scholarly journals What Can 100,000 Books Tell Us about the International Public Library e-lending Landscape?

2019 ◽  
Author(s):  
Rebecca Giblin ◽  
Jenny Kennedy ◽  
Charlotte Pelletier ◽  
Julian Thomas ◽  
Kimberlee Weatherall ◽  
...  
Keyword(s):  
United States ◽  
Machine Learning ◽  
English Language ◽  
Public Library ◽  
The United States ◽  
Original Publication ◽  
The Public ◽  
New Algorithms ◽  
Unique Dataset

Introduction: We investigated the relative availability of e-books to libraries for e-lending in five English-language countries, and analysed their licence terms and prices. Method: We created a unique dataset recording author, publisher, price and terms for 100,000 titles and 388,045 e-lending licences across Australia, New Zealand, Canada, the United States and United Kingdom via aggregator Overdrive. We developed new algorithms to estimate the original publication year for each title, and to match titles across jurisdictions.Analysis: We examined the relationships between title price, age, terms, jurisdiction, publisher and publisher type using various statistical analyses and machine learning. Results: Price and licence differences across countries are largely attributable to ‘Big 5’ publishers. Prices are largely independent of title age (unless the title is in the public domain) or the rights libraries obtain in exchange. Licence terms are not affected by age either, meaning that the most restrictive terms are often applied to older, less demanded books. Conclusions: By setting terms independent of titles’ value to libraries, publishers may discourage libraries from adding older and less-demanded books to their collections. We will test this hypothesis in a follow-up library survey.

scholarly journals Characteristics of Twitter Use by State Medicaid Programs in the United States: Machine Learning Approach

10.2196/18401 ◽  
2020 ◽  
Vol 22 (8) ◽  
pp. e18401
Author(s):  
Jane M Zhu ◽  
Abeed Sarker ◽  
Sarah Gollust ◽  
Raina Merchant ◽  
David Grande
Keyword(s):  
Public Health ◽  
United States ◽  
Machine Learning ◽  
Public Health Education ◽  
The United States ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Care Organization ◽  
The Public ◽  
The Mean

Background Twitter is a potentially valuable tool for public health officials and state Medicaid programs in the United States, which provide public health insurance to 72 million Americans. Objective We aim to characterize how Medicaid agencies and managed care organization (MCO) health plans are using Twitter to communicate with the public. Methods Using Twitter’s public application programming interface, we collected 158,714 public posts (“tweets”) from active Twitter profiles of state Medicaid agencies and MCOs, spanning March 2014 through June 2019. Manual content analyses identified 5 broad categories of content, and these coded tweets were used to train supervised machine learning algorithms to classify all collected posts. Results We identified 15 state Medicaid agencies and 81 Medicaid MCOs on Twitter. The mean number of followers was 1784, the mean number of those followed was 542, and the mean number of posts was 2476. Approximately 39% of tweets came from just 10 accounts. Of all posts, 39.8% (63,168/158,714) were classified as general public health education and outreach; 23.5% (n=37,298) were about specific Medicaid policies, programs, services, or events; 18.4% (n=29,203) were organizational promotion of staff and activities; and 11.6% (n=18,411) contained general news and news links. Only 4.5% (n=7142) of posts were responses to specific questions, concerns, or complaints from the public. Conclusions Twitter has the potential to enhance community building, beneficiary engagement, and public health outreach, but appears to be underutilized by the Medicaid program.

A Systematic Review of the Literature on Evaluative Studies of Tonsillectomy and Adenoidectomy

PEDIATRICS ◽  
1976 ◽  
Vol 57 (3) ◽  
pp. 401-407
Author(s):  
W. Shaikh ◽  
E. Vayda ◽  
W. Feldman
Keyword(s):  
United States ◽  
Study Design ◽  
English Language ◽  
Randomized Study ◽  
The United States ◽  
English Language Literature ◽  
Considerable Controversy ◽  
The Cost ◽  
Language Literature

Although tonsillectomy and adenoidectomy (T and A) is frequently performed (indeed, it is the commonest surgical procedure done in North America1,2) considerable controversy persists regarding its effectiveness. In 1971, 161,301 T and A's were performed in Canada at an estimated cost of close to 25.6 million dollars.3 In the United States in 1968 more than 1 million T and A's were performed.4 Assuming the cost per T and A to be similar to the costs in Canada, around $150 million were spent on this procedure in the United States in that year. The purpose of this study is to review the English language literature pertaining to evaluation of T and A with particular emphasis on an assessment of the scientific merit of studies which have attempted to determine the efficacy of this procedure. See Table in the PDF File METHOD Studies evaluating the results of T and A in the English language literature for the past 50 years5-33 were evaluated according to the following parameters: study design, sampling, completeness of description of illness and therapy, and precision of follow-up. Those studies which were most objective were awarded the highest points in each parameter. Conversely, studies which were purely descriptive or poorly documented received the fewest points. The maximum number of points which a randomized, prospective, well-documented study could obtain was 34 points. Table I shows the distribution of maximum scores in the various categories. For the parameter of study design, points were awarded as shown in Table II. The highest score was given to a randomized study and the lowest to a descriptive one.

