, no. 48 (2015). http://dx.doi.org/10.4414/saez.2015.04201.

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"Die «Elefantenrunde» oder: «SGIM und SGAM haben (fast) fusioniert!»." PrimaryCare 6, no. 21 (2006): 390. http://dx.doi.org/10.4414/pc-f.2006.07135.

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"Die «Elefantenrunde» oder: «SGIM und SGAM haben (fast) fusioniert!»." PrimaryCare 6, no. 21 (2006): 390. http://dx.doi.org/10.4414/pc-d.2006.07135.

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"SGIM Application for Membership." Journal of General Internal Medicine 16, s1 (2001): 0. http://dx.doi.org/10.1046/j.1525-1497.2001.0160s1000.x.

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"1. SGIM Great Update 2011." PrimaryCare 11, no. 14 (2011): 246. http://dx.doi.org/10.4414/pc-d.2011.08943.

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"Managementfortbildung SGIM/FHNW: Spannender Praxistransfer." PrimaryCare 15, no. 03 (2015): 38–39. http://dx.doi.org/10.4414/pc-d.2015.00909.

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"Managementfortbildung SGIM/FHNW: Spannender Praxistransfer." PrimaryCare 15, no. 03 (2015): 38–39. http://dx.doi.org/10.4414/pc-f.2015.00909.

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48

"Offener Brief: Fortbildungskontrolle der SGIM." PrimaryCare 4, no. 47 (2004): 939–40. http://dx.doi.org/10.4414/pc-f.2004.06376.

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"Offener Brief: Fortbildungskontrolle der SGIM." PrimaryCare 4, no. 47 (2004): 939–40. http://dx.doi.org/10.4414/pc-d.2004.06376.

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"2001 SGIM annual meeting schedule." Journal of General Internal Medicine 16, S1 (2001): 1–5. http://dx.doi.org/10.1046/j.1525-1497.2001.0160s1001.x.

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