Women deans and department chairs in medical education: a study of enabling and inhibiting factors impacting their leadership success

Women are occupying leadership roles in medical education, yet are underrepresented in senior leadership positions. This qualitative study explored the enabling and inhibiting factors that select women deans and department chairs experienced throughout their career ascent. The qualitative research included eight women, both deans and department chairs, in medical education. The deans and department chairs participated in an interview where the primary data were obtained. Qualitative research methods were used to analyze the data, and the findings were presented in narrative format. The findings were consistent with the literature review and reviewed similarities in enabling and inhibiting factors experienced. The findings suggest specific leadership styles, characteristics, and skillsets for aspiring deans and department chairs to consider. The recommendations suggest that women considering senior leadership positions in medical education may benefit from a gender-neutral workplace, which supports the professional growth of women through development opportunities in areas such as finance and strategic decision-making. A collaborative leadership approach, along with decision-making, flexibility, humility and confidence, were identified as common characteristics enabling leadership success. Women aspiring to obtain senior leadership positions may also benefit from encouragement and mentorship in obtaining department chair positions to better prepare them to move into dean roles

Weakly-Supervised Joint Sentiment-Topic Detection from Text

publication-status: Acceptedtypes: ArticleCopyright © 2012 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Sentiment analysis or opinion mining aims to use automated tools to detect subjective information such as opinions, attitudes, and feelings expressed in text. This paper proposes a novel probabilistic modeling framework called joint sentiment-topic (JST) model based on latent Dirichlet allocation (LDA), which detects sentiment and topic simultaneously from text. A reparameterized version of the JST model called Reverse-JST, by reversing the sequence of sentiment and topic generation in the modelling process, is also studied. Although JST is equivalent to Reverse-JST without hierarchical prior, extensive experiments show that when sentiment priors are added, JST performs consistently better than Reverse-JST. Besides, unlike supervised approaches to sentiment classification which often fail to produce satisfactory performance when shifting to other domains, the weakly-supervised nature of JST makes it highly portable to other domains. This is verified by the experimental results on datasets from five different domains where the JST model even outperforms existing semi-supervised approaches in some of the datasets despite using no labelled documents. Moreover, the topics and topic sentiment detected by JST are indeed coherent and informative. We hypothesize that the JST model can readily meet the demand of large-scale sentiment analysis from the web in an open-ended fashion

The Supreme Court Forecasting Project: Legal and Political Science Approaches to Predicting Supreme Court Decision-Making

This Essay reports the results of an interdisciplinary project comparing political science and legal approaches to forecasting Supreme Court decisions. For every argued case during the 2002 Term, we obtained predictions of the outcome prior to oral argument using two methods—one a statistical model that relies on general case characteristics, and the other a set of independent predictions by legal specialists. The basic result is that the statistical model did better than the legal experts in forecasting the outcomes of the Term’s cases: The model predicted 75% of the Court’s affirm/reverse results correctly, while the experts collectively got 59.1% right. These results are notable, given that the statistical model disregards information about the specific law or facts of the cases. The model’s relative success was due in large part to its ability to predict more accurately the important votes of the moderate Justices (Kennedy and O’Connor) at the center of the current Court. The legal experts, by contrast, did best at predicting the votes of the more ideologically extreme Justices, but had difficulty predicting the centrist Justices. The relative success of the two methods also varied by issue area, with the statistical model doing particularly well in forecasting “economic activity” cases, while the experts did comparatively better in the “judicial power” cases. In addition to reporting the results in detail, the Essay explains the differing methods of prediction used and explores the implications of the findings for assessing and understanding Supreme Court decision-making.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/116230/1/columbia04.pd

Cost-Effectiveness of Peer-Delivered Interventions for Cocaine and Alcohol Abuse among Women: A Randomized Controlled Trial

