Nurse Education in Practice 10 (2010) 119–125

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Nurse Education in Practice

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Diagnosing the problem: Using a tool to identify pre-registration nursingstudents’ mathematical ability

Sharon Harvey a,*, Fiona Murphy a, Richard Lake a, Lynne Jenkins b, AnnLouise Cavanna a, Mike Tait a

a School of Health Science, Swansea University, Swansea SA2 8PP, United Kingdomb Department of Adult and Continuing Education, Swansea University, Swansea SA2 8PP, United Kingdom

a r t i c l e i n f o

Article history:Accepted 21 April 2009

Keywords:Drug calculationsStudent nursesMathematics

1471-5953/$ - see front matter � 2009 Elsevier Ltd. Adoi:10.1016/j.nepr.2009.04.007

* Corresponding author. Tel.: +44 01792518540.E-mail address: S.D.Harvey@swansea.ac.uk (S. Har

a b s t r a c t

Mathematical ability is a skill nurses need to safely administer medicines and fluids to patients (Elliott,M., Joyce, J., 2005. Mapping drug calculation skills in an undergraduate nursing curriculum. Nurse Edu-cation in Practice 5, 225–229). However some nurses and nursing students lack mathematical profi-ciency (Hilton, D.E., 1999. Considering academic qualification in mathematics as an entryrequirement for a diploma in nursing programme. Nurse Education Today 19, 543–547). A tool wasdevised to assess the mathematical abilities of nursing students. This was administered to 304 nursingstudents in one Higher Education Institution (HEI) in Wales, United Kingdom (UK) on entry to a pre-reg-istration undergraduate nursing course. The students completed a diagnostic mathematics test compris-ing of 25 non-clinical General Certificate of Secondary Education (GCSE) level multiple choice questionswith a pass mark set at 72%. The key findings were that only 19% (n = 53) of students passed the test.Students appeared to have difficulties with questions involving decimals, SI units, formulae and frac-tions. The key demographic variable that influenced test scores was previous mathematical qualifica-tions on entry to the course.

The tool proved useful in two ways. First, in identifying those students who needed extra tutorial sup-port in mathematics. Second, in identifying those areas of mathematics that presented difficulties forstudents.

� 2009 Elsevier Ltd. All rights reserved.

Introduction

Mathematics is essential in nursing as it underpins a number ofkey skills including nutritional assessments, fluid balance and bodymass index calculations. However, much of the international re-search into mathematics and numeracy in nursing has concen-trated on its importance in drug calculations (Jukes and Gilchrist,2006; Grandell-Niemi et al., 2001; Weeks et al., 2000). Medicationsafety is a priority as nurses administer around 7000 drugs to pa-tients each day in England and Wales (National Patient SafetyAgency [NPSA], 2007).

Most medications are administered safely, however errorsdo occur, accounting for 25% of litigation cases in the NationalHealth Service (NHS) (Department of Health, 2004) and arethe second most common incident reported to the NPSA. Er-rors can occur at any point in the drug administration process

ll rights reserved.

vey).

and are compounded by a number of factors such as work-load, knowledge of the medication, staffing levels (O’Shea,1999) and an inability to calculate medication doses (Trim,2004).

Nurses need a reasonable level of mathematical skills to safelyadminister medicines and fluids to patients (Elliott and Joyce,2005). However, there is evidence that some trained nurses donot possess the skill of accurately calculate drug dosages and areat risk of administering incorrect dosages of drugs to their patients(Wilson, 2003). There is also concern that some pre-registrationnursing students lack ability in mathematics (Hutton, 1998a). Thiswas evident in our experiences of teaching drug calculations tostudents. It was clear, that some students lacked proficiency in ba-sic mathematics and were unable therefore to perform simple drugcalculations.

This paper reports on a strategy to assess undergraduatepre-registration nursing students’ abilities in mathematics throughthe administration of a diagnostic mathematics test. Two mainfindings were firstly that students have difficulties with somekey areas of mathematics. Secondly, that their qualificationsin mathematics on entry to the course affects theirperformance.

