Predictors of nurse absenteeism in hospitals: a systematic review

Predictors of nurse absenteeism in hospitals:a systematic review

MANDY M. DAVEY B S C N , R N , M N1, GRETA CUMMINGS P h D , R N

2,3, CHRISTINE V. NEWBURN-COOK P h D ,

R N2,4 and ELIZA A. LO B S C N , R N

2,5

1Registered Nurse, Bonnyville Health Centre, Bonnyville, AB, Canada, 2Associate Professor, Faculty of Nursing,University of Alberta, 3Principal Investigator, CLEAR Outcomes Research program; New Investigator, CanadianInstitutes of Health Research; Population Health Investigator, Alberta Heritage Foundation for Medical Research,University of Alberta, 4Associate Professor & Associate Dean (Research), Faculty of Nursing, University of Albertaand 5MN Student, Faculty of Nursing, University of Alberta, Edmonton, AB, Canada.

Introduction

Absenteeism of front-line staff nurses is of increasing

concern and a costly problem for hospital employers

(Taunton et al. 1995, Buschak et al. 1996, Benefits

Interface 2000). This issue amplifies the nursing short-

age, decreases employee morale (Seago 1996, Haun

et al. 2002) and disrupts the continuity of patient care

Correspondence

Greta Cummings

5-125 Clinical Sciences Building

Edmonton, AB T6G 2G3

Canada

E-mail: [email protected]

D A V E Y M . M . , C U M M I N G S G . , N E W B U R N - C O O K C . V . & L O E . A . (2009) Journal of Nursing

Management 17, 312–330

Predictors of nurse absenteeism in hospitals: a systematic review

Aim This study aimed to identify and examine predictors of short-term absences ofstaff nurses working in hospital settings reported in the research literature.

Background Front-line staff nurse absenteeism contributes to discontinuity of

patient care, decreased staff morale and is costly to healthcare.

Evaluation A systematic review of studies from 1986 to 2006, obtained through

electronic searches of 10 online databases led to inclusion of 16 peer-reviewed

research articles. Seventy potential predictors of absenteeism were examined and

analysed using content analysis.

Key issue Our findings showed that individual �nurses� prior attendance records�,�work attitudes� (job satisfaction, organizational commitment and work/job

involvement) and �retention factors� reduced nurse absenteeism, whereas �burnout�and �job stress� increased absenteeism. Remaining factors examined in the literature

did not significantly predict nurse absenteeism.

Conclusions Reasons underlying absenteeism among staff nurses are still poorly

understood. Lack of robust theory about nursing absenteeism may underlie the

inconsistent results found in this review. Further theory development and research is

required to explore the determinants of short-term absenteeism of nurses in acute

care hospitals.

Implications for nursing management Work environment factors that increase

nurses� job satisfaction, and reduce burnout and job stress need to be considered in

managing staff nurse absenteeism.

Keywords: absenteeism, nurse, predictors, systematic review

Accepted for publication: 6 September 2008

Journal of Nursing Management, 2009, 17, 312–330

DOI: 10.1111/j.1365-2834.2008.00958.x312 ª 2009 The Authors. Journal compilation ª 2009 Blackwell Publishing Ltd

(Taunton et al. 1995) ultimately having a negative im-

pact on the cost and quality of care and outcomes for

patients.

In Canada, absenteeism data of individual nurses are

difficult to find, largely owing to privacy of information

legislation. The Canadian Institute of Health Informa-

tion (CIHI) reports absenteeism statistics that incorpo-

rate and compare all healthcare professionals (including

nurses) to other non-healthcare occupations. On aver-

age, healthcare professionals are 1.5 times more likely

to be absent from work as a result of illness or injury

than other occupations (CIHI 2005). Average days lost

per year for Canadian healthcare professionals range

from 12 to 15 days (CIHI 2005). If a nurse makes $28/

h, works 40 hours a week, and has called in sick six

times in 1 year, it costs the organization $1344.00 (not

including benefits) to pay that nurse while absent. To

replace that nurse at double time (should overtime rates

for replacement be required) costs the organization

$2688; the total cost is $4032. To replace 50 nurses

who are absent six times in one year would cost

approximately $201, 600. Absenteeism is a current and

expensive problem that must be explored in greater

detail.

Defining and measuring absence

�Absenteeism� is defined as not coming to work when

scheduled, and is measured by frequency or duration

of work days missed. There are two types of absen-

teeism – �non-culpable� (involuntary or approved) and

�culpable� (voluntary or unapproved) absence (Ham-

mer & Landau 1981, Buschak et al. 1996, Australian

Faculty of Occupational Medicine 1999, Thomson

2005). Non-culpable or involuntary absence occurs for

reasons beyond the control of the nurse (e.g. personal

illness, family death, weather, transportation prob-

lems). In contrast, voluntary or motivated absences

occur when the nurse makes a decision to be absent.

Distinguishing between voluntary and involuntary

absences can be very difficult because hospital-based

nurses with formal sick-leave programmes typically

only receive benefits when they �call in sick� (Hackett

et al. 1989, Gellatly & Luchak 1998, Hemmingway &

Smith 1999). Thus, virtually all absences are classified

as sick days (i.e. non-culpable; involuntary), whether

the absence is for personal illness or not (for a

description of these classification errors, see Hammer

& Landau 1981).

Although imperfect, a practical way to distinguish

non-culpable from culpable absence is to assess the

frequency and duration of absences/sick days. Research

has shown consistently that frequency measures provide

a reasonable index of culpable or voluntary absenteeism

(Chadwick-Jones et al. 1971, Hammer & Landau

1981, Hackett & Guion 1985). To measure absence

frequency, each incident or episode of absence is

counted regardless of the duration of absence. Higher

frequency scores are interpreted as a sign of more rather

than less voluntary absence. Duration measures provide

an index of involuntary absence such as the Time Lost

Index (Hammer & Landau 1981, Hackett & Guion

1985). To assess absence duration, the total number of

days lost are tallied, regardless of the number of inci-

dents. Nurses with high rather than low duration scores

tend to have more involuntary absence (e.g. broken leg,

recovering from surgery). Often, studies include one or

more measures of absence.

Predictors of absenteeism

The 2005 CIHI report stated that nurses with absen-

teeism totalling more than 20 days commonly reported

�high job strain, low supervisor support and high

physical demands on the job, low control over practice,

lack of respect from supervisors, or high role overload

as factors responsible for absenteeism� (CIHI 2005,

p. xvi). Additionally, while considerable research has

been conducted identifying factors related to nurse

absenteeism, no current systematic reviews were found

that examined the relationship between individual

and organizational characteristics and absenteeism of

nurses.

Objective

This systematic literature review examined the rela-

tionships between individual and organizational fac-

tors, and absenteeism of staff nurses (Registered Nurses

and Licensed Practical Nurses) in acute care hospital

settings. The following research question guided this

review: �What are the determinants of individual staff

nurse absenteeism in hospital settings?�.

