The relationship between citations, downloads and alternative metrics in rheumatology publications: a bibliometric study

Rheumatology ◽  
2019 ◽  
Vol 59 (2) ◽  
pp. 277-280 ◽  
Author(s):  
Winnie M Y Chen ◽  
Marwan Bukhari ◽  
Francesca Cockshull ◽  
James Galloway
Keyword(s):  
Negative Binomial ◽  
Positive Association ◽  
Negative Binomial Regression ◽  
Significant Positive Association ◽  
H Index ◽  
Binomial Regression ◽  
Number Of Citations ◽  
The Impact ◽  
The Relationship ◽  
Alternative Metrics

Abstract Objective Scientific journals and authors are frequently judged on ‘impact’. Commonly used traditional metrics are the Impact Factor and H-index. However, both take several years to formulate and have many limitations. Recently, Altmetric—a metric that measures impact in a non-traditional way—has gained popularity. This project aims to describe the relationships between subject matter, citations, downloads and Altmetric within rheumatology. Methods Data from publications in Rheumatology were used. Articles published from 2010 to 2015 were reviewed. Data were analysed using Stata 14.2 (StataCorp, College Station, TX, USA). Correlation between citations, downloads and Altmetric were quantified using linear regression, comparing across disease topics. Relationship between downloads and months since publications were described using negative binomial regression, clustering on individual articles. Results A total of 1460 Basic Science and Clinical Science articles were identified, with the number of citations, downloads and Altmetric scores. There were no correlations between disease topic and downloads (R2 = 0.016, P = 0.03), citations (R2 = 0.011, P = 0.29) or Altmetric (R2 = 0.025, P = 0.02). A statistically significant positive association was seen between the number of citations and downloads (R2 = 0.29, P < 0.001). No correlations were seen between Altmetric and downloads (R2 = 0.028, P < 0.001) or citations (R2 = 0.004, P = 0.445). Conclusion Disease area did not correlate with any of the metrics compared. Correlations were apparent with clear links between downloads and citations. Altmetric identified different articles as high impact compared with citation or download metrics. In conclusion: tweeting about your research does not appear to influence citations.

scholarly journals Geographically Weighted Negative Binomial Regression Model Predicts Wildfire Occurrence in the Great Xing’an Mountains Better Than Negative Binomial Model

Forests ◽  
2019 ◽  
Vol 10 (5) ◽  
pp. 377 ◽  
Author(s):  
Zhangwen Su ◽  
Haiqing Hu ◽  
Mulualem Tigabu ◽  
Guangyu Wang ◽  
Aicong Zeng ◽  
...  
Keyword(s):  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Negative Binomial Regression Model ◽  
Stable Model ◽  
Wildfire Occurrence ◽  
Great Xing’An Mountains ◽  
Binomial Regression ◽  
The Impact ◽  
The Relationship ◽  
Better Than

Wildfire is a major disturbance that affects large area globally every year. Thus, a better prediction of the likelihood of wildfire occurrence is essential to develop appropriate fire prevention measures. We applied a global negative Binomial (NB) and a geographically weighted negative Binomial regression (GWNBR) models to determine the relationship between wildfire occurrence and its drivers factors in the boreal forests of the Great Xing’an Mountains, northeast China. Using geo-weighted techniques to consider the geospatial information of meteorological, topographic, vegetation type and human factors, we aimed to verify whether the performance of the NB model can be improved. Our results confirmed that the model fitting and predictions of GWNBR model were better than the global NB model, produced more precise and stable model parameter estimation, yielded a more realistic spatial distribution of model predictions, and provided the detection of the impact hotpots of these predictor variables. We found slope, vegetation cover, average precipitation, average temperature, and average relative humidity as important predictors of wildfire occurrence in the Great Xing’an Mountains. Thus, spatially differing relations improves the explanatory power of the global NB model, which does not explain sufficiently the relationship between wildfire occurrence and its drivers. Thus, the GWNBR model can complement the global NB model in overcoming the issue of nonstationary variables, thereby enabling a better prediction of the occurrence of wildfires in large geographical areas and improving management practices of wildfire.

