203,873 research outputs found
HUBUNGAN BODY IMAGE DENGAN ASUPAN LEMAK DAN KEBIASAAN BEROLAHRAGA DI MASA PANDEMI COVID-19 PADA MAHASISWA STIKES MITRA KELUARGA BEKASI
No full textPendahuluan: Mahasiswa berada pada rentang usia 18-25 tahun sering mengalami persepsi terhadap citra tubuhnya. Mahasiswa yang memiliki persepsi buruk terhadap tubuhnya kemungkinan akan melakukan penurunan berat badan agar terlihat menarik secara fisik. Salah satu caranya dengan membatasi asupan lemak dan mulai melakukan kebiasaan berolahraga. Penelitian ini bertujuan untuk menganalisis hubungan body image dengan asupan lemak dan kebiasaan berolahraga di masa pandemi Covid-19 pada mahasiswa STIKes Mitra Keluarga.
Metode: Penelitian ini adalah penelitian kuantitatif dengan desain cross sectional. Jumlah sampel berjumlah 180 mahasiswa STIKes Mitra Keluarga yang dipilih menggunakan metode Consecutive sampling. Pengumpulan data menggunakan kuesioner Body Shape Questionnaire, Food Frequency Questionare, Food Recall 24H, Frekuensi kebiasaan berolahraga dan dianalisis menggunakan uji Chi-square.
Hasil: Hasil analisis menunjukkan nilai p-value hubungan antar variabel yaitu body image dengan asupan lemak p-value= 0,881 , dan body image dengan kebiasaan berolahraga p-value= 0,274.
Kesimpulan: Kesimpulan dari penelitian ini tidak terdapat hubungan antara citra tubuh (body image) dengan asupan lemak dan kebiasaan berolahraga pada masa Pandemi Covid-19 di STIKes Mitra Keluarga
pralay-mitra/PROFOUND v1.0
No full text<p>This contains the codes and the input files necessary to change in protein foldability associated with Multi Point Deletions in a newly defined protein structure database.</p>
Estimating risks of identification disclosure in partially synthetic data
No full textTo limit disclosures, statistical agencies and other data disseminators can release partially synthetic, public use microdata sets. These comprise the units originally surveyed, but some collected values, for example sensitive values at high risk of disclosure or values of key identifiers, are replaced with multiple draws from statistical models. Because the original records are on the file, there remain risks of identifications. In this paper, we describe how to evaluate identification disclosure risks in partially synthetic data, accounting for released information from the multiple datasets, the model used to generate synthetic values, and the approach used to select values to synthesize. We illustrate the computations using the Survey of Youths in Custody
A comparison of two methods of estimating propensity scores after multiple imputation
No full textIn many observational studies, analysts estimate treatment effects using propensity scores, e.g., by matching or sub classifying on the scores. When some values of the covariates are missing, analysts can use multiple imputation to fill in the missing data, estimate propensity scores based on the m completed datasets, and use the propensity scores to estimate treatment effects. We compare two approaches to implementing this process. In the first, the analyst estimates the treatment effect using propensity score matching within each completed data set, and averages the m treatment effect estimates. In the second approach, the analyst averages the m propensity scores for each record across the completed datasets, and performs propensity score matching with these averaged scores to estimate the treatment effect. We compare properties of both methods via simulation studies using artificial and real data. The simulations suggest that the second method has greater potential to produce substantial bias reductions than the first
Propensity score matching with missing covariates via iterated, sequential multiple imputation
No full textIn many observational studies, analysts estimate causal effects using propensity score matching. Estimation of propensity scores is complicated when covariate values intended for collection are in fact missing. To handle the missing data, one approach is to use multiple imputation to create completed datasets, and compute propensity scores from these datasets. However, inaccurate imputation models can result in ineffective matching, thereby limiting reductions in bias. We propose a multiple imputation approach based on chained equations in which the researcher gradually reduces the set of control units used to estimate the imputation models. This approach can reduce the influence of control records far from the treated units’ region of the covariate space on the estimation of parameters in the imputation model, which can result in more plausible imputations and better balance in the true covariate distributions. This approach can be conveniently implemented with standard multiple imputation software for missing data. Using simulations, we find that the approach can improve estimation when imputation models are mis-specified; however, it can be ineffective when imputation models are correctly specified. This suggests using the approach as part of sensitivity analysis in causal inference. We apply the approach to an observational study of the effect of breast-feeding on the child’s educational outcomes later in life
Universal Statistical Properties of Inertial-particle Trajectories in Three-dimensional, Homogeneous, Isotropic, Fluid Turbulence
No full textWe obtain new universal statistical properties of heavy-particle trajectories in three-dimensional, statistically steady, homogeneous, and isotropic turbulent flows by direct numerical simulations. We show that the probability distribution functions (PDFs) P(Φ), of the angle Φ between the Eulerian velocity u and the particle velocity v, at a point and time, scales as P(Φ) ∼Φ−, with a new universal exponent ≃ 4
Estimating propensity scores with missing covariate data using general location mixture models
No full textIn many observational studies, researchers estimate causal effects using propensity scores, e.g., by matching or sub-classifying on the scores. Estimation of propensity scores is complicated when some values of the covariates aremissing. We propose to use multiple imputation to create completed datasets, from which propensity scores can be estimated, with a general location mixture model. The model assumes that the control units are a latent mixture of (i)units whose covariates are drawn from the same distributions as the treated units’ covariates and (ii) units whose covariates are drawn from different distributions. This formulation reduces the influence of control units outside the treated units’ region of the covariate space on the estimation of parameters in the imputation model, which can result in more plausible imputations and better balance in the true covariate distributions. We illustrate the benefits of 1 the latent class modeling approach with simulations and with an observationalstudy of the effect of breast feeding on children’s cognitive abilities
Self-Organised Learning Environments (SOLEs) in an English School: an example of transformative pedagogy?
No full textSelf-Organised Learning Environments (SOLEs) are models of learning in which students self-organise in groups and learn using a computer connected to the internet with minimal teacher support. The original ‘hole in the wall’ experiments in India are now applied to classrooms around the world. The idea of SOLEs is a social innovation that is inspiring educators (in schooling and also business contexts) everywhere, as demonstrated by Mitra’s award of the 2013 TED prize. However, when SOLEs are located in classrooms, a number of questions arise. Are SOLEs easily adapted for the classroom context? Is the impact on learning as transformative as suggested by the original ideas? This paper considers in detail the application over two years by one teacher, using SOLEs in a Year 4 classroom in an urban North East England primary school, in partnership with university researchers Dolan, Mitra and Leat. Issues of innovation and transformation are discussed, informed by the ideas of Bernstein, Engestrom, and Giroux. The SOLE concept, although flexible, has the potential to offer a divergent, radical transformative pedagogy. This sits somewhat uncomfortably alongside more convergent approaches which position the learner as subservient to the curriculum, with the task of merely mastering subject matter prescribed by the teacher. However, what is notable from this analysis is that transformative pedagogy seems to be positioned alongside, rather than in conflict with, the dominant educational framework
Critical realignment and democratic deepening: the Parliamentary elections of 2014 and 2019 in India
No full text‘Watershed’, ‘historic’ ‘epochal’ were used to describe India’s 2014 General Elections. The Bharatiya Janata Party secured the first single party majority in three decades, forming the Government, as the National Democratic Alliance. We argue that the sixteenth Lok Sabha elections and its aftermath constitute a re-alignment, not a clean break with the past, and that this is confirmed by the 2019 General Elections
Mitra en Hispania
No full textConjunto de datos sobre restos arqueológicos del culto al dios Mitra en la Hispania romana a los que se hace referencia en:
Mitra en Hispania, recurso electrónico. Autor: Jaime Alvar Ezquerr
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