University of Stirling

About: University of Stirling is a education organization based out in Stirling, Stirling, United Kingdom. It is known for research contribution in the topics: Population & Context (language use). The organization has 7722 authors who have published 20549 publications receiving 732940 citations. The organization is also known as: Stirling University.

The dos and don’ts of influencing policy: a systematic review of advice to academics

Abstract: Many academics have strong incentives to influence policymaking, but may not know where to start. We searched systematically for, and synthesised, the ‘how to’ advice in the academic peer-reviewed and grey literatures. We condense this advice into eight main recommendations: (1) Do high quality research; (2) make your research relevant and readable; (3) understand policy processes; (4) be accessible to policymakers: engage routinely, flexible, and humbly; (5) decide if you want to be an issue advocate or honest broker; (6) build relationships (and ground rules) with policymakers; (7) be ‘entrepreneurial’ or find someone who is; and (8) reflect continuously: should you engage, do you want to, and is it working? This advice seems like common sense. However, it masks major inconsistencies, regarding different beliefs about the nature of the problem to be solved when using this advice. Furthermore, if not accompanied by critical analysis and insights from the peer-reviewed literature, it could provide misleading guidance for people new to this field.

Media Convergence: Networked Digital Media in Everyday Life

Abstract: This book focuses on how everyday media such as Facebook, iTunes andGoogle can be understood in new ways for the21st centurythrough ideas of convergence.Key chapters explore the development of the internet, the rise of social media and the new opportunities for audiences to create, collaborate upon and share their own media.

Genome wide association and genomic prediction for growth traits in juvenile farmed Atlantic salmon using a high density SNP array

Abstract: The genetic architecture of complex traits in farmed animal populations is of interest from a scientific and practical perspective. The use of genetic markers to predict the genetic merit (breeding values) of individuals is commonplace in modern farm animal breeding schemes. Recently, high density SNP arrays have become available for Atlantic salmon, which facilitates genomic prediction and association studies using genome-wide markers and economically important traits. The aims of this study were (i) to use a high density SNP array to investigate the genetic architecture of weight and length in juvenile Atlantic salmon; (ii) to assess the utility of genomic prediction for these traits, including testing different marker densities; (iii) to identify potential candidate genes underpinning variation in early growth. A pedigreed population of farmed Atlantic salmon (n = 622) were measured for weight and length traits at one year of age, and genotyped for 111,908 segregating SNP markers using a high density SNP array. The heritability of both traits was estimated using pedigree and genomic relationship matrices, and was comparable at around 0.5 and 0.6 respectively. The results of the GWA analysis pointed to a polygenic genetic architecture, with no SNPs surpassing the genome-wide significance threshold, and one SNP associated with length at the chromosome-wide level. SNPs surpassing an arbitrary threshold of significance (P < 0.005, ~ top 0.5 % of markers) were aligned to an Atlantic salmon reference transcriptome, identifying 109 SNPs in transcribed regions that were annotated by alignment to human, mouse and zebrafish protein databases. Prediction of breeding values was more accurate when applying genomic (GBLUP) than pedigree (PBLUP) relationship matrices (accuracy ~ 0.7 and 0.58 respectively) and 5,000 SNPs were sufficient for obtaining this accuracy increase over PBLUP in this specific population. The high density SNP array can effectively capture the additive genetic variation in complex traits. However, the traits of weight and length both appear to be very polygenic with only one SNP surpassing the chromosome-wide threshold. Genomic prediction using the array is effective, leading to an improvement in accuracy compared to pedigree methods, and this improvement can be achieved with only a small subset of the markers in this population. The results have practical relevance for genomic selection in salmon and may also provide insight into variation in the identified genes underpinning body growth and development in salmonid species.

Improving reporting of meta‐ethnography: The eMERGe reporting guidance

Abstract: The aim of this study was to provide guidance to improve the completeness and clarity of meta‐ethnography reporting. Evidence‐based policy and practice require robust evidence syntheses which can further understanding of people's experiences and associated social processes. Meta‐ethnography is a rigorous seven‐phase qualitative evidence synthesis methodology, developed by Noblit and Hare. Meta‐ethnography is used widely in health research, but reporting is often poor quality and this discourages trust in and use of its findings. Meta‐ethnography reporting guidance is needed to improve reporting quality. The eMERGe study used a rigorous mixed‐methods design and evidence‐based methods to develop the novel reporting guidance and explanatory notes. The study, conducted from 2015 to 2017, comprised of: (1) a methodological systematic review of guidance for meta‐ethnography conduct and reporting; (2) a review and audit of published meta‐ethnographies to identify good practice principles; (3) international, multidisciplinary consensus‐building processes to agree guidance content; (4) innovative development of the guidance and explanatory notes. Recommendations and good practice for all seven phases of meta‐ethnography conduct and reporting were newly identified leading to 19 reporting criteria and accompanying detailed guidance.The bespoke eMERGe Reporting Guidance, which incorporates new methodological developments and advances the methodology, can help researchers to report the important aspects of meta‐ethnography. Use of the guidance should raise reporting quality. Better reporting could make assessments of confidence in the findings more robust and increase use of meta‐ethnography outputs to improve practice, policyand service user outcomes in health and other fields. This is the first tailored reporting guideline for meta‐ethnography.