Methods

Search strategy

The search was conducted using 10 online databases:

ABI Inform, Academic Search Complete, CINAHL Plus

with Full Text, Cochrane Library, EMBASE, Health

Source Nursing/Academic Addition, Ovid Healthstar,

Ovid Medline, PsychINFO and SCOPUS. English-only

studies from 1986 to 2006 were accessed.

Staff nurse absenteeism

ª 2009 The Authors. Journal compilation ª 2009 Blackwell Publishing Ltd, Journal of Nursing Management, 17, 312–330 313

Inclusion criteria

Titles and abstracts were screened twice for the fol-

lowing keywords: �hospital�, �tertiary care centre�, �nur-

ses (RNs, LPNs)� and �absenteeism� or �sick leave�, to

ensure that key articles were not missed. Only primary

reports of qualitative or quantitative research published

in peer-reviewed journals were included. Absenteeism

must have been measured or qualitatively analysed.

Articles were excluded that reported absenteeism of

student nurses, nursing attendants, psychiatric nurses,

or nurses who worked in public health, the community,

or in long-term care facilities. Only studies that sampled

nurses from hospitals were included. Articles discussing

absenteeism related to true (certified) mental or physical

illness, pregnancy or injury were excluded; these

determinants would have skewed the results as they are

legitimate causes of unplanned absenteeism.

Screening

From the review of titles and abstracts, full manuscripts

were retrieved for screening using the primary inclusion

criteria. To meet the final inclusion criteria, a relation-

ship between the determinant (independent variable)

and absenteeism (dependent variable) had to be re-

ported and measured. No qualitative studies were found

that met the inclusion criteria.

After initial screening, articles that met the inclusion

criteria discussed a predictor or determinant and

absenteeism. These articles were then grouped into

two categories, those that measured the independent

variable and absenteeism, and those that discussed a

non-measurable intervention or strategy to influence

absenteeism. As a result of the lack of precision in

measuring the relationship between independent and

dependent variables, research studies that measured only

absenteeism, but not the independent variable, were

removed. Only studies that measured the relationship

between absenteeism and its predictors were analysed.

A second reviewer independently screened 30 ran-

domly selected articles to ensure inter-rater reliability.

One discrepancy between the reviewers regarding the

inclusion of an article was dealt with by consensus.

Methodological assessment

A methodological quality assessment was completed on

each included study using a tool adapted from prior

published systematic reviews (Estabrooks et al. 2001,

Cummings & Estabrooks 2003, Estabrooks et al. 2003,

Wong & Cummings 2007). Thirteen items in this tool

were used to rate four areas of research: design, sam-

pling, measurement and statistical analysis. Twelve out

of 13 items scored 1 point. An additional point was

awarded if absenteeism was observed rather than self-

reported. If authors did not report an item addressed in

the quality assessment tool, zero points were assigned.

The total quality assessment scores ranged from 1 to 14

and, each article was categorized into low (1–4), med-

ium (5–9) or high quality (10–14). Studies of low

quality were excluded.

Results

Search results

The initial search yielded 2401 titles and abstracts. Of

these, 423 papers were retrieved for screening and 71

papers met the inclusion criteria. Thirty-three papers

were removed because they did not measure a predictor

or influence of absenteeism (an intervention designed to

influence absenteeism). Twenty-two papers were of low

quality and removed from the review. These papers had

discrepancies in the reliability and validity of their

instrumentation; eleven did not use a prospective

design, and all had sampling deficiencies. In total, 16

articles were included. Two authors (Boumans &

Landeweerd 1993, 1994, Gellatly 1995, Gellatly &

Luchak 1998) used the same sample and data in each of

two papers, reporting separate analyses in each paper.

Therefore, each of these two sets of papers was con-

sidered as one study, to prevent inflation of reported

results, leaving a total of 14 studies for analysis. Table 1

provides a summary of the search strategy results.

Table 2 provides a summary of the strengths and

limitations for the final group of 14 studies (which had

yielded 16 papers).

Of the 16 papers included in the systematic review,

three were published between 1986 and 1989, 10

between 1990 and1999, and three between 2000 and

2002. Table 3 summarizes the characteristics of each

paper. This reflects that most data were collected in the

1980s and 1990s, even although some studies were

published between 2000 and 2002.

Seven studies were conducted in the USA and three in

Canada. One Canadian study was published in 2002,

the remainder in the 1990s. Two studies were con-

ducted outside of North America, one in the Nether-

lands and the other in Israel. Two researchers did not

report which country their data came from.

Demographics were reported in 13 out of the 14

studies. Eleven studies reported the age of the nurses,

and the mean age across studies was 35.1 years. All

M. M. Davey et al.

314 ª 2009 The Authors. Journal compilation ª 2009 Blackwell Publishing Ltd, Journal of Nursing Management, 17, 312–330

other studies reported the average nurse age range as

mid-thirties to early forties. In all studies, the majority

of respondents were female (>90%). The unit of anal-

ysis in four studies was at the individual level and in

three studies was at the group level. The remaining nine

studies did not identify the unit of analysis. Each study

discussed collecting data at the individual level, but did

not explain how the results were analysed and reported

(individual or group). This is problematic, as nurse

information and results were not consistently reported

independently from other healthcare workers in each

sample.

Absenteeism

The most common measure of absenteeism reported

was �absence frequency�, which was defined as the

number of days absent (incidents) over a given period of

time. Other measures used were total days, duration

and percentage. Three studies asked participants to self-

report their personal absenteeism rates (Boumans &

Landeweerd 1993, 1994, Gellatly 1995, 1998, Gold-

berg & Waldman 2000).

Four studies reported using a theoretical framework

that directly conceptualized absenteeism and its rela-

tionship with other factors (Hackett et al. 1989, Gel-

latly 1995, 1998, Taunton et al. 1995, Goldberg &

Waldman 2000). Ten papers (nine studies) included a

conceptual model or theoretical framework that incor-

porated absenteeism, but absenteeism was not the cen-

tral focus (Boumans & Landeweerd 1993, 1994,

Somers 1995, Taunton et al. 1995, Seago 1996, Griff-

eth et al. 1997, 1998, Zboril-Benson 2002). Two

studies did not report using a theoretical framework

(Blau 1986, Parker & Kulik 1995). Each of the absen-

teeism theoretical frameworks was different. Gellatly

(1995) and Gellatly and Luchak (1998) used an absence

culture framework by Nicholson and Johns (1985),

which suggests that the level of trust and salience of the

culture are necessary for members to develop an ab-

sence culture (p. 1086). Four different absence cultures

were presented based on high and low dimensions of

trust and salience. This framework suggests that

Table 1Search strategy

Database 1986–October 2006 Search terms Number

ABI Inform Absenteeism OR sick leave AND nurse (KW) 42Academic Search Premiere Absenteeism OR sick leave AND nurse (KW & Heading) 101CINAHL Plus with Full Text Absenteeism OR sick leave AND nurse (KW & Heading) 175Cochrane Library (CDSR, ACP Journal Club, DARE, CCTR) Absenteeism OR sick leave AND nurse (KW) 115EMBASE Absenteeism OR sick leave AND nurse 344Health Source: Nursing/Academic Edition Absenteeism OR sick leave AND nurse (KW) 74Ovid Healthstar Absenteeism OR sick leave AND nurse (KW) 561Ovid Medline Absenteeism OR sick leave AND nurse (KW) 552PsychINFO Absenteeism OR sick leave AND nurse (KW) 85SCOPUS Absenteeism OR sick leave AND nurse (KW) 335Manual Search of Journal of Organizational Behavior Absenteeism OR sick leave AND nurse (KW) 17Total Titles and Abstracts 2401Articles retrieved and screened 423First selection of articles 71Second selection of articles 38Final Selection of included articles 16Final Selection of included studies 14

Table 2Quality assessment of included studies

Summary of quality assessment – 14 studies included

CriteriaNumber of

studies

Yes No

Design:Prospective studies 7 7Used probability sampling 3 11Sample:Appropriate/justified sample size 0 14Sample drawn from more than one site 7 7Anonymity protected 3 11Response rate >60% 5 9Measurement:Determinant measured reliably 12 2Determinant measured with a valid instrument 3 11Absenteeism observed rather than self-reported* 11 3Internal consistency ‡ .70 when scale used 0 14Theoretical model/framework used 11 3Statistical analyses:Correlations analysed when multipledeterminants studied

10 4

Management of outliers addressed 0 14

*This item scored two points. All others scored one point.



Tab

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M. M. Davey et al.


Tab

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Con

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Jour

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NR

a=

.76

a=

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a=

.86

NR

a=

.88

NR

NR

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NR

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NR

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inte

rvie

w–

7di

men

sion

s)R

ole

Cla

rity

(no

title

:R

izzo

etal

.19

70)

Com

mitm

ent

toJo

bC

hoic

e(n

otit

le:

Col

arel

li19

84)

Met

Exp

ecta

tions

(no

title

:no

auth

or)

Cop

ing

(Ind

exC

opin

gE

ffect

iven

ess,

Dug

oni&

Ilgen

1981

)Jo

bS

atis

fact

ion

(no

title

:H

om&

Grif

feth

1991

;28

face

ts)

Abs

ente

eism

(did

not

atte

ndw

ork

whe

nsc

hedu

led

durin

g1s

tye

arof

empl

oym

ent)

Bla

nkre

spon

ses

4pt

scal

e6

item

s,5

ptLi

kert

Ave

rage

ofite

ms

2ite

ms

NR

NR

Tot

alda

ys

NR

a=

.86

a=

.83

a=

.79

a=

.82

a=

.93

a=

.94

NR

Mul

tiple

auth

ors

NR

NR

NR

NR

NR

NR

NR

Str

uctu

ral

Equ

atio

nM

odel

ing

Hac

kett

etal

.198

9,A

cad

Man

age

JC

ount

ryN

RP

hase

1:14

0nu

rses

Pha

se2:

54nu

rses

2ho

spita

ls(G

ener

al&

Ped

s)

Pha

se1

Que

stio

nnai

res

rega

rdin

gde

scrip

tive

wor

k-re

late

dat

titud

es,

valu

es&

stre

ssor

sof

thei

rpe

rson

nel

Incl

uded

self-

repo

rted

reas

ons

for

abse

nces

&at

tend

ance

(no

title

,re

sear

cher

mad

e)P

hase

2Q

uest

ionn

aire

for

each

shift

(4–5

mo)

tom

onito

roc

curr

ence

ofab

senc

e-re

leva

ntev

ents

(no

title

,re

sear

cher

mad

e)

NR

2-ite

ms

20ite

ms

NR

NR

a=

.30–

.97

NR

NR

NR

Prin

cipa

lC

ompo

nent

sM

ultip

leR

egre

ssio

n

Hem

min

gway

&S

mith

1999

,J

Occ

upO

rgan

Psy

chC

anad

a25

2R

Ns

4ho

spita

lsO

ccup

atio

nalC

limat

e(w

ork

pres

sure

,au

tono

my,

supe

rvis

or,

supp

ort

&pe

erco

hesi

onsu

bsca

les

ofW

ork

Env

ironm

ent

Sca

le;

Moo

s&

Inse

l197

4)R

ole

Con

flict

(no

title

:R

izzo

etal

.19

70)

Rol

eA

mbi

guity

(no

title

:R

izzo

etal

.19

70)

Wor

kloa

d(N

ursi

ngS

tres

sS

cale

,G

rey

etal

.19

81)

Dea

th&

dyin

gof

patie

nts

(Nur

sing

Str

ess

Sca

le,

Gre

yet

al.

1981

)A

bsen

teei

sm(s

hort

-ter

m,

2da

ysor

less

–se

lfre

port

ofpr

evio

us6

mon

ths)

10tr

ue/fa

lse

per

subs

cale

8ite

ms

6ite

ms

6ite

m,

4pt

scal

e7

item

sƒ

a=

.52–

.73

a=

.50

a=

.78

a=

.79

a=

.81

NR

NR

NR

NR

NR

NR

NR

Hie

rarc

hica

lR

egre

ssio

n



Tab

le3

Con

tinue

d

Aut

hor(

s)Y

ear

Jour

nal&

Cou

ntry

Sam

ple

Mea

sure

men

t/Ins

trum

ents

Sco

ring

Rel

iabi

lity

Val

idity

Ana

lysi

s

Par

ker

&K

ulik

1995

,J

Beh

avM

edU

SA

73R

Ns

1ho

spita

lB

urno

ut(M

asla

chB

urno

utIn

vent

ory-

3su

bsca

les:

Mas

lach

&Ja

ckso

n19

86)

Em

otio

nale

xhau

stio

nD

eper

sona

lizat

ion

Per

sona

lacc

ompl

ishm

ent

Abs

ente

eism

(#da

ysab

sent

inpa

st6

mon

ths

–se

lfre

port

&ho

spita

lrec

ords

for

46nu

rses

)

22ite

m,

7pt

scal

eN

RN

R›v

alue

s=

flbu

rnou

tƒ

a=

.78–

.90

NR

NR

NR

NR

NR

NR

NR

NR

NR

Mul

tiple

Reg

ress

ion

Sea

go19

96,

JN

urs

Adm

US

A67

nurs

ing

units

5ho

spita

lsW

orkg

roup

Cul

ture

(Org

aniz

atio

nalC

ultu

reIn

vent

ory:

Coo

ke&

Laffe

rty

1987

)W

orkp

lace

Str

ess

(Job

Con

tent

Que

stio

nnai

re:

Kar

asek

1979

)H

ostil

ity(C

ook

&M

edle

yH

ostil

ityS

cale

:C

ook

&M

edle

y19

54)

Abs

ente

eism

(per

cent

age

ofsh

ifts

not

atw

ork

assc

hedu

led

over

3m

onth

s–

hosp

italr

ecor

ds)

120

item

s,5

ptLi

kert

15ite

ms

50T

rue/

Fal

seite

ms

%

a=

.94

a=

.69–

.77

a=

.83

NR

Fac

tor

Ana

lysi

sN

RN

RN

R

Cor

rela

tion

Som

ers1

995,

JO

rgan

US

A30

3nu

rses

1ur

ban

hosp

ital

Org

aniz

atio

nalC

omm

itmen

t(A

ffect

ive,

Con

tinua

nce,

&N

orm

ativ

e–

notit

le:

Alle

nan

dM

eyer

1990

)Jo

bW

ithdr

awal

Inte

ntio

n(n

o-tit

le:

Blu

edor

n19

82)

Abs

ente

eism

(Tot

alab

senc

esdu

ring

a12

mon

thpe

riod

&A

nnex

edab

senc

es–a

ttach

edto

wee

kend

/ho

liday

perio

dsfo

rsa

me

12m

onth

s–

hosp

italr

ecor

ds)

NR

NR ƒ

a=

.71–

.81

a=

.88

NR

NR

NR

NR

Logi

stic

Reg

ress

ion

Tau

nton

etal

.198

9,JO

NA

US

A71

wor

kers

(59

RN

s)1

hosp

ital

Ret

entio

n(n

otit

le:

Pric

e&

Mue

ller

1981

)T

urno

ver

(no

title

:P

rice

&M

uelle

r19

81)

Job

Sat

isfa

ctio

n(n

otit

le:

Pric

e&

Mue

ller

1981

)In

tent

toS

tay

(no

title

:P

rice

&M

uelle

r19

81)

Opp

ortu

nity

Els

ewhe

re(n

otit

le:

Pric

e&

Mue

ller

1981

)S

ocia

lInt

egra

tion

(no

title

:P

rice

&M

uelle

r19

81)

Edu

catio

n(n

otit

le:

Pric

e&

Mue

ller

1981

)K

insh

ipR

espo

nsib

ility

(no

title

:P

rice

&M

uelle

r19

81)

Rou

tiniz

atio

n(n

otit

le:

Pric

e&

Mue

ller

1981

)P

artic

ipat

ion

(no

title

:P

rice

&M

uelle

r19

81)

Inst

rum

enta

lCom

mun

icat

ion

(no

title

:P

rice

&M

uelle

r19

81)

Pay

(no

title

:P

rice

&M

uelle

r19

81)

Dis

trib

utiv

eJu

stic

e(n

otit

le:

Pric

e&

Mue

ller

1981

)P

rom

otio

nalO

ppor

tuni

ty(n

otit

le:

Pric

e&

Mue

ller

1981

)A

bsen

teei

sm(T

ime

Lost

,A

bsen

ceS

pells

,1–

2da

yA

bsen

ceS

pells

)

%st

ayer

orle

aver

rang

e7–

35ra

nge

2–10

rang

e4–

17ra

nge

4–23

degr

eeor

high

erra

nge

2–6

rang

e4–

20ra

nge

4–20

rang

e7–

35N

AR

ange

3–18

Ran

ge8–

40ƒ

IRR

100%

IRR

100%

a=

.87

a=

.85

a=

.75

a=

.84

NA

NA

a=

.82

NR

a=

.90

IRR

100%

a=

.76

a=

.93

NR

All

fact

oran

alyz

edex

cept

last

varia

ble

NR

Cor

rela

tion

M. M. Davey et al.


Tab

le3

Con

tinue

d

Aut

hor(

s)Y

ear

Jour

nal&

Cou

ntry

Sam

ple

Mea

sure

men

t/Ins

trum

ents

Sco

ring

Rel

iabi

lity

Val

idity

Ana

lysi

s

Tau

nton

etal

.199

5,N

urs

Eco

nU

SA

1107

nurs

es4

chur

chho

spita

lsN

urse

Cha

ract

eris

tics

(9di

men

sion

s)W

ork

Invo

lvem

ent

(Kun

ungo

1982

)W

orks

full/

part

time

(<or

>32

hour

s/w

eek)

Hea

lthS

tatu

s(9

sub

scal

es:

War

e,19

76)

Opp

ortu

nity

Els

ewhe

re(P

rice

&M

uelle

r19

86)

Uni

tS

epar

atio

n(s

taye

ror

leav

er)

Mar

italS

tatu

sJo

bD

ecis

ion

Prio

ritie

s,W

ork

Env

ironm

ent

Res

pons

ibili

tyM

anag

erC

hara

cter

istic

s(3

dim

ensi

ons)

Pow

er(2

subs

cale

s:S

herid

an&

Vre

denb

urgh

1978

)In

fluen

ce(2

subs

ales

:M

och

etal

.19

83)

Lead

ersh

ipS

tyle

(2di

men

sion

s:K

ruse

&S

togd

ill19

73)

Org

aniz

atio

nalC

hara

cter

istic

s(5

dim

ensi

ons)

Dis

trib

utiv

eJu

stic

e(P

rice

&M

uelle

r19

81)

Pro

mot

iona

lOpp

ortu

nity

(Pric

e&

Mue

ller

1981

)C

ontr

olov

erN

ursi

ngP

ract

ice

(5su

bsca

les:

Hin

shaw

etal

.19

87)

Pay

Per

mis

sive

ness

Wor

kC

hara

cter

istic

s(4

dim

ensi

ons)

Rou

tiniz

atio

n(P

rice

&M

uelle

r19

86)

Aut

onom

y(H

insh

awet

al.

1987

)In

stru

men

talC

omm

unic

atio

n(P

rice

&M

uelle

r19

86)

Gro

upC

ohes

ion

(HIn

shaw

etal

.19

87)

Job

Str

ess

(no

title

:H

insh

awet

al.

1987

)Jo

bS

atis

fact

ion

(no

title

-8

subs

cale

s:H

insh

awet

al.

1987

)Jo

bIn

volv

emen

t(K

anun

go19

82)

Com

mitm

ent

(Pric

e&

Mue

ller

1986

)In

tent

toS

tay

(Pric

e&

Mue

ller

1981

)A

bsen

teei

sm(f

ailu

reto

repo

rtfo

ra

sche

dule

dda

yof

wor

k-to

tal#

1-da

yab

senc

esov

er6

mon

ths)

NR

NR

NR

NR

NR

NR

NR

NR

NR

ratio

NR

NR

NR

NR

NR

NR

NR

NR

NR

NR

NR

NR

NR

NR

NR

NR

NR

NR

NR

NR

AN

OV

A

Dun

can

post

hoc

Pea

rson

Cor

rela

tion

Zbo

ril-B

enso

n200

2,C

anJ

Nur

sR

esC

anad

a10

79nu

rses

Mul

tiple

Acu

te&

Long

-Ter

mC

are

Abs

ente

eism

(Que

stio

nnai

re)

#of

abse

nces

,ƒ

of&

susc

eptib

ility

toab

sent

eeis

m,

rate

caus

es,

open

ende

dqu

estio

nson

inte

nt-t

o-le

ave

&re

com

men

datio

nsto

redu

ceab

sent

eeis

mJo

bS

atis

fact

ion

(no

title

;re

sear

cher

mad

e)

6ite

ms

5pt

scal

eP

ilote

dP

ilote

dP

ilote

dP

ilote

dC

hiS

quar

e



absenteeism is not an individual-level phenomenon, but

a socially constructed event.

