Dataset Information

Open Knowledge about Slaughter on the Internet-A Case Study on Controversies.

ABSTRACT: Knowledge about slaughter of animals for human food is often perceived as controversial and therefore not made widely available. An open educational resource on the Internet about the slaughter of animals has created tension at launch but also resolved tension. Aiming to explore how this resource at the boundary between academia and society is perceived, a study was carried out with participants from slaughterhouses, universities, authorities and NGOs. Focus group sessions were video recorded and transcripts were coded using an interpretive thematic analysis. The results show that an open educational resource in addition to contributing to learning and awareness raising can also induce dialogue (and thus resolve tension) about animal welfare and contribute to animal welfare resilience. Our results also indicate that participants had diverse opinions about the influence of multimedia on attitudes towards animal slaughter. The use of additional instruments such as comment fields may lead to more knowledgeable citizens and socially robust knowledge, but has to be carefully weighed against the risk of false or fake data.

SUBMITTER: Algers A

PROVIDER: S-EPMC5742795 | biostudies-literature | 2017 Dec

REPOSITORIES: biostudies-literature

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Open Knowledge about Slaughter on the Internet-A Case Study on Controversies.

Algers Anne A Berg Charlotte C

Animals : an open access journal from MDPI 20171218 12

Knowledge about slaughter of animals for human food is often perceived as controversial and therefore not made widely available. An open educational resource on the Internet about the slaughter of animals has created tension at launch but also resolved tension. Aiming to explore how this resource at the boundary between academia and society is perceived, a study was carried out with participants from slaughterhouses, universities, authorities and NGOs. Focus group sessions were video recorded an ...[more]

PMID: 29258266

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Project description:To safely protect workplaces and the workforce during and after the COVID-19 pandemic, a scalable integrated sensing solution is required in order to offer real-time situational awareness and early warnings for decision-makers. However, an information-based solution for industry reopening is ineffective when the necessary operational information is locked up in disparate real-time data silos. There is a lot of ongoing effort to combat the COVID-19 pandemic using different combinations of low-cost, location-based contact tracing, and sensing technologies. These ad hoc Internet of Things (IoT) solutions for COVID-19 were developed using different data models and protocols without an interoperable way to interconnect these heterogeneous systems and exchange data on people and place interactions. This research aims to design and develop an interoperable Internet of COVID-19 Things (IoCT) architecture that is able to exchange, aggregate, and reuse disparate IoT sensor data sources in order for informed decisions to be made after understanding the real-time risks in workplaces based on person-to-place interactions. The IoCT architecture is based on the Sensor Web paradigm that connects various Things, Sensors, and Datastreams with an indoor geospatial data model. This paper presents a study of what, to the best of our knowledge, is the first real-world integrated implementation of the Open Geospatial Consortium (OGC) Sensor Web Enablement (SWE) and IndoorGML standards to calculate the risk of COVID-19 online using a workplace reopening case study. The proposed IoCT offers a new open standard-based information model, architecture, methodologies, and software tools that enable the interoperability of disparate COVID-19 monitoring systems with finer spatial-temporal granularity. A workplace cleaning use case was developed in order to demonstrate the capabilities of this proposed IoCT architecture. The implemented IoCT architecture included proximity-based contact tracing, people density sensors, a COVID-19 risky behavior monitoring system, and the contextual building geospatial data.

Project description:BackgroundAn accurate understanding of dietary supplements (DS) is a prerequisite for informed decisions regarding their intake. However, there is a need for studies on this understanding among the public based on validated research tools.ObjectiveThis study aims to assess the knowledge about DS among Polish internet users with no medical education and to identify its determinants and design an appropriate predictive model.MethodsThe study protocol was prospectively registered with a statistical analysis plan. Polish users of a web-based health service and a social networking service were administered a survey consisting of the recently developed questionnaire on knowledge about DS, the questionnaire on trust in advertising DS, the beliefs about medicines questionnaire, and several other health-related single-item measures and sociodemographic questions. The results were subjected to general linear modeling.ResultsA total of 6273 participants were included. Of the 17 yes or no questions in the questionnaire of knowledge about DS, the mean number of correct responses was 9.0 (95% CI 8.9-9.1). Health service users performed worse than social networking users by 2.3 points (95% CI 2.1-2.5) in an analysis adjusted for potential confounders. Internet users had fewer true beliefs about DS if they presented higher trust in their advertising (adjusted β=-.37; 95% CI -.39 to -.34), used DS (adjusted β=-.14; 95% CI -.17 to -.12), experienced their positive effect (adjusted β=-.16; 95% CI -.18 to -.13), were older or younger than 35 years (adjusted β=-.14; 95% CI -.17 to -.12), expressed interest in the topic of DS (adjusted β=-.10; 95% CI -.13 to -.08), reported getting information about the products from friends (adjusted β=-.13; 95% CI -.15 to -.11), and believed that medicines are harmful (adjusted β=-.12; 95% CI -.15 to -.10). The proposed 5-predictor model could explain 31.2% of the variance in knowledge about DS. The model appeared resistant to overfitting and was able to forecast most of the observed associations.ConclusionsPolish internet users with no medical education exhibit some false beliefs regarding DS. Trusting the advertising of DS appears to conflict with knowledge about them. There is an urgent need for effective web-based educational campaigns on DS and the promotion of advertising literacy. After the proposed predictive model is externally validated, it may help identify the least informed target audience.

Dataset Information

Open Knowledge about Slaughter on the Internet-A Case Study on Controversies.

Publications

Open Knowledge about Slaughter on the Internet-A Case Study on Controversies.

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