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https://doi.org/10.1177/01939459241236631
Western Journal of Nursing Research
2024, Vol. 46(4) 288 –295
© The Author(s) 2024
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DOI: 10.1177/01939459241236631
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Research Report
“Do no harm” is one of the main ethical principles guiding
the clinical practice for nurses and all health care profession-
als. However, medical errors are ranked as the third leading
cause of death in the United States, and medication errors are
among the most frequently occurring errors.1,2 These find-
ings suggest many healthcare professionals, including
nurses, are failing to maintain the “do no harm” promise to
their profession and patients. Nurses function at the point of
care and assume a major role in intercepting errors before
reaching their patients.3 To perform this role effectively,
nurses must be well-rested and alert. However, being rested
and alert is challenging due to the nursing shortage, which
has greatly increased since the novel coronavirus disease
2019 (COVID-19) pandemic.4 As of 2022, the nursing
vacancy rate stood at 17%, representing a 7.1-point increase
from the previous year.4 To maintain 24/7 nursing care, most
hospitals will continue relying on extended work hours,
12-hour shifts, rotating shifts, and even mandatory overtime
in the near future. It is anticipated that these ongoing extended
work requirements will have a negative impact on nurses’
fatigue and alertness, and may potentially affect patient
safety outcomes, such as medication errors and near misses.
However, few studies have evaluated these relationships.
Fatigue and Alertness Among Nurses
The terms fatigue and sleepiness are sometimes used inter-
changeably. Although the 2 are related and could co-occur,
1236631WJNXXX10.1177/01939459241236631Western Journal of Nursing Research</italic>Farag et al.
research-article2024
1College of Nursing, The University of Iowa, Iowa City, IA, USA
2Department of Health and Human Physiology, The University of Iowa,
Iowa City, IA, USA
Corresponding Author:
Amany Farag, College of Nursing, The University of Iowa, 444 College of
Nursing Building, 50 Newton Rd., Iowa City, IA 52242, USA.
Email: Amany-farag@uiowa.edu
Examining the Relationship Between
Nurse Fatigue, Alertness, and Medication
Errors
Amany Farag1, Jacob Gallagher2, and Lucas Carr2
Abstract
Background: Working for extended hours in a physically and mentally demanding profession has subjected nurses to
occupational fatigue. Limited evidence exists about nurse fatigue and alertness changes throughout shift work and their
relationship with medication errors and near misses.
Purpose: The purposes of this study were to: (1) assess the relationship between nurses’ fatigue and alertness, (2) evaluate
nurses’ fatigue and alertness changes throughout their shift, and (3) examine the relationship between nurses’ fatigue,
alertness, and medication errors and near misses.
Methods: This prospective study is part of a larger mixed-method study. Fatigue and alertness data from 14 work and non-
workdays were collected from a convenience sample of 90 nurses. A wearable actigraph (ReadibandTM) was used to measure
alertness, while ecological momentary assessment (EMA) using text messaging was used to measure nurses’ fatigue.
Results: A 1-unit increase in fatigue was associated with a 1.06-unit reduction in nurses’ alertness score (β = –1.06, 95%
CI: [–1.33, –0.78], p < .01). Night-shift nurses experienced a 31-point reduction in alertness from the start to the end of
the work shift. Nurses’ fatigue, but not alertness, was associated with medication errors and near misses (OR = 1.26, 95%
CI [1.07, 1.48], p = .01).
Conclusion: Initiating fatigue mitigation measures during mid-shift, especially for night-shift nurses, may be a viable option to
mitigate fatigue and alertness deterioration among nurses and to maintain patient safety. The multifaceted nature of fatigue,
as captured by EMA, is a stronger predictor of medication errors and near misses than device-measured alertness.
Keywords
nurses, fatigue, medical errors, nurse fatigue, nurses’ alertness, medication error, ecological momentary assessment
Farag et al. 289
they represent different constructs.5 Occupational fatigue is
defined as a general sense of tiredness and exhaustion that is
intensified by excessive work demands and inadequate
recovery.5,6 Fatigue can be classified based on its nature as
physical, mental, or global, and based on its duration into
acute or chronic.5-9 In this study, we used self-rated global
fatigue, defined as a general feeling of diminished energy to
perform the required tasks resulting from prolonged physical
and mental fatigue.8 As we move forward, we will use the
term fatigue to refer to occupational fatigue. Sleepiness, on
the other hand, is defined as the tendency to fall asleep.5
Regardless of the conceptual difference, poor sleep quality is
among the most common causes of nurse fatigue, decreased
alertness, and sleepiness.5,10-16 The relationship between
sleep and fatigue is supported by multiple studies, including
our previous work.10-16 However, there are limited studies
conducted to evaluate the relationship between fatigue and
alertness.
Nurse fatigue has been examined in many studies.
Working in a mentally and physically demanding profes-
sion and complex work environment has subjected nurses
to extreme fatigue.17-20 Multiple studies have evaluated
predictors of nurse fatigue and its relationship with various
nurse and patient outcomes. These studies have consis-
tently linked nurse fatigue to a range of negative outcomes,
such as drowsy driving,21-23 decision regret,13 medication
errors,5 turnover intentions,24 needlestick injuries,25 sick-
ness absences,26,27 and nurses’ mental health.28
Although scholars have proposed that fatigue may dimin-
ish nurses’ alertness,8,10,14-16 empirical evidence exploring
this relationship remains limited. One notable study by Min
and colleagues16 used wearable wrist actigraphy (Readiband)
to measure alertness, and ecological momentary assessment
(EMA) to measure fatigue at the beginning and end of shift
work. The study findings revealed a significant correlation
between fatigue at the beginning of the day shift and the sub-
sequent decline in alertness.16 This study, however, was con-
ducted in Korea where nurses commonly work rotating
8-hour shifts, and did not capture fatigue variation through-
out the shift.
Despite the breadth of research on nurse fatigue, a com-
prehensive understanding of how fatigue and alertness fluc-
tuate during nurses’ shifts is still lacking. Furthermore, few
studies have evaluated the relationship between fatigue and
medication errors,5 and the relationship between fatigue,
alertness, medication errors, and near misses has not been
thoroughly explored. This gap in the literature highlights the
need for more studies to understand these relationships.
Purpose
A recent systematic review examining the relationship
between nurse fatigue and patient outcomes identified a
limited number of longitudinal studies that evaluated pat-
terns of nurse fatigue and its consequences.19 Thus, more
longitudinal or prospective studies are needed to identify
patterns of nurse fatigue and alertness throughout the shift
to add knowledge about fatigue. Improved understanding in
this area could provide some insights about the appropriate
time to deliver fatigue mitigation interventions (eg, taking
a break). Therefore, the purposes of this study were to: (1)
assess the relationship between nurses’ fatigue and alert-
ness, (2) evaluate changes in nurses’ fatigue and alertness
throughout their shift work, and (3) examine the relation-
ship between nurses’ fatigue, alertness, and medication
errors and near misses.
Methods
Design, Setting, and Sample
This prospective observational study was part of a larger
mixed-method study. The parent study aimed to evaluate
predictors of nurse fatigue and examine if inter-shift recov-
ery moderates the relationship between nurses’ fatigue and
patient safety outcomes (medication errors and near misses).
Data for the parent study were collected from 8 conveniently
selected sites in the Midwest. All sites were located within a
120-mile radius of the principal investigators’ (PI) academic
institution. The parent study included 3 consecutive phases;
this article reports on one arm of the study’s second phase.