Goldberg and Waldman (2000) amalgamated two

theories by Brooke and Price (1989) and Steers and

Rhodes (1978), to create a conceptual model. In short,

individual predictors (marital status, number of chil-

dren, health, tenure and position level) and situational

predictors (organizational permissiveness, role prob-

lems, pay and job characteristics) influenced both job

satisfaction and absenteeism.

Building on Morgan and Herman�s (1976) earlier

research, Hackett et al. (1989) discussed absenteeism as

a volitional behaviour in that employees choose whe-

ther or not they attend work. Zboril-Benson�s (2002)

followed Nicholson�s (1977) framework, which dis-

cussed attendance at work as a �normal� behaviour.

People attend work regularly, until absence-inducing

advents �interrupt the regularity of attendance� (p. 92).

Nicholson explained this theory using a continuum

from unavoidable to avoidable absences. The theoreti-

cal frameworks are summarized in Table 4.


Using content analysis, a total of 70 independent vari-

ables (potential predictors) were categorized into eight

types of determinants: prior attendance, work attitudes,

retention, burnout and stress, manager characteristics,

human resource management practices, nurse charac-

teristics, and work and job characteristics (Table 5).

�Attendance� was defined as being present at work

when scheduled. Using structural equation modelling,

Gellatly (1995) and Gellatly and Luchak (1998) found

that perceived �absence norm� (what individuals thought

about the unit�s absenteeism rate) and �prior individual

absence� were significantly related to absenteeism. As

absence norms and prior absences increased, so did

absenteeism. However, group level absence was not a

predictor of individual absenteeism. Poor standing,

referring to previous attendance records (Hackett et al.

1989), was significant and positively related to nine out

of 51 nurses� desire to be absent, whereas significant and

negatively related to two out of 51 nurses� desire to be

absent. In addition, actual absenteeism significantly

increased for three out of 22 nurses and significantly

decreased for two out of 22 nurses when poor standing

increased.

�Work attitudes� refer to personal feelings or beliefs

associated with work, such as job and work involve-

ment, organizational commitment, group commitment,

occupational commitment and job satisfaction or dis-

satisfaction. Job satisfaction significantly predicted

absenteeism in three studies (Hackett & Guion 1985,

Taunton et al. 1989, 1995) in that as job satisfaction

increased, absenteeism decreased. Zboril-Benson (2002)

reported that as job dissatisfaction increased, self-re-

ported absenteeism increased.

The direct relationship of job involvement to absen-

teeism was not significant in two studies (Blau 1986,

Taunton et al. 1995); however, Taunton et al.�s study

showed a significant negative relationship of job

involvement and absenteeism if job involvement and

organizational commitment were both high. Cohen

(2000) reported that, as job involvement increased,

absenteeism significantly decreased. Cohen also de-

scribed a significant positive relationship between work

involvement and absenteeism. Job involvement differed

from work involvement in that �job involvement� de-

scribed a general attitude towards an organization as a

whole (Kanungo 1982). �Work involvement� described

how much the job can satisfy present needs (Kanungo

1982).

Five studies discussed the relationship between orga-

nizational commitment and absenteeism (Blau 1986,

Gellatly 1995, Somers 1995, Taunton et al. 1995,

Cohen 2000). Five out of 12 results were significant and

negatively related to absenteeism (Gellatly 1995, Som-

ers 1995, Taunton et al. 1995), indicating that the more

committed one was to the organization, the less one was

absent from work.

�Retention� refers to any type of attitude or behaviour

associated with staying or leaving the job, including

turnover, turnover intentions, intent-to-stay, promo-

tional opportunities and opportunities elsewhere. In

three studies, retention, intent-to-stay and promotional

opportunities were negatively and significantly related

to absenteeism (Taunton et al. 1989, 1995, Boumans &

Landeweerd 1994). Turnover was significantly and

positively related to absenteeism (Taunton et al. 1989).

Overall, retention factors were related to lower nurse

absenteeism.

�Burnout and stress� were grouped together because

the two concepts are closely linked. �Burnout is a syn-

drome of emotional exhaustion and cynicism that

occurs frequently among individuals who do �people

work� of some kind� (Maslach & Jackson 1981, p. 99).

Taunton et al. (1995) described two types of stress:

personal and situational. �Personal job stress� involves

�juggling multiple care expectations of various profes-

sionals as well as clients� (p. 228). �Situational job stress�involves �conflicting values between professional and

bureaucratic demands� (p. 228). One dimension of

burnout (personal accomplishment) was significantly

and negatively related to absenteeism (as personal

M. M. Davey et al.


Table 4Theoretical frameworks

Articles Theoretical framework

Goldberg &Waldman 2000

Brook & Price (1989) developed this model based on the work of Steers and Rhodes. Goldberg and Waldmanused the simplified model (drawn below) which includes only �those variables that were significantly relatedto absenteeism�. (Brooke & Price 1989, p. 12).

Path diagram of the causal model of absenteeism. (Brooke & Price 1989, p. 3).Hackett et al. 1989 Morgan and Herman (1976) conceptualized absenteeism in terms of two categories of consequences - deterrent

and motivating. Deterrent consequences, �can be controlled by the organization and are directly associated with the job(e.g., loss of pay, co-workers having to work harder)�. Motivating consequences �are controlled by the employee and arenot directly associated with the job (e.g., being absent to recover from illness or to have more leisure time)� (Morgan &Herman 1976, p. 738). They hypothesized that �employees will decide whether or not to attend work by weighing thedeterrent and motivating consequences of being absent�. (p. 173).

Zboril-Benson 2002 Nicholson�s (1977) theoretical framework assumes that attendance is a �normal� behavior (Zboril-Benson 2002).Nicholson characterized employee absence on an A–B continuum by identifying the avoidability of absence-inducingevents (Nicholson 1977, p. 231). A-type events are unavoidable (choice cannot influence absence) and B-type eventsare avoidable (employee has control over decision). This theory addresses the role that individual attributions play inabsenteeism (Zboril-Benson 2002, p. 92).

Gellatly 1995,Gellatly & Luchak1998

Nicholson and Johns (1985) developed this framework as a �set of shared understandings about absencelegitimacy…and the established ``custom and practice'' of employee absence behavior and its control�(Johns & Nicholson 1982, p. 136)

Absence culture depends on two factors (Nicholson & Johns 1985) – the level of trust and the salience of the culture tomembers (Gellatly & Luchak 1998, p. 1087).

There are 4 types of absence cultures; Type 1 (low salience & high trust); Type 2 (high salience & high trust); Type 3 (lowsalience & low trust); Type 4 (high salience & low trust).

�It can be hypothesized that more salient cultures have more extreme absence levels (high or low) and less variationamong employees than less salient cultures� (Nicholson & Johns 1985, p. 403).



accomplishment increased, absenteeism decreased).