<div><h3>Aims</h3><p>To determine whether the additional interventions to standard care are cost-effective in addressing cocaine and alcohol abuse at 4 months (4 M) and 12 months (12 M) from baseline.</p> <h3>Method</h3><p>We conducted a cost-effectiveness analysis of a randomized controlled trial with three arms: (1) NIDA's Standard intervention (SI); (2) SI plus a Well Woman Exam (WWE); and, (3) SI, WWE, plus four Educational Sessions (4ES).</p> <h3>Results</h3><p>To obtain an additional cocaine abstainer, WWE compared to SI cost $7,223 at 4 M and$3,611 at 12 M. Per additional alcohol abstainer, WWE compared to SI cost $3,611 and$7,223 at 4 M and 12 M, respectively. At 12 M, 4ES was dominated (more costly and less effective) by WWE for abstinence outcomes.</p> <h3>Conclusions</h3><p>To our knowledge, this is the first cost-effectiveness analysis simultaneously examining cocaine and alcohol abuse in women. Depending on primary outcomes sought and priorities of policy makers, peer-delivered interventions can be a cost-effective way to address the needs of this growing, underserved population.</p> <h3>Trial Registration</h3><p>ClinicalTrials.gov <a href="http://www.clinicaltrials.gov/ct2/show/NCT01235091">NCT01235091</a></p> </div

Sentimen Analisis Pelanggan Shopee di Twitter dengan Algoritma Naive Bayes

Sentimen analisis merupakan suatu bidang studi yang menganalisis tentang opini-opini seseorang, mengevaluasi, menilai, perilaku, dan emosi seperti produk-produk,&nbsp; layanan, kejadian, serta topik-topik. Sentimen analisis dalam dunia bisnis biasanya dipakai untuk menganlisis suatu kebutuhan masyarakat dan kebutuhan pasar, yang diharapkan mampu menyusun strategi pemasaran yang dapat meningkatkan pendapatan perusahaan mereka. Penelitian ini mengambil data-datanya dari grup Shopee yang ada di Twitter. Shopee merupakan salah satu market place yang sering digunakan oleh masyarakat Indonesia maupun masyarakat dinegara lain. Market place Shopee yang menjual jasa maupun menjual barang-barang untuk kebutuhan sehari-hari seperti elektronik,alat-alat makan, pulsa, maupun tiket pesawat, tiket kereta, dan masih banyak lagi jasa yang dijual oleh Shopee.Tujuan dari dilakukan penelitian ini yaitu untuk mengetahui&nbsp; berapa jumlah riview dari komentar positif maupun komentar negatif pada grup Shopee yang ada di media sosial Twitter, pengambilan datanya menggunakan Rstudio, Rstudio merupakan suatu aplikasi yang dipakai untuk menulis program-program dengan menggunakan bahasa R. Aplikasi Rstudio dapat dijalankan pada operasi sistem Windows, Linux, maupun Apple. Serta untuk proses menghitung menggunakan metode Naive Bayes yang dimasukkan&nbsp; kedalam sentimen analisis yang&nbsp; pengambilan data di ambil dari grup Shopee. Dalam penghitungan tingkat keakurasian dengan menggunakan convusion matriks. Hasil dari penelitian ini yaitu dapat diketahui bahwa jumlah komentar positif maupun negatif setara yaitu 150:150, karena data yang diambil adalah 300 data dan tingkat keakurasiannya adalah sebesar 97%.Sentiment analysis is a field of study that analyzes a person's opinions, evaluates, judges, behaviors, and emotions such as products, services, events, and topics. Sentiment analysis in the business world is usually used to analyze the needs of the community and market needs, which are expected to be able to develop marketing strategies that can increase their company's income. This study takes the data from the Shopee group on Twitter. Shopee is one of the market places that are often used by the people of Indonesia and people in other countries. Shopee's market place that sells services or sells goods for daily needs such as electronics, cutlery, credit, as well as airline tickets, train tickets, and many more services sold by Shopee. The purpose of this research is to find out how many reviews of positive comments and negative comments on the Shopee group on Twitter social media, the data collection uses Rstudio. The Rstudio application can be run on Windows, Linux, or Apple operating systems. As well as for the calculation process using the Naive Bayes method which is included in the sentiment analysis where data collection is taken from the Shopee group. In calculating the level of accuracy using the convusion matrix. The results of this study are that it can be seen that the number of positive and negative comments is equal, namely 150:150, because the data taken is 300 data and the level of accuracy is 97%

Treating patients as persons : a capabilities approach to support delivery of person-centered care

Peer reviewedPublisher PD