The sample for the selected arm consisted of 111 nurses
working in 7 randomly selected general, critical care, emer-
gency department, and behavioral health units, all from one
academic medical center that was part of the parent study. The
units were strategically selected to reflect the various special-
ties within the study site. Because some participants were
missing some text messaging or wearable data, the final sam-
ple consisted of 90 participants. Nurse managers, advanced
practice nurses, agency nurses, and float pool nurses were
excluded from the study. A full description of the parent study
setting, and sample are described elsewhere.12
Measures
Ecological momentary assessment using text messaging was
used to measure nurses’ self-rated fatigue. Ecological mo-
mentary assessment involves collecting data over multiple
time points to capture behaviors, emotions, and experiences
from participants while in their naturalistic environment,
such as at home or work setting.29,30 This method of repeat-
edly collecting data in real-time minimizes participants’
recall bias and allows researchers to effectively discern how
context influences the behavior under study.29,30 Ecological
momentary assessment has been used successfully to collect
behavioral health data (eg, smoking, drinking, stress, physi-
cal activities, eating behaviors) from nurses, physicians,
patients, and healthy individuals.31-36 The frequency, dura-
tion, and time interval between EMA data collection points
are selected by the investigator, based on feasibility and
potential subject burden.29,30
290 Western Journal of Nursing Research 46(4)
To avoid multiple interruptions to the nurses’ workflow,
the PI decided to collect data 4 times every day over a 14-day
period during work and non-workday days. The first text was
sent 30 minutes before the beginning of the shift, the last text
was sent 30 minutes after the end of the shift, and the middle
2 texts were sent at equal intervals during the shift. The lead-
ership group of the participating units approved the proposed
plan. The texting platform was designed and built for the
study by collaborators at the College of Engineering. Before
starting the data collection, the study PI and 4 research assis-
tants enrolled as subjects in the texting platform and tested
the system for a month. Each text prompted nurses to rate
their fatigue using a 11-point Likert-type scale. The scale
ranged from not fatigued at all (0) to extremely fatigued (10).
In addition, participants received 2 texts at the end of their
workdays to indicate if they had a medication error or near
miss. Participants answered using a dichotomous scale of yes
(1) or no (0) to indicate their answers.
A commercially available actigraphy wearable device,
Fatigue Science ReadiBandTM (Fatigue Science, Vancouver,
Canada; fatiguescience.com), was used to measure nurses’
alertness. The ReadiBandTM captures 5 sleep and awake
parameters: (1) minutes in bed, (2) minutes asleep, (3) sleep
latency, (4) sleep efficiency, and (5) awake after sleep onset.
These parameters were processed by the Sleep, Activity,
Fatigue, and Task Effectiveness (SAFTETM) biomathematical
model to yield a predicted alertness score. The SAFTE© model
and the ReadiBandTM have been validated in laboratory set-
tings through controlled trials.37-39 The ReadiBand has an
overall accuracy rate of 93% compared with another common
method, polysomnography.39 The predicted alertness scores
range from 0 to 100, with high scores indicating good alert-
ness. Alertness scores of 80 and above indicate a low risk of
error.37-39 Alertness scores of 70% to 80% indicate an elevated
risk of accidents/errors, with a decline in reaction time; and
scores below 70% indicate a very high risk of accidents/errors
with a significant decline in reaction time.37-39
Procedures
After obtaining the required University of Iowa Institutional
Review Board’s (IRB# 201503758) approval, the study PI
attended all the monthly staff meetings for all the participat-
ing units. Nurses were informed that their participation in the
study was voluntary and that they could withdraw from the
study at any point. The PI’s Institutional Review Board
approved the request to waive the informed consent signa-
ture. After the study introduction meetings, the PI distributed
the study packages to the nurses' mailboxes. The packages
contained the survey for the first phase of the study, a cover
letter detailing elements of the informed consent, an invita-
tion to participate in the second phase of the study, and a
pre-stamped return envelope. To encourage participation and
improve the response rate, weekly reminder flyers were dis-
tributed in the participating units over a 3-week period.
Nurses who completed the study survey and were inter-
ested in the second phase of the study completed the invita-
tion form and included their phone number and a convenient
time to call. One of the study research team members called
all participants to obtain their 14-day schedule, entered the
schedule into a texting platform, and then scheduled a time
to deliver the ReadiBandTM. At the agreed-upon time, the
research team member met with each study participant,
delivered the ReadiBandTM, answered any questions, and
asked about a good date and time to collect the ReadibandTM.
Because the SAFTE© model requires 72-hour sleep data to
calculate the alertness score, the participants were instructed
to wear the ReadiBandTM on their non-dominant wrists
starting 3 days before receiving the first fatigue text mes-
sage. After 17 days of wearing the ReadibandTM (including
14 days of receiving the text messages and wearing the
ReadibandTM), one of the research team members met with
each participant to collect the ReadibandTM. After complet-
ing the data collection, each participant received a $90
compensation check and a $10 bonus if they responded to
at least 75% of the fatigue text messages. We achieved an
approximately 95% text messaging completion rate using
the described compensation approach.