120 S. Harvey et al. / Nurse Education in Practice 10 (2010) 119–125

Background

Mathematical ability in nursing students

Mathematics is an essential skill in nursing. Although, drugdosage calculations are the most visible application of mathemat-ics within the clinical setting, mathematics underpins a widerange of clinical activities from fluid balance calculations, otherclinical measurements such as body mass index (BMI) calcula-tions, as well as understanding and interpreting healthcare re-search. The ability to understand numerical data is central tonursing and competence in numeracy is both a pre-requisite forentry into pre-registration nurse education (Nursing and Mid-wifery Council (NMC), 2004) and entry onto the professional reg-ister (NMC, 2007; Quality Assurance Agency for Higher Education(QAA), 2001). It has been suggested that there is a lack of mathe-matical ability in nursing students, a problem shared by other dis-ciplines in higher education (Learning and Teaching SupportNetwork [LTSN], 2000) and in other healthcare groups (NHS Edu-cation for Scotland (NES) Numeracy Working Group, 2006; Sabin,2001).

Currently there is no minimum formal academic qualification inmathematics stipulated for entry into nurse education within theUK (NMC, 2008). Yet there is an assumption that learners enteringHigher Education function at a higher level and as such are compe-tent in mathematics (Hall et al., 2005; Weeks, 2004; Sabin, 2001).However, a number of UK and international studies have suggestedthat there is a deficiency in calculation skills among pre-registra-tion nursing students (Lauder et al., 2008; Barrett, 2007; Hallet al., 2005; Sabin, 2001; Grandell-Niemi et al., 2001; Hilton,1999; Hutton, 1998a; Kapborg, 1995; Bliss-Holtz, 1994). Althoughthese studies focus on the mathematical abilities of pre-registra-tion student nurses, Allen and Pappas (1999) suggest that poormathematical ability continues after qualifying causing embarrass-ment to both the profession and its professional bodies. This wasconfirmed by Bindler and Bayne (1991), Bindler and Bayne(1984) who demonstrated that up to 38% (n = 23) of student nursesand 43.6% (n = 48) of qualified nurses in the United States (US)were unable to pass a mathematics and drug calculations test ata pass level of 70%.

Jukes and Gilchrist (2006) in the UK demonstrated similar re-sults. In a small sample of 37 second year student nurses testedon drug calculations, only 35% (n = 13) of the students achievedover 70% in the test. Students had difficulties with unit conver-sions, calculations that required multiple steps and ratio but werein general able to perform whole number calculations. These errorsoccurred in basic mathematics such as addition, subtraction, mul-tiplication and division, sometimes producing incorrect and oftenunrealistic answers (Sabin, 2001; Weeks et al., 2000; Blais andBath, 1992).

Jukes and Gilchrist also found that the most common errorsmade were because of conception or problem solving ability ratherthan computational errors. Accurate calculation of drug dosage re-quires the nurse to have both computational and conceptual orproblem solving skills. A deficiency in these skills could affect clin-ical effectiveness and patient safety (Weeks et al., 2000). Thus inaddition to poor arithmetical skills, student nurses had difficultyconceptualising the problem in terms of accurately interpretingprescription charts, medication vials and bottles (Wright, 2004,2008).

Explanations for reduced mathematical ability

A starting point has been to look at the teaching of math-ematics and numeracy in compulsory (up to the age of 16)

school education. One way has been to consider the formal en-trance qualifications in mathematics that students have beforeentering nursing courses and seeing whether that influencestheir performance. There has been considerable research activ-ity in this area, but as Sabin (2001) identifies it is difficult tomake comparisons across studies due to such factors as a lackof standardisation of both the tests used and pass marks. Asan example, Hutton’s (1998a) UK study of 99 nursing studentsconsidered the effect that previous qualifications in mathemat-ics had on their ability to perform basic mathematics in a penand paper diagnostic mathematics test. The average score was51% with only 30% of the students attaining over 50%. Studentswith an entry mathematics qualification above General Certifi-cate of Secondary Education (GCSE) grade C, (a qualificationavailable to students in England, Wales and Northern Irelandwhich is usually taken around the age of 16 years), fairedbetter in the test with an average score of 69.6% than thosewith GCSE grade C or equivalent or less. However, Hilton(1999) identified in the UK that the attainment of GCSE gradeC was not a reliable indicator of student’s ability innumeracy.

