Using Intervention Mapping to Develop an Efficacious Multicomponent Systems-Based Intervention to Increase Human Papillomavirus (HPV) Vaccination in a Large Urban Pediatric Clinic Network

Background: The CDC recommends HPV vaccine for all adolescents to prevent cervical, anal, oropharyngeal, vaginal, vulvar, and penile cancers, and genital warts. HPV vaccine rates currently fall short of national vaccination goals. Despite evidence-based strategies with demonstrated efficacy to increase HPV vaccination rates, adoption and implementation of these strategies within clinics is lacking. The Adolescent Vaccination Program (AVP) is a multicomponent systems-based intervention designed to implement five evidence-based strategies within primary care pediatric practices. The AVP has demonstrated efficacy in increasing HPV vaccine initiation and completion among adolescents 10-17 years of age. The purpose of this paper is to describe the application of Intervention Mapping (IM) toward the development, implementation, and formative evaluation of the clinic-based AVP prototype. Methods: Intervention Mapping (IM) guided the development of the Adolescent Vaccination Program (AVP). Deliverables comprised: a logic model of the problem (IM Step 1); matrices of behavior change objectives (IM Step 2); a program planning document comprising scope, sequence, theory-based methods, and practical strategies (IM Step 3); functional AVP component prototypes (IM Step 4); and plans for implementation (IM Step 5) and evaluation (IM Step 6). Results: The AVP consists of six evidence-based strategies implemented in a successful sequenced roll-out that (1) established immunization champions in each clinic, (2) disseminated provider assessment and feedback reports with data-informed vaccination goals, (3) provided continued medical and nursing education (with ethics credit) on HPV, HPV vaccination, message bundling, and responding to parent hesitancy, (4) electronic health record cues to providers on patient eligibility, and (5) patient reminders for HPV vaccine initiation and completion. Conclusions: IM provided a logical and systematic approach to developing and evaluating a multicomponent systems-based intervention to increase HPV vaccination rates among adolescents in pediatric clinics

Reply With: Proactive Recommendation of Email Attachments

Email responses often contain items-such as a file or a hyperlink to an external document-that are attached to or included inline in the body of the message. Analysis of an enterprise email corpus reveals that 35% of the time when users include these items as part of their response, the attachable item is already present in their inbox or sent folder. A modern email client can proactively retrieve relevant attachable items from the user's past emails based on the context of the current conversation, and recommend them for inclusion, to reduce the time and effort involved in composing the response. In this paper, we propose a weakly supervised learning framework for recommending attachable items to the user. As email search systems are commonly available, we constrain the recommendation task to formulating effective search queries from the context of the conversations. The query is submitted to an existing IR system to retrieve relevant items for attachment. We also present a novel strategy for generating labels from an email corpus---without the need for manual annotations---that can be used to train and evaluate the query formulation model. In addition, we describe a deep convolutional neural network that demonstrates satisfactory performance on this query formulation task when evaluated on the publicly available Avocado dataset and a proprietary dataset of internal emails obtained through an employee participation program.Comment: CIKM2017. Proceedings of the 26th ACM International Conference on Information and Knowledge Management. 201

Measuring and Managing Answer Quality for Online Data-Intensive Services

Online data-intensive services parallelize query execution across distributed software components. Interactive response time is a priority, so online query executions return answers without waiting for slow running components to finish. However, data from these slow components could lead to better answers. We propose Ubora, an approach to measure the effect of slow running components on the quality of answers. Ubora randomly samples online queries and executes them twice. The first execution elides data from slow components and provides fast online answers; the second execution waits for all components to complete. Ubora uses memoization to speed up mature executions by replaying network messages exchanged between components. Our systems-level implementation works for a wide range of platforms, including Hadoop/Yarn, Apache Lucene, the EasyRec Recommendation Engine, and the OpenEphyra question answering system. Ubora computes answer quality much faster than competing approaches that do not use memoization. With Ubora, we show that answer quality can and should be used to guide online admission control. Our adaptive controller processed 37% more queries than a competing controller guided by the rate of timeouts.Comment: Technical Repor

An Architecture for Provenance Systems

This document covers the logical and process architectures of provenance systems. The logical architecture identifies key roles and their interactions, whereas the process architecture discusses distribution and security. A fundamental aspect of our presentation is its technology-independent nature, which makes it reusable: the principles that are exposed in this document may be applied to different technologies