Statistical Analysis
R Studio Version 4.2.2 software (R Core Team)40 was used to
analyze the study data. To assess the relationship between
nurses’ fatigue and alertness (aim 1), a repeated measures
correlation was used to analyze the relationship between
self-reported fatigue (0-10) assessed by EMA and alertness
reported by the ReadibandTM. The ReadibandTM reports an
alertness score every minute. To compare the 4 measures of
self-reported fatigue with alertness, the minute-by-minute
data at the time of self-reported and rolling alertness aver-
ages from the previous 5 minutes, 1 hour, and 4 hours were
also compared with the participants’ self-reported fatigue at
those time points. A mixed linear model was used to quantify
the relationship between alertness and fatigue scores with a
random intercept and slope to account for the repeated mea-
sures. Since the rolling averages were highly correlated with
the minute-by-minute alertness score, only the minute-by-
minute alertness score (ie, the alertness score at the time the
self-reported fatigue score was collected) was used in the
mixed linear model.
To evaluate changes in nurses’ fatigue and alertness
throughout their shift work (aim 2), a mixed-model control-
ling for repeated measures was used to compare changes in
the alertness scores and self-reported fatigue throughout each
shift. Sex, age, marital status, education, and having a child at
home were controlled for as covariates. The type of shift (day
or night) was included in the model. An interaction term was
included to examine how changes throughout a shift might
vary between day and night shifts. Shifts starting at 7 pm, 11
pm, 5 pm, 3 pm, and 2 pm were coded as night shifts. Our
Farag et al. 291
analyses were conducted using 1207 and 840 observations
from day and night shifts, respectively.
To examine the relationship between nurses’ fatigue,
alertness, and medication errors, and near misses (aim 3), 2
univariable mixed logistic models were used to determine
the relationship between having a medication error or near
miss and alertness or fatigue. Medication errors and near
misses were coded as a dichotomous outcome (Yes/No), and
the minute-by-minute alertness scores (model 1) or fatigue
scores (model 2) were the independent predictors. The odds
ratio and confidence intervals were calculated from the
resulting model.
Results
Sample Description
Of the 90 participants, the majority were female (90%) with
a mean age of 30.6 (SD: 10.0) years old. On average, the
participants had 6.2 years of nursing experience (SD: 7.7)
and 3.8 years of experience in their unit (SD: 5.4). In terms of
education, 78% of the nurses had a bachelor’s degree, fol-
lowed by a master’s degree (10%), and an associate degree
(11%). Regarding the type of unit, the largest percentage of
participants worked in critical care (41%), followed by pedi-
atrics (35%), with the smallest percentage of participants
(3%) working in medical-surgical units. Slightly more than
half of the participants (54.9%) started their shift at 7 am,
followed by 34.3% who started at 7 pm. Most nurses (83.3%)
worked a 12-hour shift, with a smaller proportion of nurses
(12.3%) working an 8-hour shift (Table 1).
Relationship Between Nurses’ Self-Rated Fatigue
and Objective Alertness Scores
To examine the relationship between EMA self-reported
fatigue and the alertness score, we matched 4295 observa-
tions (including work and non-workdays) of concurrent
EMA self-reported fatigue and ReadibandTM alertness scores
among the 90 individual nurses. The EMA self-reported
fatigue score was inversely associated with the correspond-
ing minute alertness score (rrm = –0.28, 95% CI: [–0.31,
–0.25], p < .001). A significant relationship was also
observed between fatigue and alertness in the mixed linear
model (β = –1.06, 95% CI: [–1.33, –0.78], p < .001), such
that, a 1-unit increase in fatigue was associated with a 1.06-
unit reduction in nurses’ alertness score.