Additionally in the UK, students may enter nurse educationvia routes other than mainstream secondary education such as‘Access’ courses. These are further education qualifications toprepare mature students with few or no higher education entryqualifications for entry to higher education. There is little re-search into the mathematical abilities of nursing students enter-ing nurse education via this route. Some studies (Wilson, 2003;Ofori, 2000) identified that possession of entry qualificationssuch as Access courses did not affect overall performance. InScotland, Lauder et al. (2008) identified that wider access stu-dents scored significantly lower on a drug calculation assess-ment than those with standard grades.

In addition to formal qualifications, Sabin (2001) exploreshow structural factors such as gender, class and ethnicity mayaffect individual ability in the performance of mathematics. Gi-ven the larger numbers of women in nursing gender has beenidentified as a possible explanation (Hall et al., 2005; Coben,2003). Although Sabin (2001) argues that ability in mathematicsis socially constructed, it is apparent that women may underes-timate their abilities in mathematics and be more prone tomaths anxiety. The case for differences based on ethnicity is lessclear as it is difficult to unpick the competing influences of socio-economic class, educational attainment and possible racialdiscrimination.

Strategies to improve mathematical ability

The causes of the problem may be complex (Hall et al., 2005),but there is a need to address this within pre-registration educa-tional programmes. There is now interest in benchmarking stan-dards for numeracy and developing strategies for the assessmentof ability in medication calculations (Coben and Hodgen, 2008).However, there are concerns as to the accuracy of such testspartly because of the complexities of constructing them (Cobenand Hodgen, 2008; Wright, 2007). A concern is that numericallycompetent students may fail such tests and, probably of moreconcern, students who are not numerically competent may passsuch tests. A further consideration is when these tests should bedelivered. Sabin (2001) recommends that candidates should betested at the interview stage before commencing the coursewhilst others (Hall et al., 2005) advocate testing early on inthe course.

Once assessment has been performed then various teachingand learning strategies can be utilised to improve students per-formance. These usually include a mixture of approaches includ-

S. Harvey et al. / Nurse Education in Practice 10 (2010) 119–125 121

ing formal face to face teaching and the provision of resourcesranging from workbooks to on-line computer assisted learning(CAL) programmes (Moriarty et al., 2008; Wright, 2004, 2008;Rainbooth and De Masi, 2006; Hall et al., 2005). However theutility and efficacy of such interventions has not been fullyevaluated.

Despite concerns over the mathematical abilities of nursing stu-dents, there is little evidence that failure in a classroom test resultsin drug errors or miscalculations in practice (Jukes and Gilchrist,2006; Hutton, 1998b). However, deaths caused by medication er-rors have increased by 500% between 1991 and 2001 in the UK(Audit Commission, 2001). Although not all are caused by medica-tion errors, O’Shea (1999) suggests that the mathematical skill ofnurses is a key factor in some. It is the duty of educationalists toensure that nurses are educated to a high standard in drug admin-istration and calculation in order to protect the safety of the public(Segatore et al., 1993). However, it appears from this literature,that nursing students may not be proficient in mathematics tosafely calculate drug dosages. If competence in mathematics isnot being achieved then students may not be fit to practice (Hut-ton, 1998a).

Fig. 1. Test scores.

Methods

Aim

To identify pre-registration, undergraduate nursing students’mathematical ability through administering a diagnostic mathe-matics test on entry to the course.

Methodological approach and sample

This was a quantitative, population study of two cohorts ofundergraduate nursing students in one HEI in Wales UK. The totalsample size was 323 consisting of 266 adult branch, 40 mentalhealth and 18 child branch students. The response rate was 94%(n = 304).

Method

First year students on entry to the course completed aWeb-delivered questionnaire comprising a section of demo-graphic details (age, gender, branch programme and mathemat-ical entry qualifications) and a diagnostic mathematics test.The test was devised by a multi-disciplinary team comprisingof a mathematician specialising in the teaching of mathematicsto adults and nurse lecturers involved in the teaching of drugcalculations.