Taunton et al. (1995) reported that as job stress

increased, absenteeism increased in one out of four

hospitals. Only one study measured workplace stress as

a predictor of nurse absenteeism (Seago 1996). In this

study, decision latitude (a dimension of workplace

stress) was negatively and significantly related to

absenteeism; however, decision latitude was not

defined. Overall, burnout and stress predicted increased

absenteeism.

�Manager characteristics� refer to traits or behaviours

such as leadership, influence and power, used by man-

agers to help guide staff members. Leadership was

examined in two studies. Leaders with high social and

high instrumental leadership or low social and low

instrumental leadership had the lowest rates of

employee absence (Boumans & Landeweerd 1993).

A significant negative relationship was reported – when

leaders showed consideration to employees, employee

absenteeism decreased. One manager characteristic

(influence in personnel resources) was significantly and

negatively related to staff nurse absenteeism in two out

of four hospitals. Overall, these few significant results in

this category suggest that relational leadership practices

of managers may reduce absenteeism.

�Human resource management practices� include

those policies, programmes or managerial activities

endorsed by the organization, such as distributive jus-

tice, pay, permissiveness, overstaffing, staffing ratios,

recruiting sources and control over nursing practice.

Taunton et al. (1995) showed that distributive justice

was significantly and negatively related to absenteeism

in one out of four hospitals. Pay was significantly and

negatively related to absenteeism in one study (Gold-

berg & Waldman 2000) but not in the others (Taunton

et al. 1989, 1995). Organizational permissiveness,

control over nursing practice, graduate nurse-filled full-

time equivalent (FTE) ratio and BSN-filled FTE ratio

were not significant predictors of absenteeism.

Overstaffing was significantly and positively related to

desired (18.9% of sample) and actual absenteeism

(0.5% of sample) (Hackett et al. 1989). As overstaffing

increased, the desire to be absent significantly decreased

for eight out of 10 nurses. Both the part-time staffing

ratio and recruiting sources significantly and negatively

predicted absenteeism. Overall, most results in this

category were not significant and no consistent pattern

was found.

�Nurse characteristics� in single studies were found to

be significantly related to nurse absenteeism: age, ten-

ure, marital status, education, position level, health,

disrupted sleep, doldrums, home responsibilities,

compassionate leave and unit separation. Doldrums

describe �low spirits, emotional and physical fatigue,

Table 4Continued

Articles Theoretical framework

Goldberg &Waldman 2000

Steers and Rhodes� (1978) theoretical framework premises employee attendance on two things – motivationto attend and ability to attend (Steers & Rhodes 1978, p. 392). Factors that affect an employee�s decision and ability tocome to work arise both from the individual (e.g., personal work ethic, demographic factors) and from the workenvironment (e.g., the job situation, incentive/reward systems, work group norms) (1978, p. 401).

Major influences on employee attendance (Steers & Rhodes, 1978).

M. M. Davey et al.


Table 5Predictors of absenteeism by category

Determinant Source

Significantrelationship

with absenteeism Comments

Prior attendancePerceived absence norm Gellatly & Luchak 1998 +

Gellatly 1995 +Prior individual absence Gellatly & Luchak 1998 +Group level absence Gellatly & Luchak 1998 NS

Gellatly 1995 NSPoor standing Hackett et al. 1989 + 9/51 staff (Desire to be absent)

) 2/51 staff (Desire to be absent)NS 40/51 staff (Desire to be absent)+ 3/22 staff (Actual absence)) 2/22 staff (Actual absence)

NS 17/22 staff (Actual absence)Work AttitudesJob satisfaction (JS) Goldberg & Waldman 2000 NS

Hackett & Guion 1985 )Taunton et al. 1989Taunton et al. 1995 ) Organizational JS & Hospitals C B & D

) Organizational JS in Hospital ANS Professional JS in all 4 hospitalsNS

Job dissatisfaction Zboril-Benson 2002 +Job involvement Blau 1986 NS

) Job involvement x CommitmentCohen 2000 ) Direct Model

NS Indirect Model ANS Indirect Model B

Taunton et al. 1995 NSWork involvement Cohen 2000 + Direct Model

NS Indirect Model ANS Indirect Model B

Organizational commitment (OC) Blau 1986 NS-Affective Commitment (AC)-Continuance Commitment (CC) Cohen 2000 NS-Normative Commitment (NC)

Somers 1995 ) AC & annexed absences) AC x CC & annexed absence

NS All subscales of OC & total absencesNS CC NC & AC x NC CC x NC &

annexed absenceGellatly 1995 ) AC & absence frequency (Models A & B2)

NS AC & absence frequency (Model B1)) AC & Total days absent (Model B1)

NS AC & total days absent (Models A & B2)NS CC & absence frequency & total days

absent (Models A B1 & B2)Taunton et al. 1995 ) Hospital B

NS Hospitals A C & DOccupational commitment Cohen 2000 NSGroup commitment Cohen 2000 NSRetention factorsRetention Taunton et al. 1989 )Turnover Taunton et al. 1989 +Turnover Intentions Cohen 2000 NSIntent to Stay Taunton et al. 1989 )

Taunton et al. 1995 NSPromotional Opportunity Taunton et al. 1989 NS

Taunton et al. 1995 )Boumans & Landeweerd 1994 NS Non-ICU nurses

) ICU-Nurses



Table 5Continued

Determinant Source



Opportunity Elsewhere Taunton et al. 1989, 1995 NSBurnout/StressBurnout Parker & Kulik 1995 ) Personal Accomplishment &

Hospital Record Sick LeaveNS Emotional Exhaustion depersonalization

& reported absenteeismJob Stress Taunton et al. 1995 NS

+Workplace Stress Seago 1996 ) Decision Latitude – a component

of workplace stressNS Psychological Demand – a

component of workplace stressManager CharacteristicsLeadership Boumans & Landeweerd 1993 NS Social & Initiating Structure (Leadership)

) Leader with ›social & ›instrumentalor flsocial & flinstrumental

NSBoumans & Landeweerd 1994Taunton et al. 1995 )

NSInfluence Taunton et al. 1995 NS All 4 hospitals (Work coordination

-manager influence variable)) Hospital C & D (Personnel resources

– manager influence variable)Power Taunton et al. 1995 NS Manager position (variable

included in power)NS Manager power

Human Resource Management PracticesDistributive Justice Taunton et al. 1989 NS

Taunton et al. 1995 ) Hospital DNS Hospital A B & C

Pay Goldberg & Waldman 2000 )Taunton et al. 1989, 1995 NS

Organizational Permissiveness Goldberg & Waldman 2000 NSTaunton et al. 1995 NS

Overstaffing Hackett et al. 1989 ) 8/51 staff (Desire to be absent)+ 2/51 staff (Desire to be absent)

NS 41/51 staff (Desire to be absent)+ 1/22 staff (Actual absenteeism)

NS 21/22 staff (Actual absenteeism)Graduate Nurse/ Filled FTE Ratio Taunton et al. 1995 NSBSN/Filled FTE Ratio Taunton et al. 1995 NSPart-Time/Filled FTE Ratio Taunton et al. 1995 ) Hospital C & D

