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A bibliometric study of 25 EU pharmacy departments showed that the top two department members (in terms of the number of articles in which the two top staff members are author (or coauthor) over a 14year period from 1998 through 2012) had hindices of 14 (mean) / 9 (median) and 12 (mean) / 8.5 (median). These were similar to values published for pharmacy department members in the USA. Global data for departments showed lower values as they were affected by the very skewed nature of the distribution with 16% of department members accounting for 76% of the department’s publications.
Strategic planning and assessment are becoming more and more essential in academic pharmacy during a time of socioeconomic difficulty. Measurement of outcomes can help determine how well goals are being met and—ultimately—how well (limited) resources are being used. The measurement of scientific output is an essential element of the review process. However, whilst the relevance of the output of scientists who earn a Nobel Prize is unquestionable, for the vast majority of scientists the problem remains of how to quantify the cumulative impact and relevance of an individual’s scientific research output. Agencies that control quality assurance in research output in pharmacy departments do not have standards for output although attempts are being made to propose and possibly introduce such standards [
These parameters have been applied to pharmacy department publications in the USA [
This paper reports on the use of these indices for the examination of the published output of the staff of pharmacy departments in the EU. For each department the h and m indices for the two top staff members in terms of the number of articles in which they are author (or coauthor) were calculated. The use of h and m indices is generally restricted to individual scientists. Hirsch [
In the first case, for individual scientists, the numbers of published articles, citations, citations for the most cited article, hindex and mquotient were calculated for the top two—in terms of number of articles they authored or coauthored over the period 1998–2012 (15 years)—department staff members. Articles were thoroughly reviewed to ensure that they all had the same author, address, and concerned a pharmaceutical topic.
In the second case, for departments, the address (AD) search parameters were fixed as: AD = (pharmacy SAME city). In this case, the search time span was 1994–2012 (19 years). The top 10 papers (in terms of citations) were checked as having a pharmacy department address and a “pharmaceutical” topic.
Parametric and nonparametric statistics were used. The KolmogorovSmirnov statistic (KS) was calculated to test for continuous, onedimensional probability distributions [
Medians, means, tests of normality (KolmogorovSmirnov (KS) normality test, skewness, kurtosis) for articles published, citations, number of citations per article, number of citations for the most cited article, hindices and m quotients were calculated. In some cases, variability was calculated as coefficient of variation (CV % = standard deviation of the mean / mean × 100). Transformation of data to normalize distributions was not used. Parametric correlation and linear regression were used to investigate relationships between variables. Probability was fixed at
GraphPad v6.01 [
Medians, means, tests of normality (KolmogorovSmirnov (KS) normality test, skewness, kurtosis) for numbers of articles published, total number of citations, hindex, number of citations per article, and number of citations for most cited article for top two department members (in terms of number of articles authored or coauthored) (1st author/coauthor: top panel, 2nd author/coauthor: bottom panel) of EU pharmacy departments.
1st author/coauthor  

Number of values  25^{†}  25  25  25  25  
25% Percentile  17  116  5.5  5.5  33  
Median  49  249  9  10  55  
75% Percentile  79  1752  23  20  157  
Mean  56  971  14  13  109  
Std. Deviation  46  1312  11  10  128  
Std. Error of Mean  9.1  262  2.2  2.1  26  
KS normality test  
KS distance  0.13  0.27  0.25  0.19  0.28  
0.2  < 0.0001

0.0004

0.0196  < 0.0001


Passed normality test (alpha=0.05)?  Yes  No  No  No  No  
Skewness^{††}  0.9  1.8

0.92

0.91

2.2


Kurtosis^{†††}  0.19  3.3

−0.22  0.44  5.4


2nd author/coauthor  






Number of values  24^{†}  24  24  24  24  
25% Percentile  13  87  5.3  0.063  23  
Median  28  221  8.5  0.32  40  
75% Percentile  70  757  18  4.6  81  
Mean  45  661  12  3.3  66  
Std. Deviation  39  965  10  6.9  66  
Std. Error of Mean  8  197  2.1  1.4  14  
KS normality test  
KS distance  0.2  0.25  0.18  0.32  0.21  
P value  0.0147

