Measuring pregnancy planning: An assessment of the London Measure of Unplanned Pregnancy among urban, south Indian women

Copyright © 2010 Corinne H. Rocca et al. This open-access work is published under the terms of the Creative Commons Attribution NonCommercial License 2.0 Germany, which permits use, reproduction & distribution in any medium for non-commercial purposes, provided the original author(s) and source are given credit. See http:// creativecommons.org/licenses/by-nc/2.0/de/.We evaluated the psychometric properties of the London Measure of Unplanned Pregnancy among Indian women using classical methods and Item Response Modeling. The scale exhibited good internal consistency and internal structure, with overall scores correlating well with each item’s response categories. Items performed similarly for pregnant and non-pregnant women, and scores decreased with increasing parity, providing evidence for validity. Analyses detected small disadvantages, including low endorsement of middle response categories and some evidence of differential item functioning by parity. We conclude that the LMUP is suitable for use in India and recommend steps for improving scale performance for this cultural context.National Institute of Child Health and Human Development and the Levis Strauss Foundation

Charter-School Management Organizations: Diverse Strategies and Diverse Student Impacts

Examines the growth of charter school management organizations, characteristics of students served, and use of resources; CMO practices; impact on students, including middle school test scores; and structures and practices linked to positive outcomes

Beyond Volume: The Impact of Complex Healthcare Data on the Machine Learning Pipeline

From medical charts to national census, healthcare has traditionally operated under a paper-based paradigm. However, the past decade has marked a long and arduous transformation bringing healthcare into the digital age. Ranging from electronic health records, to digitized imaging and laboratory reports, to public health datasets, today, healthcare now generates an incredible amount of digital information. Such a wealth of data presents an exciting opportunity for integrated machine learning solutions to address problems across multiple facets of healthcare practice and administration. Unfortunately, the ability to derive accurate and informative insights requires more than the ability to execute machine learning models. Rather, a deeper understanding of the data on which the models are run is imperative for their success. While a significant effort has been undertaken to develop models able to process the volume of data obtained during the analysis of millions of digitalized patient records, it is important to remember that volume represents only one aspect of the data. In fact, drawing on data from an increasingly diverse set of sources, healthcare data presents an incredibly complex set of attributes that must be accounted for throughout the machine learning pipeline. This chapter focuses on highlighting such challenges, and is broken down into three distinct components, each representing a phase of the pipeline. We begin with attributes of the data accounted for during preprocessing, then move to considerations during model building, and end with challenges to the interpretation of model output. For each component, we present a discussion around data as it relates to the healthcare domain and offer insight into the challenges each may impose on the efficiency of machine learning techniques.Comment: Healthcare Informatics, Machine Learning, Knowledge Discovery: 20 Pages, 1 Figur

Assessing Evapotranspiration Estimates from the Global Soil Wetness Project Phase 2 (GSWP-2) Simulations

Abstract and PDF report are also available on the MIT Joint Program on the Science and Policy of Global Change website (http://globalchange.mit.edu/).We assess the simulations of global-scale evapotranspiration from the Global Soil Wetness Project Phase 2 (GSWP-2) within a global water-budget framework. The scatter in the GSWP-2 global evapotranspiration estimates from various land surface models can constrain the global, annual water budget fluxes to within ±2.5%, and by using estimates of global precipitation, the residual ocean evaporation estimate falls within the range of other independently derived bulk estimates. However, the GSWP-2 scatter cannot entirely explain the imbalance of the annual fluxes from a modern-era, observationally-based global water budget assessment, and inconsistencies in the magnitude and timing of seasonal variations between the global water budget terms are found. Inter-model inconsistencies in evapotranspiration are largest for high latitude inter-annual variability as well as for inter-seasonal variations in the tropics, and analyses with field-scale data also highlights model disparity at estimating evapotranspiration in high latitude regions. Analyses of the sensitivity simulations that replace uncertain forcings (i.e. radiation, precipitation, and meteorological variables) indicate that global (land) evapotranspiration is slightly more sensitive to precipitation than net radiation perturbations, and the majority of the GSWP-2 models, at a global scale, fall in a marginally moisture-limited evaporative condition. Finally, the range of global evapotranspiration estimates among the models is larger than any bias caused by uncertainties in the GSWP-2 atmospheric forcing, indicating that model structure plays a more important role toward improving global land evaporation estimates (as opposed to improved atmospheric forcing).NASA Energy and Water-cycle Study (NEWS, grant #NNX06AC30A), under the NEWS Science and Integration Team activities

Evaluation of the NAS-ILAB Matrix for Monitoring International Labor Standards: Project Report

The Bureau of International Labor Affairs (ILAB) engaged the National Research Council of the National Academy of Sciences (NAS) to recommend a method to monitor and evaluate labor conditions in a given country. The method focuses on 5 labor standards: freedom of association and collective bargaining, forced or compulsory labor, child labor, discrimination, and acceptable conditions of work

In pursuit of comparable concepts and data about collective action:

Research on collective action confronts two major obstacles. First, inconsistency in the conceptualization and operationalization of collective action, the key factors expected to affect collective action, and the outcomes of collective action hampers the accumulation of knowledge. Inconsistent terminology obscures consistent patterns. Second, the scarcity of comparable data thwarts evaluation of the relative importance of the many variables identified in the literature as likely to influence collective action. The International Forestry Resources and Institutions (IFRI) research program addresses both of these problems. Since its founding in 1993, the IFRI network of collaborating research centers has used a common set of methods and concepts to study forests, the people who use forest resources, and their institutions for resource management. The basic social unit of analysis in IFRI is the user group, defined as a set of individuals with the same rights and responsibilities to forest resources. This definition does not require formal organization or collective action, since these features are potential dependent variables. This strategy for data collection allows analysis of relationships between diverse forms of social heterogeneity and collective action within groups with comparable rights to resources. IFRI's relational database also captures the connections among forest systems, sets of resource users, particular forest products, formal and informal rules for resource use, and formal local and supra-local organizations. By the middle of 2001, the IFRI database included data on 141 sites with 231 forests, 233 user groups, 94 forest organizations, and 486 products in 12 countries. Drawing upon these data, IFRI researchers are contributing substantially to our understanding of collective action for institutional development, the mediating role institutions play relative to demographic and market pressures in patterns of resource use, and relationships between particular institutions and forest conditions. The paper describes IFRI's strategy for collecting comparable data based on consistent conceptualization and operationalization, summarizes the contributions of IFRI research to the study of collective action for natural resource management, and identifies continuing challenges.resource management, Forests and forestry Social aspects., Collective action, Forest products., Capacity,

Turning the Table on Assessment: The Grantee Perception Report

This book chapter describes the origins of the GPR, illustrates lessons learned, and provides examples of changes made by foundations that have used this tool. It also reports on some of the broadly applicable insights gained from CEP's large-scale surveys of grantees. (This material is excerpted from the Grantmakers for Effective Organizations (GEO) book, A Funder's Guide to Organizational Assessment.