NS Hospital A & BRecruiting Sources Griffeth et al. 1997 )Control Over Nursing Practice Taunton et al. 1995 NSNurse CharacteristicsAge Gellatly 1995 ) Model A B1 & B2 & absence frequency

NS Model A B1 & B2 & total days absentTaunton et al. 1995 NS

Tenure Goldberg & Waldman 2000 NSGellatly 1995 NS Model A B1 & B2 & absence frequency

+ Model A B1 & B2 & total days absentTenure Expectations Taunton et al. 1995 NSMarital Status Goldberg & Waldman 2000 NS

Taunton et al. 1995 ) For single & divorced people &only in Hospital B

NS Hospital A C & D

M. M. Davey et al.


Table 5Continued

Determinant Source



No. Children Goldberg & Waldman 2000 NSNursing Education Taunton et al. 1989 )

Taunton et al. 1995 NSPosition Level Goldberg & Waldman 2000 )Years in Profession Taunton et al. 1995 NSYears in Job Taunton et al. 1995 NSYears in Hospital Taunton et al. 1995 NSHealth Goldberg & Waldman 2000 )

Taunton et al. 1995 NS Current health) Resistance to illness) Rejection of sick role

NS Health outlookNS Sickness OrientationNS Health worry/Concerns) Prior health

NS Attitude toward going to physicianNS Drug use

Disrupted Sleep Hackett et al. 1989 + 2/51 staff (Desire to be absent)) 3/51 staff (Desire to be absent)

NS 47/51 staff (Desire to be absent)+ 5/22 staff (Actual absence)) 2/22 staff (Actual absence)

NS 17/22 staff (Actual absence)Doldrums Hackett et al. 1989 ) 36/51 staff (Desire to be absent)

NS 15/51 staff (Desire to be absent)+ 8/22 staff (Actual absence)

NS 14/22 staff (Actual absence)Home Responsibilities Hackett et al. 1989 + 8/51 staff (Desire to be absent)


NS 15/22 staff (Actual absence)Kinship Responsibility Taunton et al. 1989 NSResponsibility Taunton et al. 1995 NSCompassionate Leave Hackett et al. 1989 + 4/51 staff (Desire to be absent)

) 3/51 staff (Desire to be absent)NS 44/51 staff (Desire to be absent)+ 2/22 staff (Actual absence)

NS 20/22 staff (Actual absence)Hostility Seago 1996 NSWorks Full-time/Part-time Taunton et al. 1995 NSJob Decision Priorities Taunton et al. 1995 NSUnit Separation (stayer/leaver) Taunton et al. 1995 + Hospital D

NS Hospital A B & CWork & Job CharacteristicsRoutinization Taunton et al. 1989, 1995 NSInstrumental Communication Taunton et al. 1989, 1995 NSWork Responsibilities Hackett et al. 1989 + 4/51 staff (Desire to be absent)


NS 19/22 staff (Actual absence)Job Characteristics Goldberg & Waldman 2000 NSRole Ambiguity Goldberg & Waldman 2000 NSRole Conflict Goldberg & Waldman 2000 NSParticipation Taunton et al. 1989 NSSocial Integration Taunton et al. 1989 NS



and feeling down-trodden� (Hackett et al. 1989) How-

ever, in other studies many of these same characteristics

did not significantly predict absenteeism rates. For

example, Gellatly (1995) reported that as age increased,

absenteeism decreased. Taunton et al. (1995) found

that absenteeism was not related to age. Gellatly (1995)

also reported that as tenure increased, total days absent

increased. Goldberg and Waldman (2000) found no

relationship between tenure and absenteeism. Taunton

et al. (1995) found that being single or divorced nega-

tively and significantly predicted nurse absenteeism in

one out of four hospitals. Thus, no consistent pattern

between demographic characteristics and absenteeism

was shown in this review.

Two studies suggested a relationship between nurses�health and their absence rates. Goldberg and Waldman

(2000) reported that health was significantly and neg-

atively related to absenteeism. Taunton et al. (1995)

reported that in three out of four hospitals, as resistance

to illness increased, absenteeism decreased. Rejection of

the sick role and prior health were both significantly

and negatively associated to absenteeism.

�Work and job characteristics� were associated with

the work or job itself, and included routinization,

instrumental communication, work responsibilities, job

characteristics, role ambiguity, role conflict, participa-

tion, social integration, work group culture, group

cohesion, interactional justice, complexity/difficulty,

feedback/clarity, work pressure, autonomy, task allo-

cation and unit workload (see Table 5). The impact of

work responsibilities on absenteeism was equivocal –

some studies reported significant associations (Hackett

et al. 1989, Boumans & Landeweerd 1994, Taunton

et al. 1995), whereas other studies found no significant

associations (Taunton et al. 1989, 1995, Boumans &

Landeweerd 1994, Gellatly 1995, Seago 1996, Gold-

berg & Waldman 2000). This category of predictor

variables was not associated with staff nurse absentee-

ism.

Discussion

In Canada, absenteeism of front-line staff nurses is a

concern for administrators and health care organiza-

tions. CIHI (2005) denotes absenteeism as a current

issue in Canada, yet the majority of nursing and inter-

disciplinary healthcare research on absenteeism was

conducted in the United States. Studying the predictors

of nurse absenteeism in the Canadian context is

important because of differences in the US and Cana-

dian healthcare systems.


Attendance (i.e. previous attendance and perceived

absence norm) was the best predictor of absenteeism,

with greater than 50% significant results. If perceived

absence norms, prior individual absence or poor atten-

dance records have been high, then current absenteeism

may increase, as absence cultures may develop. When

the culture supports poor attendance, future individual

attendance will align itself with cultural norms and

expectations (Nicholson & Johns 1985). This suggests

that it is important for administrators to screen prior

absence behaviour of potential employees and monitor

attendance behaviour of current employees for patterns

in absenteeism. Finding such attendance patterns will

allow administrators and researchers to implement

interventions in collaboration with individuals or

groups to improve absenteeism rates.

The CIHI (2005) reported that nurses rated unit

leadership and personal health as important predictors

Table 5Continued

Determinant Source



Work Group Culture Seago 1996 NSGroup Cohesion Taunton et al. 1995 )Interactional Justice Gellatly 1995 NSComplexity/Difficulty Boumans & Landeweerd 1994 NSFeedback/Clarity Boumans & Landeweerd 1994 NS Non-ICU nurses

) ICU nursesWork Pressure Boumans & Landeweerd 1994 NSAutonomy Boumans & Landeweerd 1994 NS

Taunton et al. 1995 NSTask Allocation Boumans & Landeweerd 1994 NSUnit Workload Taunton et al. 1995 NS


M. M. Davey et al.

of absenteeism, and therefore the results reported in this

review were expected to support this relationship. Yet,

work attitudes, along with characteristics of the man-

ager, organization, work and the individual nurse,

provided inconclusive and mixed results about the

predictors of staff nurse absenteeism. Less than half of

the results within each category were significant. Only

organizational commitment was significantly related to

lower absenteeism in three studies. Organizational

commitment has been shown to be influenced by work

environment factors, such as leadership style and

organizational culture (McNeese-Smith 1995, Zboril-

Benson 2002). This indirect effect may explain why the

majority of the direct relationships between work

environment factors and absenteeism were not signifi-

cant. The lack of theoretical frameworks on absentee-

ism in nursing that specifically address these indirect

effects may also have contributed to the gap between

what nurses report, how studies are designed and what

is actually measured.