scholarly journals NATO between Exclusivity and Inclusivity: Measuring NATO’s Partnerships

2021 ◽  
pp. 1-32
Author(s):  
Branislav Mičko
Keyword(s):  
Human Rights ◽  
Fixed Effects ◽  
International Institutions ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Original Dataset ◽  
Strategic Approach ◽  
Binomial Regression ◽  
The Given ◽  
The Relationship

Building on an original dataset, this article focuses on the interactions between NATO and its declared worldwide partners. It argues that the analysis of these interactions can reveal NATO’s strategic approach to partnerships, but it can also provide a tool for its classification as an organisation that is either exclusive – defined by the focus on defence of its members, or inclusive – emphasising the global protection of democracies and human rights. The relationship between types of interactions and NATO categorisation is estimated using an unconditional negative binomial regression with fixed effects as well as a within-between (hybrid) model. Furthermore, they are illustrated on two brief case studies of Sweden and Japan. The results of the study suggest that NATO engages primarily with countries that are powerful relative to their neighbourhood, even though they are not the most powerful among the partners. The given country’s level of democracy, integration into the international institutions, and stability, do not seem to play any overarching role here.

scholarly journals Assessing the Impact of Nightlight Gradients on Street Robbery and Burglary in Cincinnati of Ohio State, USA

2019 ◽  
Vol 11 (17) ◽  
pp. 1958 ◽  
Author(s):  
Hanlin Zhou ◽  
Lin Liu ◽  
Minxuan Lan ◽  
Bo Yang ◽  
Zengli Wang
Keyword(s):  
Negative Binomial ◽  
Infrared Imaging ◽  
Negative Impact ◽  
Positive Impact ◽  
Negative Binomial Regression ◽  
Information Criterion ◽  
Binomial Regression ◽  
Street Robbery ◽  
The Impact

Previous research has recognized the importance of edges to crime. Various scholars have explored how one specific type of edges such as physical edges or social edges affect crime, but rarely investigated the importance of the composite edge effect. To address this gap, this study introduces nightlight data from the Visible Infrared Imaging Radiometer Suite sensor on the Suomi National Polar-orbiting Partnership Satellite (NPP-VIIRS) to measure composite edges. This study defines edges as nightlight gradients—the maximum change of nightlight from a pixel to its neighbors. Using nightlight gradients and other control variables at the tract level, this study applies negative binomial regression models to investigate the effects of edges on the street robbery rate and the burglary rate in Cincinnati. The Akaike Information Criterion (AIC) of models show that nightlight gradients improve the fitness of models of street robbery and burglary. Also, nightlight gradients make a positive impact on the street robbery rate whilst a negative impact on the burglary rate, both of which are statistically significant under the alpha level of 0.05. The different impacts on these two types of crimes may be explained by the nature of crimes and the in-situ characteristics, including nightlight.

scholarly journals The association between self-harm and area-level characteristics in Northern Ireland: an ecological study

2019 ◽  
Vol 29 (5) ◽  
pp. 948-953 ◽  
Author(s):  
Eve Griffin ◽  
Brendan Bonner ◽  
Christina B Dillon ◽  
Denise O’Hagan ◽  
Paul Corcoran
Keyword(s):  
Northern Ireland ◽  
Population Density ◽  
Suicidal Behaviour ◽  
Urban Areas ◽  
Negative Binomial ◽  
Positive Association ◽  
Negative Binomial Regression ◽  
Binomial Regression ◽  
Self Harm ◽  
Deprived Areas

