Dataset Information

A characterization of professional media and its links to research.

ABSTRACT: The publishing industry is a vast system whose elements form a metaphorical ecosystem with knowledge flowing through connections between heterogeneous elements. In this paper we seek a more robust understanding of different types of literature, and whether and how they support one another in the diffusion of knowledge. We analyze a corpus comprising professional electronic media in US dentistry and its relation to the peer reviewed journal literature. Our corpus includes full text from magazines, news sites and blogs that provide information to clinicians. We find links to research are made through several mechanisms: articles describing new clinical guidelines, referencing, summaries of recently published journal articles and crossover authoring. There is little to no apparent time lag in the diffusion of information from research literature to professional media.

SUBMITTER: Hicks D

PROVIDER: S-EPMC7153776 | biostudies-literature | 2019 May

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

A characterization of professional media and its links to research.

Hicks Diana D Melkers Julia J Isett Kimberley R KR

Scientometrics 20190304 2

The publishing industry is a vast system whose elements form a metaphorical ecosystem with knowledge flowing through connections between heterogeneous elements. In this paper we seek a more robust understanding of different types of literature, and whether and how they support one another in the diffusion of knowledge. We analyze a corpus comprising professional electronic media in US dentistry and its relation to the peer reviewed journal literature. Our corpus includes full text from magazines ...[more]

PMID: 32284667

Similar Datasets

Project description:BackgroundA recent trend among health care professionals is the use of social media for professional purposes. These rapidly expanding media allow for timely and efficient access to health information, but they also carry the potential for increased liability. There is a paucity of research detailing how health care professionals, specifically pharmacists, use social media.ObjectiveTo characterize the use of social media by pharmacists in the Canadian province of Alberta and to identify independent determinants of and perceived barriers to using social media for professional purposes.MethodsData for this mixed-methods study were collected by an online survey in March and April 2014. Alberta pharmacists were invited to participate via e-mail distributed by 2 professional organizations.ResultsThe survey had 273 respondents. Of these, 226 (82.8%) stated that they had a social media account for either personal or professional purposes, and 138 (61.1%) of these reported using social media for professional purposes, although most respondents used social media predominantly for personal reasons. The most commonly reported social media applications were Facebook and Twitter, accessed primarily via smartphones. Of the 273 respondents, 206 (75.5%) had a Facebook account, and 101 (49.0%) of these used Facebook to some extent for professional purposes. Twitter users (104 [38.1%] of respondents) had a higher rate of professional utilization (57/104 [54.8%]). The most commonly identified barrier to using social media for professional purposes was concern over liability. Positive predictors of use of social media for professional purposes included younger age and fewer years of professional experience. Participants perceived the most beneficial aspect of social media (in professional terms) as connecting with pharmacist colleagues.ConclusionMore than 80% of pharmacists in Alberta reported that they had a social media account, and over half of them reported using their accounts for professional purposes. Although Facebook had a higher reported rate of utilization in general, a greater proportion of respondents reported using Twitter for professional purposes. Individuals and organizations seeking to expand their professional social media presence should focus on Twitter.

Project description:BackgroundPatient-based analysis of social media is a growing research field with the aim of delivering precision medicine but it requires accurate classification of posts relating to patients' experiences. We motivate the need for this type of classification as a pre-processing step for further analysis of social media data in the context of related work in this area. In this paper we present experiments for a three-way document classification by patient voice, professional voice or other. We present results for a convolutional neural network classifier trained on English data from two different data sources (Reddit and Twitter) and two domains (cardiovascular and skin diseases).ResultsWe found that document classification by patient voice, professional voice or other can be done consistently manually (0.92 accuracy). Annotators agreed roughly equally for each domain (cardiovascular and skin) but they agreed more when annotating Reddit posts compared to Twitter posts. Best classification performance was obtained when training two separate classifiers for each data source, one for Reddit and one for Twitter posts, when evaluating on in-source test data for both test sets combined with an overall accuracy of 0.95 (and macro-average F1 of 0.92) and an F1-score of 0.95 for patient voice only.ConclusionThe main conclusion resulting from this work is that combining social media data from platforms with different characteristics for training a patient and professional voice classifier does not result in best possible performance. We showed that it is best to train separate models per data source (Reddit and Twitter) instead of a model using the combined training data from both sources. We also found that it is preferable to train separate models per domain (cardiovascular and skin) while showing that the difference to the combined model is only minor (0.01 accuracy). Our highest overall F1-score (0.95) obtained for classifying posts as patient voice is a very good starting point for further analysis of social media data reflecting the experience of patients.

Dataset Information

A characterization of professional media and its links to research.

Publications

A characterization of professional media and its links to research.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets