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      Comprehensive Researcher Achievement Model (CRAM): a framework for measuring researcher achievement, impact and influence derived from a systematic literature review of metrics and models

      systematic-review

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          Abstract

          Objectives

          Effective researcher assessment is key to decisions about funding allocations, promotion and tenure. We aimed to identify what is known about methods for assessing researcher achievements, leading to a new composite assessment model.

          Design

          We systematically reviewed the literature via the Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols framework.

          Data sources

          All Web of Science databases (including Core Collection, MEDLINE and BIOSIS Citation Index) to the end of 2017.

          Eligibility criteria

          (1) English language, (2) published in the last 10 years (2007–2017), (3) full text was available and (4) the article discussed an approach to the assessment of an individual researcher’s achievements.

          Data extraction and synthesis

          Articles were allocated among four pairs of reviewers for screening, with each pair randomly assigned 5% of their allocation to review concurrently against inclusion criteria. Inter-rater reliability was assessed using Cohen’s Kappa (ĸ). The ĸ statistic showed agreement ranging from moderate to almost perfect (0.4848–0.9039). Following screening, selected articles underwent full-text review and bias was assessed.

          Results

          Four hundred and seventy-eight articles were included in the final review. Established approaches developed prior to our inclusion period (eg, citations and outputs, h-index and journal impact factor) remained dominant in the literature and in practice. New bibliometric methods and models emerged in the last 10 years including: measures based on PageRank algorithms or ‘altmetric’ data, methods to apply peer judgement and techniques to assign values to publication quantity and quality. Each assessment method tended to prioritise certain aspects of achievement over others.

          Conclusions

          All metrics and models focus on an element or elements at the expense of others. A new composite design, the Comprehensive Researcher Achievement Model (CRAM), is presented, which supersedes past anachronistic models. The CRAM is modifiable to a range of applications.

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          Most cited references126

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          Can Tweets Predict Citations? Metrics of Social Impact Based on Twitter and Correlation with Traditional Metrics of Scientific Impact

          Background Citations in peer-reviewed articles and the impact factor are generally accepted measures of scientific impact. Web 2.0 tools such as Twitter, blogs or social bookmarking tools provide the possibility to construct innovative article-level or journal-level metrics to gauge impact and influence. However, the relationship of the these new metrics to traditional metrics such as citations is not known. Objective (1) To explore the feasibility of measuring social impact of and public attention to scholarly articles by analyzing buzz in social media, (2) to explore the dynamics, content, and timing of tweets relative to the publication of a scholarly article, and (3) to explore whether these metrics are sensitive and specific enough to predict highly cited articles. Methods Between July 2008 and November 2011, all tweets containing links to articles in the Journal of Medical Internet Research (JMIR) were mined. For a subset of 1573 tweets about 55 articles published between issues 3/2009 and 2/2010, different metrics of social media impact were calculated and compared against subsequent citation data from Scopus and Google Scholar 17 to 29 months later. A heuristic to predict the top-cited articles in each issue through tweet metrics was validated. Results A total of 4208 tweets cited 286 distinct JMIR articles. The distribution of tweets over the first 30 days after article publication followed a power law (Zipf, Bradford, or Pareto distribution), with most tweets sent on the day when an article was published (1458/3318, 43.94% of all tweets in a 60-day period) or on the following day (528/3318, 15.9%), followed by a rapid decay. The Pearson correlations between tweetations and citations were moderate and statistically significant, with correlation coefficients ranging from .42 to .72 for the log-transformed Google Scholar citations, but were less clear for Scopus citations and rank correlations. A linear multivariate model with time and tweets as significant predictors (P < .001) could explain 27% of the variation of citations. Highly tweeted articles were 11 times more likely to be highly cited than less-tweeted articles (9/12 or 75% of highly tweeted article were highly cited, while only 3/43 or 7% of less-tweeted articles were highly cited; rate ratio 0.75/0.07 = 10.75, 95% confidence interval, 3.4–33.6). Top-cited articles can be predicted from top-tweeted articles with 93% specificity and 75% sensitivity. Conclusions Tweets can predict highly cited articles within the first 3 days of article publication. Social media activity either increases citations or reflects the underlying qualities of the article that also predict citations, but the true use of these metrics is to measure the distinct concept of social impact. Social impact measures based on tweets are proposed to complement traditional citation metrics. The proposed twimpact factor may be a useful and timely metric to measure uptake of research findings and to filter research findings resonating with the public in real time.
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            Bibliometrics: tracking research impact by selecting the appropriate metrics

            Traditionally, the success of a researcher is assessed by the number of publications he or she publishes in peer-reviewed, indexed, high impact journals. This essential yardstick, often referred to as the impact of a specific researcher, is assessed through the use of various metrics. While researchers may be acquainted with such matrices, many do not know how to use them to enhance their careers. In addition to these metrics, a number of other factors should be taken into consideration to objectively evaluate a scientist's profile as a researcher and academician. Moreover, each metric has its own limitations that need to be considered when selecting an appropriate metric for evaluation. This paper provides a broad overview of the wide array of metrics currently in use in academia and research. Popular metrics are discussed and defined, including traditional metrics and article-level metrics, some of which are applied to researchers for a greater understanding of a particular concept, including varicocele that is the thematic area of this Special Issue of Asian Journal of Andrology. We recommend the combined use of quantitative and qualitative evaluation using judiciously selected metrics for a more objective assessment of scholarly output and research impact.
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              Quantifying long-term scientific impact.

              The lack of predictability of citation-based measures frequently used to gauge impact, from impact factors to short-term citations, raises a fundamental question: Is there long-term predictability in citation patterns? Here, we derive a mechanistic model for the citation dynamics of individual papers, allowing us to collapse the citation histories of papers from different journals and disciplines into a single curve, indicating that all papers tend to follow the same universal temporal pattern. The observed patterns not only help us uncover basic mechanisms that govern scientific impact but also offer reliable measures of influence that may have potential policy implications.
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                Author and article information

                Journal
                BMJ Open
                BMJ Open
                bmjopen
                bmjopen
                BMJ Open
                BMJ Publishing Group (BMA House, Tavistock Square, London, WC1H 9JR )
                2044-6055
                2019
                30 March 2019
                : 9
                : 3
                : e025320
                Affiliations
                [1 ] departmentAustralian Institute of Health Innovation , Macquarie University , North Ryde, New South Wales, Australia
                [2 ] departmentDivision of Health Sciences , University of South Australia , Adelaide, South Australia, Australia
                Author notes
                [Correspondence to ] Professor Jeffrey Braithwaite; jeffrey.braithwaite@ 123456mq.edu.au
                Author information
                http://orcid.org/0000-0003-0296-4957
                http://orcid.org/0000-0002-9923-3116
                http://orcid.org/0000-0002-6107-7445
                http://orcid.org/0000-0002-9628-7987
                http://orcid.org/0000-0002-4317-7960
                http://orcid.org/0000-0002-2012-1980
                http://orcid.org/0000-0001-7284-5625
                Article
                bmjopen-2018-025320
                10.1136/bmjopen-2018-025320
                6475357
                30928941
                952b994d-750e-4f32-a560-f2f7f024fcdd
                © Author(s) (or their employer(s)) 2019. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.

                This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/.

                History
                : 10 July 2018
                : 04 February 2019
                : 06 February 2019
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100000925, National Health and Medical Research Council;
                Categories
                Research Methods
                Research
                1506
                1730
                Custom metadata
                unlocked

                Medicine
                researcher assessment,research metrics,h-index,journal impact factor,citations,outputs,comprehensive researcher achievement model (cram)

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