0.0003

0.0355  < 0.0001

0.0072


Passed normality test (alpha=0.05)?  No  No  No  No  No  
Skewness^{††}  0.93

2  1.2*  2.8

1.8


Kurtosis^{†††}  0.078  2.7

0.82  7.6

2.9

^{†} Of the 27 EU member states, 2 (Cyprus and Luxembourg) do not have pharmacy departments ; ^{††} (upper panel) Percentage points for n = 25 and alpha = 0.05: 0.71; ^{†††} Percentage points for n=25 and alpha = 0.05: 2.32; Probability given as:
Top department scientists (in terms of number of articles authored or coauthored) had a median of 49 and a mean of 56 articles published up to 2012. The median in terms of the total number of citations amounts to 249, the mean to 971; the median in terms of the citations per article amounts to 10, the mean to 13. The median in terms of the citations for the most cited article amounts to 9, the mean to 14. The second best department scientists (in terms of number of articles authored or coauthored) had a median of 28 and a mean of 45 articles published. In 1/25 departments a 2nd author/coauthor could not be clearly identified. The total number of citations had a median of 221 and a mean of 661; there were 0.3 (median) and 3.3 (mean) citations per article and 40 (median), 66 (mean) citations for the most cited article. The hindex had a median of 8.5 and a mean of 12.
The number of articles authored or coauthored by the two top department members represented 24 (median) and 34 (mean) percentage of the total published output of the department. The number of citations obtained for articles published by the two top publishers represented 26 (median) and 39 (mean) percentage of the total number of citations for the published output of the department. With a mean number of department staff of 80 (see below) this means that 2/80 = 2.5% of the staff are involved in 34% of the articles published by departments
In a subgroup of the 4 departments (staff numbers 91 ± 23) publishing the highest number of articles, the output of the top 15 (16.5%) principal publishing staff members accounted for 76±6% of the total published output of the department.
The data for the number of staff per department passed all normality tests and the median (70) and mean (80) were similar; variability was high (coefficient of variability (CV) = 69%). The number of articles published had a median of 363 and a mean of 511; this data failed the KS normality test showing skewness with values bunched to the left at low numbers of articles published, and a long right tail at high numbers. The same was true for the total number of citations (median, 3507, and mean, 6571). The number of citations per article had a median of 9 and a mean of 11; the distribution was flat with a right side tail. The number of citations for the most cited article had a similar distribution with a median of 125 and a mean of 238. Values for the hindex passed the KS test but nonetheless showed skewness with a right side tail; median hindex was 25 and mean 30.
Medians, means, tests of normality (KolmogorovSmirnov (KS) normality test, skewness, kurtosis) for numbers of staff, articles published, citations, number of citations per article, number of citations for most cited article, and m quotient / number of staff of EU pharmacy departments.
Staff  Articles  Citations  hindex  Number of citations per article  Number of citations for most cited article  m quotient / number of staff  

Number of values  25^{†}  25  25  25  25  25  25 
25% Percentile  28  70  750  16  5.5  88  0.014 
Median  70  363  3507  25  9  125  0.022 
75% Percentile  122  797  9333  43  14  308  0.049 
Mean  80  511  6571  30  11  238  0.029 
Std. Deviation  55  473  9115  20  7.5  280  0.02 
Std. Error of Mean  11  95  1823  4.0  1.5  56  0.004 
KS normality test  
KS distance  0.14  0.18  0.28  0.12  0.19  0.24  0.2 
P value  > 0.10  0.0424*  < 0.0001****  > 0.10  0.0171*  0.0007***  0.0086** 
Passed normality test (alpha=0.05)?  Yes  No  No  Yes  No  No  No 
Skewness^{††}  0.61  0.71*  2.2*  0.86*  1.5*  2.4*  0.84* 
Kurtosis^{†††}  −0.35  −0.48  5.5*  0.39  2.8*  6*  −0.57 
^{†} Of the 27 EU member states, 2 (Cyprus and Luxembourg) do not have pharmacy departments; ^{††} Percentage points for n = 25 and alpha = 0.05: 0.71; ^{†††} Percentage points for n = 25 and alpha = 0.05: 2.32; Probability given as:
Analysis was done for publications from 1994 through 2012 in 20 departments; in the 5 remaining departments the period was shorter. These departments were created post1994. The mquotients had a normal distribution with a median of 2.00 and a mean of 2.05; variability was high (CV = 60%). The variable “m / staff number” had a distribution with a right side tail; the median was 0.022 and the mean 0.029.
In some cases the median is very different from the mean (e.g.,
When the data presented here for individual scientists are compared to those published by the group of Thompson and Nahata [
A question can be posed as to whether the top authors/coauthors are in the biggest departments with (possibly) the greatest research resources. Although—as to be expected—there was a positive correlation between number of articles published and number of staff (r^{2} = 0.41,
Comparison of data with that of pharmacy staff members in the USA.
USA  EU  