The test consisted of 25 questions at GCSE mathematics levelconcentrating on those mathematical skills needed for drug calcu-lations. These included fractions, SI units (Le Système Internationald’Unités), decimals, percentages, formulae, ratio, rounding, areaand perimeter, basic statistics, graphs and charts. (Examples ofsome of the questions are provided in Box 1). The pass mark wasset at 18 out of 25 (72%) as it was felt that this indicated that a stu-dent had a reasonable level of proficiency in the basic numericalmethods being tested (Elliott and Joyce, 2005). Pass marks for cal-culation tests cited by other authors include 90% (Cunningham andRoche, 2001; Blais and Bath, 1992), 85% (Bliss-Holtz, 1994; Segato-re et al., 1993; Shockley et al., 1989) and 70% (Hilton, 1999). How-ever, initial analysis of the marks obtained by students indicatedthat setting the pass mark at 72% would allow sufficient numbers

in the pass category to enable further statistical analysis to be per-formed on the data.

654321myAges

200

150

100

50

0

Co

un

t

Fig. 2. Age. 1 = between 18 and 25; 2 = between 26 and 30; 3 = between 31 and 35;4 = between 36 and 40; 5 = between 41 and 45; 6 = 46 or older.

Table 1Mean scores by age group.

Age Mean score Standard deviation of score

Young 14.41 3.470Mature 14.50 3.945

122 S. Harvey et al. / Nurse Education in Practice 10 (2010) 119–125

Pilot

The questionnaire and test were piloted on 14 volunteers inwhich the need to give immediate feedback to participants ontheir test scores was identified. As a result of this, a scorepage was added which gave participants their score out of 25.It was delivered to a cohort of nursing students (n = 197) andwas optional, however, only 42% (82) of this cohort completedthe test. The findings of this pilot identified significant mathe-matical difficulties within the cohort; therefore, it was decidedto make the test compulsory for subsequent cohorts ofstudents.

Validity and reliability

Care was taken to ensure that questions were set at anappropriate level and that all topics covered were those rele-vant to numeracy in nursing practice. The choice of the typesof questions asked was also informed by similar tests for nurs-ing students appearing in the literature and to reflect generalstandards of mathematics in the UK (Pirie, 1987). The condi-tions were the same for all students doing the test; all stu-dents took the test at the same stage of the course and tookthe test in a similar setting.

Ethical considerations

The study was presented to the HEI’s ethics committeewhere permission was given to proceed. To facilitate informedconsent, an e-mail was sent to all students explaining the re-search; a verbal presentation of the research was given to thewhole student group and explained again in the small groupsessions. Students could not opt out of doing the test but hadthe option to prevent their test results being used for researchpurposes if they wished. To ensure anonymity, when studentslogged on they were randomly assigned a number on the data-base but this was not linked to either their name or student re-cord number. The data was stored on a secure database, whichonly the research team had access to.

Data analysis

Data from the test was directly written to a database and ana-lysed using the Statistical Package for Social Sciences (SPSS) ver-sion 13.0.1. Cross-tabulational analysis was carried out betweeneach element of demographic data, between the demographic dataand the test result and between each individual questions and testresults. t-Tests were also performed on the mean scores obtainedfor the various sub-categories of demographic data.

Box 2Performance on Specific Question Areas.

Question Topic % Correct

1 Money 92% (n = 281)2 S.I. units 88% (n = 266)3 S.I. units 78% (n = 239)4 S.I. units 29% (n = 88)5 Decimals 84% (n = 255)6 Decimals 27% (n = 83)7 Decimals 29% (n = 88)8 Fractions 58% (n = 176)9 fractions and percentages 63% (n = 193)10 Fractions and decimals 30% (n = 94)11 Fractions 55% (n = 169)12 Ratio 84% (n = 257)13 Decimals and percentages 21% (n = 65)

Results

Identified areas of difficulty

Nineteen per cent (n = 53) of the students passed the test at apass mark of 72% (18/25). The distribution of scores were normallydistributed with students obtaining a mean score of 14.45 with astandard deviation of 3.695 (Fig. 1). The modal score for the testwas 16 out of 25 (64%) and the range was 18.

Students appeared to have difficulty in some areas of basicmathematics such as decimals (questions 6 and 7), formulae (ques-tions 18, 19 and 20) and to lesser extent fractions (questions 8, 9and 11) and SI units (question 4) (Box 2). This was even moreapparent when questions contained a mixture of topics such asquestions 10, 13 and 15.

Demographic factors and test performance

A number of demographic factors were considered includingage, previous mathematical qualifications, branch and gender.Branch was not analysed as the numbers precluded statisticalanalysis.