Abstract Background Factors contributing to suicidal behaviour are complex and multi-faceted. This study took an ecological approach to examine the association between area-level factors and rates of self-harm in Northern Ireland. Methods Data on self-harm presentations to emergency departments (EDs) were obtained from the Northern Ireland Self-harm Registry. The study included residents of Northern Ireland aged 16–64 years. Deprivation was measured using the Northern Ireland Multiple Deprivation Measure 2017. Population density and social fragmentation were calculated using measures from the 2011 census. Associations between area-level factors and self-harm rates were explored using negative binomial regression. Results Between 2013 and 2015, 14 477 individuals aged 16–64 years presented to EDs in Northern Ireland following self-harm. The rate of self-harm was 472 per 100 000 and was higher for male residents (478 vs. 467). Self-harm rates were highest in urban areas—680 per 100 000 in Belfast City and 751 per 100 000 in Derry City. Rates of self-harm in Northern Ireland were more than four times higher in the most deprived areas. A positive association with rates of self-harm held for the deprivation domains of employment, crime, education, health and income. There was a moderate association with population density. Some gender differences emerged, with associations with male rates of self-harm more pronounced. Conclusion These findings indicate that self-harm rates are highest for those residing in highly deprived areas, where unemployment, crime and low level of education are challenges. Community interventions tailored to meet the needs of specific areas may be effective in reducing suicidal behaviour.

scholarly journals The Relationship between Near-Repeat Street Robbery and the Environment: Evidence from Malmö, Sweden

2020 ◽  
Vol 9 (4) ◽  
pp. 188
Author(s):  
Markus Rasmusson ◽  
Marco Helbich
Keyword(s):  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Socioeconomic Indicators ◽  
Law Enforcement Agencies ◽  
Environmental Characteristics ◽  
Binomial Regression ◽  
Using Data ◽  
Near Repeat ◽  
The Relationship ◽  
Crime Event

Near-repeat crime refers to a pattern whereby one crime event is soon followed by a similar crime event at a nearby location. Existing research on near-repeat crime patterns is inconclusive about where near-repeat patterns emerge and which physical and social factors influence them. The present research addressed this gap by examining the relationship between initiator events (i.e., the first event in a near-repeat pattern) and environmental characteristics to estimate where near-repeat patterns are most likely to emerge. A two-step analysis was undertaken using data on street robberies reported in Malmö, Sweden, for the years 2006–15. After determining near-repeat patterns, we assessed the correlations between initiator events and criminogenic places and socioeconomic indicators using a negative binomial regression at a street segment level. Our results show that both criminogenic places and socioeconomic indicators have a significant influence on the spatial variation of initiator events, suggesting that environmental characteristics can be used to explain the emergence of near-repeat patterns. Law enforcement agencies can utilize the findings in efforts to prevent further street robberies from occurring.

scholarly journals Customer reactions to a webshop’s service quality

Empirica ◽  
2019 ◽  
Vol 47 (4) ◽  
pp. 699-731
Author(s):  
Franz Hackl ◽  
Rudolf Winter-Ebmer
Keyword(s):  
Service Quality ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Data Set ◽  
Product Categories ◽  
Service Characteristics ◽  
Binomial Regression ◽  
Quality Aspects ◽  
Quality Dimensions ◽  
The Impact

Abstract E-commerce has become an integral part of the world’s economy. In this study we investigate the impact of service quality in e-tailing on site visits and consumer demand. Such an analysis is important given the almost Bertrand-like competitive structure. Our analysis is based on a large representative data set obtained from a price comparison site covering essentially the complete Austrian e-tailing market. Customer evaluations for a broad range of 15 different service characteristics are condensed using factor analysis. Negative binomial regression analysis is used to measure the impact of service quality dimensions on referral requests to online shops for different product categories. Our results show that the most important service quality aspects are those related to the ordering process and the firm’s website performance.

Mandated Representation: Exploring the Consequences of Gender Quota Design on Parliamentary Activity

2020 ◽  
Author(s):  
Eva-Maria Euchner ◽  
Elena Frech
Keyword(s):  
Negative Binomial ◽  
Gender Diversity ◽  
Negative Binomial Regression ◽  
Gender Quotas ◽  
Binomial Regression ◽  
Interdisciplinary Framework ◽  
Research Gap ◽  
And Gender ◽  
Gender Quota ◽  
The Impact