Reference  [ 
[ 
This work  This work 
Professor at a researchintensive pharmacy department  Pharmacy practice chairs  1st publisher  2nd publisher  
Articles published / year  3.2/2.0  1.4/0.8  4.0/3.5  3.2/2.0 
Citations / year  49/13  Not available  69/18  47/16 
Citations / article  7.1/4.2  7.9/6.7  13/10  3.3/0.3 
mquotient  2.5/2.0  0.36/0.30  1.0/0.6  0.9/0.6 
Data are given as mean/median. In reference 2 the period of analysis was 19652008; in reference 3 the period was 2005–2009.
The starting point for the evaluation of departments was the department address. This approach will include present and former staff members. Another way of calculating hindices for groups has been proposed by Schubert [
The approach caused difficulties in the case of the 9/25 cases in which pharmacy is not an independent, individual department but just another department alongside others such as surgery and cardiology—all of which had the same “pharmacy and medicine” school address, with the same street address.
Calculations based on a group as defined by address and not on individuals mean that in any given department there may be staff with degrees in areas other than pharmacy (such as medicine, pharmacology and chemistry); such persons are included. Pharmacy department staff members who do their research in another department and use that address are not included.
Although the global approach to calculation of hindices has been criticised^{1}° the correlation between the individual hindices for the top scientist (in terms of number of articles authored or coauthored) and the global m quotients for their departments showed a correlation coefficient of r^{2} = 0.72 (
The size of the pharmacy departments in the EU is small (mean 80, median 70) but production is high with 511 (mean) / 363 (median) articles published over the 19 year period. Distributions are not normal with significant skew (values bunched to the left with a long right side tail) and often significant kurtosis (flattening of distributions). Thus distributions are pulled to the right by the better performing departments. The number of articles published by the two top staff members represented 24 (median) and 34 (mean) percentage of the total published output of the department. Furthermore in a subgroup of the 4 departments (mean staff numbers 91 ± 23) publishing the highest number of articles, the output of the top 15 (16%) publishing staff accounted for 76% ± 6% of the total published output of the department. This is also seen when comparing data for departments to that for department members. Based on the mquotient / number of staff for departments the “average” department member would have an hindex for a 15 year career of 15 × 0.029 = 0.44 whereas the actual values for the top two authors/coauthors were 14 and 12, respectively. This emphasizes that the global hindex for departments calculated here includes many staff members with little or no output.
A proviso on methodology has to be added, however. In
This study was performed in pharmacy using the Web of Knowledge database. Journal indicators vary among disciplines and databases [
Finally, in a recent paper from Ding and coworkers at the China Pharmaceutical University in Nanjing, it was concluded that multiple bibliometric indicators showed that China and other Asian countries were lagging behind the USA and European countries in terms of the quality of their scientific output [
A bibliometric study of the two top scientists in EU pharmacy departments gave hindices of 14 (mean)/9 (median) for the first and 12 (mean) / 8.5 (median) for the second, similar to values published from pharmacy departments in the USA. Results compared to those from other regions of the world showed that performance in the EU—as in the USA—is at a relatively high level. This may partially explain the good performance of research and development in the European and American pharmaceutical industries. Data for departments gave much lower values as they were affected by the skewed nature of the distribution with 16% of department staff involved in 76% of the department’s publications. The results showed that qualitative performance of individuals or departments is not related to the size of the department. Scientists from some small departments had a performance equal to that of those from much larger departments showing that resources were not the only determinate for performance. Finally the paper discussed the strengths and the weaknesses of the approach used here for evaluating the performance of departments compared to other published methods.
This project has been funded with support from the European Commission. This article was produced with the support of the Lifelong Learning programme of the European Union; grant number 527194LLP120121BEERASMUSEMCR. This publication reflects the views only of the author, and the European Commission cannot be held responsible for any use which may be made of the information contained therein. The author thanks the PHARMINE/PHARQA partners (
J. Atkinson is executive director of the EU consortia PHARQA “Quality assurance in European pharmacy education and training” (527194LLP120121BEERASMUSEMCR) and PHARIN “Competences for industrial pharmacy practice in biotechnology” (538252LLP12013BEERASMUSEKA).