Question Topic % Correct

14 Percentages 80% (n = 245)15 Decimals/S.I. Units 35% (n = 108)16 Rounding 86% (n = 260)17 Rounding 70% (n = 214)18 Formulae 42% (n = 129)19 Formulae 34% (n = 103)20 Formulae 38% (n = 117)21 Area/perimeter 62% (n = 187)22 Statistics 79% (n = 240)23 Timetable 94% (n = 286)24 Charts 78% (n = 238)25 Interpretation of pie charts 11% (n = 35)

Fig. 3. Highest mathematics qualification; 1 = Certificate of Education in Mathe-matics; 2 = CSE; 3 = GCSE grades A*–C; 4 = GCSE grades D–F; 5 = ‘O’ level grade A–C;6 = ‘O’ level grade D–F; 7 = ‘AS’ level; 8 = Access to Health; 9 = ‘A’ level.

Table 2Relationship between previous qualifications in mathematics and test scores.

Previous qualification Mean score Standard deviation of score

Access 13.57 3.057GCSE/‘O’ level 15.61 3.390Other 12.77 3.895‘A’ level 17.33 2.188

S. Harvey et al. / Nurse Education in Practice 10 (2010) 119–125 123

AgeThe modal age was 18–25 years (Fig. 2). In order to further ana-

lyse the data the ages of the sample were re-classified into young(under 25 years) and mature (26 years and over). A t-test was per-formed on the mean scores obtained by young and mature stu-dents and no significant difference was found between the meanscores obtained by the two groups (Table 1), thus no relationshipwas found between the age of the student and whether theypassed or failed the test.

Previous qualifications in mathematicsThe modal entry mathematics qualification was GCSE A*–C

grade (43%) with the next largest group being the Access to Healthgroup of students (21%) (Fig. 3). An association was found betweenage and previous qualifications with more young students than ex-pected having GCSE/’O’ level grades A*–C and more mature stu-dents than expected coming through the Access route.

In order to further analyse the data, the student’s entry mathe-matical qualifications were re-classified into four groups – Access,GCSE grade C or above (or ‘O’ level), ‘A’ level (a qualification avail-able to students in England, Wales and Northern Ireland which isusually taken around the age of 18 years) and ‘Other’. The ‘Other’category included those students who had obtained a lower gradeGCSE/’O’ level or have a Certificate of Education (CSE). Independentt tests were performed on the mean scores (Table 2) for each ofthese categories and significant differences were noted betweenAccess students and GCSE/‘O’ level grades A*–C students(p < 0.001); between ‘Other’ and GCSE/‘O’ level grades A* and C stu-dents (p < 0.001); between Access and ‘A’ level students (p < 0.001)

Table 3Relationship between gender and test scores.

Gender Mean score Standard deviation of score

Male 15.96 4.273Female 14.30 3.605

and between ‘Other’ students and ‘A’ level students (p < 0.001). Inall cases, Access and ‘Other’ students obtained lower mean scores.

No significant differences were noted between the mean scoresof the ‘A’ level students and GCSE students (p = 0.086). Likewise nosignificant difference was noted between the mean scores of Ac-cess students and ‘Other’ students (p = 0.176). A relationship wasfound between previous qualifications and whether a studentpassed or failed the test (p = 0.001) with less Access and ‘Other’category students than expected passing and more students inthe GCSE category than expected passing. A relationship was alsofound between previous qualifications and whether studentsachieved a correct answer to questions 3 (p < 0.001), 5(p = 0.004), 7 (p = 0.013), 12 (p = 0.002), 13(p = 0.028), 14 (p =0.004), 18(p = 0.047), 19(p < 0.001), 21(p < 0.001), 22 (p = 0.001)(Box 2) with less Access and ‘Other’ students than expected obtain-ing the correct answer.

GenderThe sample consisted of 277 females (91%) and 27 males (9%)

with their modal age being 18 to 25 years. An independent t-testwas performed on the mean scores obtained by male (n = 28)and female (n = 278) students and it was found that there was asignificant difference between the mean score achieved by malesand females (p = 0.023) with male students outperforming femalestudents. Proportionately more male students and less female stu-dents than expected passed (p = 0.039) (Table 3).

Discussion

The aim of the study was to identify pre-registration undergrad-uate nursing students’ mathematical ability. Limitations were thatthe study was confined to one institution in the UK and thus maynot be generalisable and in common with other similar researchstudies, a standardised test was not available. In order to calculatedrugs accurately a basic understanding of mathematical tech-niques is required. Yet, in common with other UK and internationalstudies (Jukes and Gilchrist, 2006; Grandell-Niemi et al., 2001; Hil-ton, 1999, 1998a; Pirie, 1987), this study identified that some nurs-ing students had difficulties with a number of basic mathematicalskills. These include decimals, formulae and to a lesser extent frac-tions and S.I. units.

The average mark for students in this study was similar to oth-ers (Hilton, 1999, 1998a). However many of the students in thisstudy were unable to achieve a 70% pass mark (81% n = 251). Thiswas a worse performance than students in the Hilton (1999) study,where 53.4% (n = 55) failed to achieve 70%. It was apparent thatoverall performance was linked to the students’ previous mathe-matical qualification. Surprisingly, the students with ‘A’ levelmathematics, often referred to as the ‘‘gold-standard”, did not ap-pear to perform as well as expected. Although the mean score ob-tained by the ‘A’ level students was better than for other groups,the mark obtained was not significantly better than students withGCSE/’O’ level grades A*–C. This appears to contradict Wright(2006) who found that students with higher mathematical qualifi-cations were more mathematically able. It would also appear thatthe basic mathematical techniques required to do drug calcula-tions might not be adequate even among ‘A’ level students. Tariq(2002) who identified that students applying to numerate disci-plines do not have the basic mathematical skills for their degreechoice supports this.

Another important finding is that students who came throughthe Access route fared worse than ‘A’ level students and GCSEgrades A*–C students and did not perform significantly better thanthose students in the ‘Other’ category. Students who entered viathe Access route seemed to have particular weaknesses in certain

124 S. Harvey et al. / Nurse Education in Practice 10 (2010) 119–125

key areas and performed worse than other students in these areas.This is an important finding since a large percentage of the totalnumber of students (21% n = 64) entered via this route and doesnot support the findings of Ofori (2000) and Wilson (2003).

Other than previous mathematical qualification, the only otherdemographic factor that influenced test scores was gender,with more males that expected passing the test. Kiefer andSekaquaptewa (2007) argue that the basis for females’ poorerperformance in mathematics is the negative stereotypes they faceresulting in an assumption that women are not as able in mathe-matics as their male counterparts (Hackett and Betz, 1989) andare more prone to math anxiety. This is characterised by ‘panic,helplessness, paralysis, and mental disorganization” (Tobias andWeissbrod, 1980, p. 63), which influences the student’s ability toperform basic mathematical problems. A number of factors exacer-bate anxiety, such as lack of confidence (Dodd, 1999), poor attitudeto mathematics (Ma and Kishor, 1997) and students’ perception ofthe importance of mathematics (Wigfield and Meece, 1988). Thiscan lead to mathematics avoidance (Metje et al., 2007) and impacton the students’ ability to perform accurate drug calculations(Glaister, 2007).

Conclusions

The development and administration of a tool to identify nurs-ing students’ ability in mathematics was successfully imple-mented. It allowed both students and teaching staff to identifyareas of mathematics that needed to be developed further. Thetwo main findings that students have difficulties with key areasof mathematics and that previous qualification in mathematicsinfluence their performance has obvious implications for nurseeducation. The first is the need for HEIs to identify the areas ofmathematics that students have difficulty with and devise appro-priate teaching and learning strategies to support them. This hasbegun to be addressed in this institution by the development ofcomputer assisted learning packages, self-directed on-line mathe-matical support and individual tutor support to complementteaching that is more formal.

The second relates to the admission of candidates to nursingcourses. It could be argued that mathematics entry requirementsshould be standardised to ensure parity across institutions. Forexample, in the UK a minimum entry requirement of GCSE gradeC or above or equivalent could be set. However, this may restrictentry to courses for those; particularly mature students, whomay not have this qualification. If a range of entrance qualificationsare accepted, candidates could sit ‘entrance tests’ in mathematicsprior to admission to ensure agreed standards in mathematicsare met. This would seem of particular importance given the find-ing that some Access students had difficulties possibly dependingon what level they achieved in mathematics on the Access course.