scholarly journals The Public Library System of the United States

Nature ◽  
1928 ◽  
Vol 122 (3062) ◽  
pp. 39-39
Author(s):  
S. C. B.
Keyword(s):  
United States ◽  
Public Library ◽  
The United States ◽  
The Public ◽  
Library System

scholarly journals Computerized monitoring of COVID-19 trials, studies and registries in ClinicalTrials.gov registry

PeerJ ◽  
2020 ◽  
Vol 8 ◽  
pp. e10261
Author(s):  
Craig S. Mayer ◽  
Vojtech Huser
Keyword(s):  
United States ◽  
Observational Studies ◽  
The United States ◽  
The Public ◽  
Clinical Trial Registries ◽  
Vaccine Trials ◽  
Mapping Techniques ◽  
Computerized Monitoring ◽  
Study Type

Clinical trial registries can provide important information about relevant studies for a given condition to other researchers and the public. We developed a computerized informatics based approach to provide an overview and analysis of COVID-19 studies registered on ClinicalTrials.gov registry. Using the perspective of analyzing active or completed COVID-19 studies, we identified 401 interventional clinical trials, 287 observational studies and 64 registries. We analyzed features of each study type separately such as location, design, interventions and update history. Our results show that the United States had the most COVID-19 interventional trials, France had the most COVID-19 observational studies and France and the United States tied for the most COVID-19 registries on ClinicalTrials.gov. The majority of studies in all three study types had a single study site. For update history “Study Status” is the most updated information and we found that studies located in Canada (2.70 updates per study) and the United States (1.76 updates per study) update their studies more often than studies in any other country. Using normalization and mapping techniques, we identified Hydroxychloroquine (92 studies) as the most common drug intervention, while convalescent plasma (20 studies) is the most common biological intervention. The primary purpose of most interventional trials is for treatment with 298 studies (74.3%). For COVID-19 registries we found the most common proposed follow-up time is 1 year (15 studies). Of specific importance and interest is COVID-19 vaccine trials, of which 12 were identified. Our informatics based approach allows for constant monitoring and updating as well as multiple applications to other conditions and interests.

scholarly journals The #MeToo Movement in the United States: Text Analysis of Early Twitter Conversations

10.2196/13837 ◽  
2019 ◽  
Vol 21 (9) ◽  
pp. e13837 ◽  
Author(s):  
Sepideh Modrek ◽  
Bozhidar Chakalov
Keyword(s):  
United States ◽  
Machine Learning ◽  
Sexual Assault ◽  
Sexual Harassment ◽  
Early Life ◽  
English Language ◽  
Life Experiences ◽  
The United States ◽  
Learning Methods ◽  
Machine Learning Methods

Background The #MeToo movement sparked an international debate on the sexual harassment, abuse, and assault and has taken many directions since its inception in October of 2017. Much of the early conversation took place on public social media sites such as Twitter, where the hashtag movement began. Objective The aim of this study is to document, characterize, and quantify early public discourse and conversation of the #MeToo movement from Twitter data in the United States. We focus on posts with public first-person revelations of sexual assault/abuse and early life experiences of such events. Methods We purchased full tweets and associated metadata from the Twitter Premium application programming interface between October 14 and 21, 2017 (ie, the first week of the movement). We examined the content of novel English language tweets with the phrase “MeToo” from within the United States (N=11,935). We used machine learning methods, least absolute shrinkage and selection operator regression, and support vector machine models to summarize and classify the content of individual tweets with revelations of sexual assault and abuse and early life experiences of sexual assault and abuse. Results We found that the most predictive words created a vivid archetype of the revelations of sexual assault and abuse. We then estimated that in the first week of the movement, 11% of novel English language tweets with the words “MeToo” revealed details about the poster’s experience of sexual assault or abuse and 5.8% revealed early life experiences of such events. We examined the demographic composition of posters of sexual assault and abuse and found that white women aged 25-50 years were overrepresented in terms of their representation on Twitter. Furthermore, we found that the mass sharing of personal experiences of sexual assault and abuse had a large reach, where 6 to 34 million Twitter users may have seen such first-person revelations from someone they followed in the first week of the movement. Conclusions These data illustrate that revelations shared went beyond acknowledgement of having experienced sexual harassment and often included vivid and traumatic descriptions of early life experiences of assault and abuse. These findings and methods underscore the value of content analysis, supported by novel machine learning methods, to improve our understanding of how widespread the revelations were, which likely amplified the spread and saliency of the #MeToo movement.

The Public Library Movement in the United States

The Library ◽  
1896 ◽  
Vol s1-8 (1) ◽  
pp. 110-124 ◽  
Author(s):  
JOSEPH LEROY HARRISON
Keyword(s):  
United States ◽  
Public Library ◽  
The United States ◽  
The Public
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