Abstract Although the scholarship on legislative behaviour widely agrees that electoral rules determine parliamentary activities, surprisingly little is known on the impact of gender quotas. We contribute to this research gap by developing an innovative interdisciplinary framework and by exploring it based on a unique dataset on varying gender quota designs throughout EU countries and parties running for the 7th term of the European Parliament (2009–2014). Based on the scholarship on gender diversity in management teams and the research on gendered processes in political parties, we argue that especially mandated gender quotas stimulate processes of social categorisation, intergroup biasing and competition due to a normative mis-fit between conceptions of gender equality and gender quotas, which in turn influences coordination and communication and hence, parliamentary activity more generally. Combining negative-binomial regression models and expert interviews, we indeed find that mandated gender quotas promote ‘individual’ parliamentary activities (e.g. speeches) and tend to impede ‘collaborative’ parliamentary activities (e.g. reports).

scholarly journals Does Car-Sharing Reduce Car-Use? An Impact Evaluation of Car-Sharing in Flanders, Belgium

2020 ◽  
Vol 12 (19) ◽  
pp. 8155
Author(s):  
Donald A. Chapman ◽  
Johan Eyckmans ◽  
Karel Van Acker
Keyword(s):  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Experimental Methods ◽  
Car Ownership ◽  
Car Sharing ◽  
Car Use ◽  
Binomial Regression ◽  
Quasi Experimental ◽  
Private Car ◽  
The Impact

Private car-use is a major contributor of greenhouse gases. Car-sharing is often hypothesised as a potential solution to reduce car-ownership, which can lead to car-sharing users reducing their car-use. However, there is a risk that car-sharing may also increase car-use amongst some users. Existing studies on the impacts of car-sharing on car-use are often based on estimates of the users’ own judgement of the effects; few studies make use of quasi-experimental methods. In this paper, the impact of car-sharing on car-ownership and car-use in Flanders, Belgium is estimated using survey data from both sharers and non-sharers. The impact on car-use is estimated using zero-inflated negative binomial regression, applied to matched samples of car-sharing users and non-users. The results show that the car-sharing may reduce car-use, but only if a significant number of users reduce their car-ownership. Policy intervention may therefore be required to ensure car-sharing leads to a reduction in car-use by, for example, discouraging car-ownership. Further research using quasi-experimental methods is required to illuminate whether the promise of car-sharing is reflected in reality.

scholarly journals Prevalence of SARS-CoV-2 infection among people experiencing homelessness in Toronto during the first wave of the COVID-19 pandemic

2021 ◽  
Author(s):  
Linh Luong ◽  
Michaela Beder ◽  
Rosane Nisenbaum ◽  
Aaron Orkin ◽  
Jonathan Wong ◽  
...  
Keyword(s):  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Infection Rates ◽  
Individual Site ◽  
Site Characteristics ◽  
Increased Risk ◽  
Binomial Regression ◽  
Great Heterogeneity ◽  
The Relationship ◽  
Over Time

Background: People experiencing homelessness are at increased risk of SARS-CoV-2 infection. This study reports the point prevalence of SARS-CoV-2 infection during testing conducted at sites serving people experiencing homelessness in Toronto during the first wave of the COVID-19 pandemic. We also explored the association between site characteristics and prevalence rates. Methods: The study included individuals who were staying at shelters, encampments, COVID-19 physical distancing sites, and drop-in and respite sites and completed outreach-based testing for SARS-CoV-2 during the period April 17 to July 31, 2020. We examined test positivity rates over time and compared them to rates in the general population of Toronto. Negative binomial regression was used to examine the relationship between each shelter-level characteristic and SARS-CoV-2 positivity rates. We also compared the rates across 3 time periods (T1: April 17-April 25; T2: April 26-May 23; T3: May 24-June 25). Results: The overall prevalence of SARS-CoV-2 infection was 8.5% (394/4657). Site-specific rates showed great heterogeneity with infection rates ranging from 0% to 70.6%. Compared to T1, positivity rates were 0.21 times lower (95% CI: 0.06, 0.75) during T2 and 0.14 times lower (95% CI: 0.043, 0.44) during T3. Most cases were detected during outbreak testing (384/394 [97.5%]) rather than active case finding. Interpretation: During the first wave of the pandemic, rates of SARS-CoV-2 infection at sites for people experiencing homelessness in Toronto varied significantly over time. The observation of lower rates at certain sites may be attributable to overall time trends, expansion of outreach-based testing to include sites without known outbreaks and/or individual site characteristics.

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