Conflict of interest statement

There are no conflicts of interest.

References

Allen, S., Pappas, A., 1999. Enhancing math competency of baccalaureate students.Journal of Professional Nursing 15 (2), 123–129.

Audit Commission, 2001. A Spoonful of Sugar: Medicines Management in NHSHospitals. Audit Commission, London.

Barrett, G., 2007. Improving student nurses’ ability to perform drug calculations:guesstimate, estimate, calculate. Journal of Children’s and Young People’sNursing 1 (1), 29–35.

Bindler, R., Bayne, T., 1984. Do baccalaureate students possess basic mathematicsproficiency? Journal of Nursing Education 23 (5), 192–197.

Bindler, R., Bayne, T., 1991. Medication calculation ability of registered nurses.Image: Journal of Nursing Scholarship 23 (4), 221–224.

Blais, K., Bath, J.B., 1992. Drug calculation errors of baccalaureate nursing students.Nurse Educator 17 (1), 12–15.

Bliss-Holtz, J., 1994. Discriminating types of medication errors in nursing practice.Nursing Research 43 (6), 373–375.

Coben, D., 2003. Adult Numeracy: Review of Research and Related Literature. NRDC,London.

Coben, D., Hodgen, J., 2008. Assessing numeracy for nursing. Proceedings of theBritish Society for Research into Learning Mathematics 28 (3), 2008.

Cunningham, H., Roche, J., 2001. Using Web CT to determine competency inmedication dosage calculation for nursing students. Nurse Educator 26 (4), 164.

Department of Health, 2004. Building a Safer NHS for Patients: ImprovingMedication Safety. DOH, London.

Dodd, A.W., 1999. Insights from a math phobic. Math Teacher 85 (4),296–299.

Elliott, M., Joyce, J., 2005. Mapping drug calculation skills in an undergraduatenursing curriculum. Nurse Education in Practice 5, 225–229.

Glaister, K., 2007. The presence of mathematics and computer anxiety in nursingstudents and their effects on medication dosage calculations. Nurse EducationToday 27 (4), 341–347.

Grandell-Niemi, H., Hupli, M., Leino-Kilpi, H., 2001. Medication calculation skills ofgraduating nursing students in Finland. Advances in Health Sciences Education6, 15–24.

Hackett, G., Betz, N.E., 1989. An exploration of the mathematics self-efficacy/mathematics performance correspondence. Journal for Research inMathematics Education 20 (3), 261–273.

Hall, C., Jones, C., Hilton, D., Davies, S., MacDiarmid, I., 2005. A study to evaluate firstyear student nurses use of a national numeracy resource to develop key skillsfor nursing practice. Higher Education Academy.

Hilton, D.E., 1999. Considering academic qualification in mathematics as an entryrequirement for a diploma in nursing programme. Nurse Education Today 19,543–547.

Hutton, B.M., 1998a. Do school qualifications predict competence in nursingcalculations? Nurse Education Today 18, 25–31.

Hutton, B.M., 1998b. Nursing mathematics: the importance of application. NursingStandard 13 (11), 35–38.

Jukes, L., Gilchrist, M., 2006. Concerns about numeracy skill of nursing students.Nurse Education in Practice 6, 192–198.

Kapborg, I., 1995. An evaluation of Swedish nurse students’ calculating ability inrelation to their earlier educational background. Nurse Education Today 15 (1),69–74.

Kiefer, A.K., Sekaquaptewa, D., 2007. Implicit stereotypes, gender identification, andmath-related outcomes. Psychological Science 18 (1), 13–18.

Lauder, W., Holland, K., Roxburgh, M., Topping, K., Watson, R., Johnson, M., Porter,M., Behr, A., 2008. Measuring competence, self-reported competence and self-efficacy in pre-registration students. Nursing Standard 22 (20), 35–43.

Learning and Teaching Support Network (LTSN), 2000. Measuring the MathematicsProblem. The Engineering Council, London.

Ma, X., Kishor, N., 1997. Assessing the relationship between attitude towardmathematics and achievement in mathematics: a meta-analysis. Journal forResearch in Mathematics Education 28 (1), 26–47.

