Credibility of Health Information and Digital Media: New Perspectives and Implications for Youth

Part of the Volume on Digital Media, Youth, and Credibility. This chapter considers the role of Web technologies on the availability and consumption of health information. It argues that young people are largely unfamiliar with trusted health sources online, making credibility particularly germane when considering this type of information. The author suggests that networked digital media allow for humans and technologies act as "apomediaries" that can be used to steer consumers to high quality health information, thereby empowering health information seekers of all ages

Citation Advantage of Open Access Articles

Open access (OA) to the research literature has the potential to accelerate recognition and dissemination of research findings, but its actual effects are controversial. This was a longitudinal bibliometric analysis of a cohort of OA and non-OA articles published between June 8, 2004, and December 20, 2004, in the same journal (PNAS: Proceedings of the National Academy of Sciences). Article characteristics were extracted, and citation data were compared between the two groups at three different points in time: at “quasi-baseline” (December 2004, 0–6 mo after publication), in April 2005 (4–10 mo after publication), and in October 2005 (10–16 mo after publication). Potentially confounding variables, including number of authors, authors' lifetime publication count and impact, submission track, country of corresponding author, funding organization, and discipline, were adjusted for in logistic and linear multiple regression models. A total of 1,492 original research articles were analyzed: 212 (14.2% of all articles) were OA articles paid by the author, and 1,280 (85.8%) were non-OA articles. In April 2005 (mean 206 d after publication), 627 (49.0%) of the non-OA articles versus 78 (36.8%) of the OA articles were not cited (relative risk = 1.3 [95% Confidence Interval: 1.1–1.6]; p = 0.001). 6 mo later (mean 288 d after publication), non-OA articles were still more likely to be uncited (non-OA: 172 [13.6%], OA: 11 [5.2%]; relative risk = 2.6 [1.4–4.7]; p < 0.001). The average number of citations of OA articles was higher compared to non-OA articles (April 2005: 1.5 [SD = 2.5] versus 1.2 [SD = 2.0]; Z = 3.123; p = 0.002; October 2005: 6.4 [SD = 10.4] versus 4.5 [SD = 4.9]; Z = 4.058; p < 0.001). In a logistic regression model, controlling for potential confounders, OA articles compared to non-OA articles remained twice as likely to be cited (odds ratio = 2.1 [1.5–2.9]) in the first 4–10 mo after publication (April 2005), with the odds ratio increasing to 2.9 (1.5–5.5) 10–16 mo after publication (October 2005). Articles published as an immediate OA article on the journal site have higher impact than self-archived or otherwise openly accessible OA articles. We found strong evidence that, even in a journal that is widely available in research libraries, OA articles are more immediately recognized and cited by peers than non-OA articles published in the same journal. OA is likely to benefit science by accelerating dissemination and uptake of research findings

Correspondence

[There were several responses to the editorial remarks about the funding of women\u27s studies programs made by F. H. in the first issue of the Newsletter. We print one of these below.] From MARY L. EYSENBACH, Director of Women Studies, University of Washington, Seattle 98105

Electronic health information and long term conditions

This article discusses the increasing availability of health-related information, and the impact that this can have for people with long-term conditions’ expectations of healthcare providers. The article suggests a framework for decision making about the role that healthcare staff should play in the information searching, retrieval, and synthesis activities which people with long-term conditions engage in. The framework is based on a series of decisions related to: perceptions of ownership of long-term conditions; whether intermediatory or apomediatory approaches to information management are deemed to be most appropriate; and, as a result of these considerations, what, if any, place healthcare staff should take in the process of patients searching or and interpreting information about long-term health needs. These decisions will enable healthcare providers to plan services based on clear decision pathways, and to clarify to all concerned what are deemed to be reasonable expectations of health service provision

Maximum Entropy RL (Provably) Solves Some Robust RL Problems

Many potential applications of reinforcement learning (RL) require guarantees that the agent will perform well in the face of disturbances to the dynamics or reward function. In this paper, we prove theoretically that standard maximum entropy RL is robust to some disturbances in the dynamics and the reward function. While this capability of MaxEnt RL has been observed empirically in prior work, to the best of our knowledge our work provides the first rigorous proof and theoretical characterization of the MaxEnt RL robust set. While a number of prior robust RL algorithms have been designed to handle similar disturbances to the reward function or dynamics, these methods typically require adding additional moving parts and hyperparameters on top of a base RL algorithm. In contrast, our theoretical results suggest that MaxEnt RL by itself is robust to certain disturbances, without requiring any additional modifications. While this does not imply that MaxEnt RL is the best available robust RL method, MaxEnt RL does possess a striking simplicity and appealing formal guarantees.Comment: Blog post and videos: https://bair.berkeley.edu/blog/2021/03/10/maxent-robust-rl/. arXiv admin note: text overlap with arXiv:1910.0191

Towards a model of information scatter: Implications for search and design

Recent studies suggest that users often retrieve incomplete healthcare information because of the complex and skewed distribution of facts across relevant webpages. To understand the causes for such skewed distributions, this paper presents the results of two analyses: (1) A distribution analysis discusses how facts related to healthcare topics are scattered across high-quality healthcare pages. (2) A cluster analysis of the same data suggests that the skewed distribution can be explained by the existence of three page profiles that vary in information density, each of which play in important role in providing comprehensive information of a topic. The above analyses provide clues towards a model of information scatter which describes how the design decisions by individual webpage authors could collectively lead to the scatter of information as observed in the data. The analyses also suggest implications for the design of websites, search algorithms, and search interfaces to help users find comprehensive information about a topic.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/57321/1/14504301225_ftp.pd

Contrastive Difference Predictive Coding

Predicting and reasoning about the future lie at the heart of many time-series questions. For example, goal-conditioned reinforcement learning can be viewed as learning representations to predict which states are likely to be visited in the future. While prior methods have used contrastive predictive coding to model time series data, learning representations that encode long-term dependencies usually requires large amounts of data. In this paper, we introduce a temporal difference version of contrastive predictive coding that stitches together pieces of different time series data to decrease the amount of data required to learn predictions of future events. We apply this representation learning method to derive an off-policy algorithm for goal-conditioned RL. Experiments demonstrate that, compared with prior RL methods, ours achieves $2 \times$ median improvement in success rates and can better cope with stochastic environments. In tabular settings, we show that our method is about $20 \times$ more sample efficient than the successor representation and $1500 \times$ more sample efficient than the standard (Monte Carlo) version of contrastive predictive coding.Comment: Website (https://chongyi-zheng.github.io/td_infonce) and code (https://github.com/chongyi-zheng/td_infonce