It is also possible that methodological errors (sam-

pling technique, retrospective design, self-report data,

unit-of-analysis, etc) in the studies reviewed also

contributed to the non-significant results. Vigorous

research is needed in this area to discover and conclude

whether these characteristics are predictors of absen-

teeism.

Theoretical framework

One of the objectives of doing research is to build and

test theory. A theoretical framework helps guide the

method and intervention(s) appropriately. Initially,

absenteeism was conceptualized to be an individual

phenomenon. Steers and Rhodes� (1978) model

described a variety of factors that influence motivation

and ability to attend. Many researchers have studied the

hypothesized predictor variables in that framework, but

have not found strong relationships in the data. A recent

movement to look at absenteeism from an organiza-

tional perspective began with Chadwick et al. (1971)

and was later developed by Nicholson and Johns (1995).

Absence culture, from a social-networking standpoint, is

beginning to dominate the multidisciplinary world of

absenteeism. This implies that absenteeism may be so-

cially created within groups of people (friends, peers,

colleagues, etc.). From this social-exchange perspective,

absenteeism can be seen to be exchanged for negative

managerial behaviours. A social-exchange framework

suggests that �people enter into relationships to acquire

valuable resources� (Fuller & Hester 2006), which can

be material goods or social goods.

Shamian and El-Jardali (2007) discussed the impor-

tance of creating healthy Canadian workplaces to

reduce organizational outcomes such as absenteeism

and turnover. Theory must be tested and developed in

this area to determine if these variables consistently

predict absenteeism. Once predictors are identified, it

may be easier for administrators to implement inter-

ventions that foster healthy work environments to

promote attendance and reduce absenteeism.

The majority of included studies did not explicitly

follow a theoretical framework focused on absenteeism.

This may have been as a result of inadequate theoriza-

tion of nursing absenteeism as well as its determinants

and consequences. This is reflected in the number of

studies where absenteeism was examined as an ancillary

variable rather than as the primary focus of the study. In

order to advance this area of organizational behaviour,

refinement and development of existing theories are

needed to guide future research and interventions to

reduce absenteeism.

Absenteeism

Self-report, retrospective research designs may not be as

accurate as direct observation or recording owing to

unreliable memory or social desirability forces. Self-

report designs may lead to false–negative or false–posi-

tive results when potential for behavioural judgements

exist, influencing the significance of the relationships

found. In this review, self-report bias may have con-

tributed to type 2 error, leading to a false representation

of the number of non-significant results.

The focus of this review was on reported relationships

between measured predictors and individual absentee-

ism. Additionally, a large body of literature on absen-

teeism among nurses examined the influence of an

unmeasured intervention, such as a programme or

development session, to reduce nurse absenteeism.

These interventions/strategies may influence absentee-

ism and must be considered in future review studies.

This review has several potential limitations.

A reporting bias may exist as only published studies in

English were included and published studies tend to

over-report positive findings. The unit of analysis was

unclear in more than half of the studies, thus affecting

how the results were reported (group or individual) and

specific nurse outcomes may be contaminated with

outcomes from other healthcare professionals or con-

textual factors influencing the sample. We did not

conduct a meta-analysis because of the variation in

independent variables and the heterogeneity of this

group of studies. None of the studies in this review were



conducted in rural hospital settings, making rural con-

texts a current and important area to research.

Based on our results, several recommendations are

provided for healthcare organizations and researchers

interested in staff nurse absenteeism. First, researchers

and healthcare organizations should invest in long-term

partnerships to examine the root causes of nurse

absenteeism using longitudinal prospective designs and

robust theory. This will help both administrators and

researchers to understand possible predictors of absen-

teeism within the organization�s context.

Second, it is clear from this review that the predictors of

short-term nurse absenteeism are still largely unknown.

Well-designed studies, using solid theoretical frame-

works, must be conducted in order to accurately display

the variables that significantly influence absenteeism.

Structural equation modelling techniques (Gellatly 1995,

Griffeth et al. 1997) would provide information about

the causal relationships between specific organizational

and individual level determinants of absenteeism,

including both indirect and direct effects.

Third, many studies on absenteeism (both included

and not included in this review) rely on only self-report

data, or on past absenteeism records. Both of these

methods may increase error in the study results. To

strengthen the use of self-reported absence measures,

these measures can be verified through comparisons

with actual administrative data. Collecting data in real

time increases the accuracy of the results, thus reducing

bias and error (Hackett et al. 1989, Gellatly 1995,

Gellatly & Luchak 1998). Absence behaviour is also

only detected over time. Therefore, repeated longitudi-

nal data collections are necessary to observe trends or

changes in absenteeism behaviour. Developing part-

nerships between organizations and researchers may

give rise to analysis of anonymized prospective per-

sonnel absenteeism data without violating the privacy

of individual nurses. Analysing group absence by

aggregating individual measures would also provide

useful correlates with absence cultures and be less

threatening to individual nurses.

Fourth, random sampling increases the generaliz-

ability of the results and reduces convenient and

voluntary sampling bias. Random sampling was a

limitation in the majority of the papers included in this

review. It is difficult to use prospective sampling within

hospitals because random selection immediately reduces

the sample size; and, as the sample population is already

small, the effect size is limited. However, sufficient

capacity to conduct such research designs can be cre-

ated through partnerships between researchers and

large health regions/organizations.

Conclusion

In this systematic review, we examined and integrated

70 independent variables from 14 different studies into

eight predictive categories of absenteeism. Our findings

show trends toward individual nurses� prior attendance

records, work attitudes (job satisfaction, organizational

commitment and work/job involvement) and retention

factors reducing nurse absenteeism, with burnout and

job stress increasing absenteeism. However, our find-

ings suggest that research on hospital nurse absenteeism

provides no conclusive evidence about the predictors of

staff nurse absenteeism. As long as the predictors

remain unclear, efforts at dealing with the costly

problems associated with absenteeism will be less

fruitful. Future research should be conducted in Cana-

dian contexts to provide Canadian healthcare organi-

zations with generalizable information, to use

prospective and longitudinal designs and to be theory

driven, using or developing robust absenteeism theory.

Partnerships between researchers and Canadian

healthcare organizations and predictive theory about

the determinants of absenteeism in nursing would guide

the development of individual and organizational

interventions to address this costly issue for individuals

and organizations in order to improve the continuity

and quality of care for patients.

Acknowledgement

Peter Davey for providing much needed support throughoutthis review and assistance as a second reviewer.

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Predictors of nurse absenteeism in hospitals: a systematic review

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