Metje, N., Frank, H.L., Croft, P., 2007. Can’t do maths – understanding students’maths anxiety. Teaching Mathematics and its Applications 26 (2), 79–88.

Moriarty, D., Traynor, E., Munro, J., Joy, J., 2008. NES Numeracy Skills Upfront ProjectFinal Report. University of Glasgow. <www.aloscotland.com/alo/downloadresource.htm?id=550->.

National Patient Safety Agency, 2007. Safety in Doses: Improving the Use ofMedicines in the NHS. NPSA, London.

NES Numeracy Working Group, 2006. Identifying and Supporting the NumeracyNeeds of Healthcare Staff in Scotland (Consultation). NES, Edinburgh.

Nursing and Midwifery Council, 2004. Standards of Proficiency for Pre-registrationNursing Education. NMC, London.

Nursing and Midwifery Council, 2007. Essential Skills Clusters for Pre-registrationNursing Programmes. NMC, London.

Nursing and Midwifery Council, 2008. NMC Circular 03/2008: Evidence of Literacyand Numeracy for Entry to Pre-registration Nursing and MidwiferyProgrammes. NMC, London.

Ofori, R., 2000. Age and ‘type’ of domain specific entry qualifications as predictors ofstudent nurses’ performance in biological, social and behavioural sciences innursing assessments. Nurse Education Today 20, 298–310.

O’Shea, E., 1999. Factors contributing to medication errors: a literature review.Journal of Clinical Nursing 8, 496–504.

Pirie, S., 1987. Nurses and Mathematics: Deficiencies in Basic Mathematical SkillAmong Nurses: Development and Evaluation of Early Treatment. RCN, London.

Quality Assurance Agency for Higher Education, 2001. Benchmark Statement:Health Care Programmes Phase 1 – Nursing. QAA, Gloucester. Available from<http://www.qaa.ac.uk/academicinfrastructure/benchmark/health/nursing.pdf>(accessed 16.02.07.).

Rainbooth, L., De Masi, C., 2006. Nursing students’ mathematic calculation skills.Nurse Education Today 26, 655–661.

Sabin, M., 2001. Competence in Practice Based Calculation: Issues for NursingEducation: A Critical Review of the Literature. LTSN, London.

Segatore, M., Edge, D., Miller, M., 1993. Posology errors by sophomore nursingstudents. Nursing Outlook 41 (4), 160–165.

Shockley, J.S., McGurn, W.C., Gunning, C., Graveley, E., Tillotson, D., 1989. Effects ofcalculator use on arithmetic and conceptual skills of nursing students. Journalof Nurse Education 28 (9), 402–405.

S. Harvey et al. / Nurse Education in Practice 10 (2010) 119–125 125

Tariq, V.N., 2002. A decline in numeracy skills among bioscience undergraduates.Journal of Biological Education 36 (2), 76–83.

Tobias, S., Weissbrod, C., 1980. Anxiety and mathematics: an update. HarvardEducational Review 50 (1), 63–70.

Trim, J., 2004. Clinical skills: a practical guide to working out drug calculations.British Journal of Nursing 13 (10), 602–606.

Weeks, K.W., Lyne, P., Torrance, C., 2000. Written drug dosage errors made bystudents: the threat to clinical effectiveness and the need for a new approach.Clinical Effectiveness in Nursing 4, 20–29.

Weeks, K., 2004. Supporting Numeracy Learning amongst Healthcare Students –The Experience from Higher Education Presented at a Calculated Risk NumeracyNeeds in Healthcare Conference, Edinburgh 19th Nov 2004.

Wigfield, A., Meece, J., 1988. Math anxiety in elementary and secondary schoolstudents. Journal of Educational Psychology 80, 210–216.

Wilson, A., 2003. Nurses’ maths: researching a practical approach. Nursing Standard17 (47), 33–36.

Wright, K., 2004. An investigation to find strategies to improve student nurses’maths skills. British Journal of Nursing 13 (21), 1280–1284.

Wright, K., 2006. Barriers to accurate drug calculations. Nursing Standard 20 (28),41–45.

Wright, K., 2007. Student nurses need more than maths to improve their drugcalculating skills. Nurse Education Today 27 (4), 278–285.

Wright, K., 2008. Can effective teaching and learning strategies help student nursesto retain drug calculation skills. Nurse Education Today 28, 856–864.