Change in Nurses’ Alertness Throughout the Shift
Work
We collected 2047 observations from 90 participants who
worked day or night shifts. For alertness, a significant
interaction effect across the work shift was observed
among night-shift nurses (β = –7.86, 95% CI: [–8.45,
–7.22], p < .001) but not among day-shift nurses (β =
–0.12, 95% CI: [–0.32, 0.55], p = .55). The beginning of
shift alertness scores were 16 points higher among night-
shift nurses compared with day-shift nurses (β = 16.31,
95% CI: [14.5, 17.9], p < .001). Night-shift nurses expe-
rienced a 31-point reduction in alertness scores from the
start to the end of the work shift, which was not observed
among day-shift nurses (Figure 1).
Change in Nurses’ Fatigue Throughout the Shift
Work
We observed a significant main effect for time (β = 0.37,
95% CI [0.23, 0.50], p < .001) and an interaction between
shift and time (β = 0.37, 95% CI [0.19, 0.55], p < .001).
Specifically, fatigue scores increased +1.48 points over the
course of a given shift among day-shift nurses and +2.96
Table 1. Nurses’ Demographics and Shift Characteristics
(N = 90).
Variable Mean (SD)n (%)
Age 30.6 (10.0)
Years of experience in nursing 6.2 (7.7)
Years of experience in the unit 3.8 (5.4)
Gender
Female 81(90)
Male 8 (8.9)
Marital statusa
Single 40 (44.4)
Married 30 (33.3)
Live with a partner 12 (13.3)
Divorced 8 (8.9)
Highest nursing educationa
Associate 11 (11.2)
Bachelor 70 (77.8)
Masters’ and higher 9 (10)
Do you have children
Yes 27 (30.0)
No 63 (70.0)
Type of unita
Medical surgical 3 (3.3)
Critical care 35 (38.9)
Pediatrics 32 (35.6)
Emergency department 11 (12.2)
Other units 9 (10.0)
Shift start timea
7:00 284 (54.3)
19:00 184 (35.2)
15:00 15 (2.9)
Shift lengtha
12 hours 438 (83.8)
8 hours 63 (12.0)
Abbreviation: SD: standard deviation.
aOnly values with high frequency were reported.
292 Western Journal of Nursing Research 46(4)
points among night-shift nurses. Compared with day-shift
nurses, night-shift nurses reported lower fatigue scores at the
beginning of their shift (β = –0.56, 95% CI [–1.06, –0.08], p
= .009), but the greater increase in fatigue during the night
shift mitigated this difference by the second time point
(Figure 2).
Nurses’ shift length (8, 10, or 12 hours) had no significant
relationship with either fatigue or alertness. However, the
timing of the shift (AM vs PM) was found to be associated
with both alertness and fatigue, but this result should be
interpreted with caution because we did not have an adequate
representation of the different shift lengths.
Predicting Near Misses and Medical Errors
A total of 88 of 90 participants provided complete EMA data
for medication errors and near misses. This included 455
shifts in which nurses reported having 17 near misses and 5
medication errors. Because of the small number of reported
errors, we combined medication errors and near misses. In
univariable mixed effect logistic models, the risk of near
misses or medication errors was not associated with alertness
(OR = 1.00, 95% CI [0.99, 1.00], p = .65) but was positively
associated with self-reported fatigue (OR = 1.26, 95% CI
[1.07, 1.48], p = .01). For every 1-unit increase in self-
reported fatigue (eg, 2-3 of 10), there was a 25% increase in
the odds of medication errors and near misses.
Discussion
This study aimed to assess the relationship between nurses’
fatigue and alertness, evaluate changes in fatigue and alert-
ness throughout nurses’ shift work, and examine the relation-
ship between nurses’ fatigue, alertness, and medication errors
and near misses. To the best of our knowledge, this study is
the first to evaluate the relationship between nurses’ fatigue
and alertness and to measure fatigue and alertness concur-
rently. While most studies on nurse fatigue have proposed
that fatigued nurses have compromised alertness as mani-
fested by a slow reaction time, this relationship has not been
adequately examined. The findings of our study support the
proposed relationship between fatigue and alertness, such
that increased fatigue among nurses was associated with a
decline in alertness.
Our study also concurrently assessed fatigue and alertness
multiple times throughout all shifts, which provides a rigor-
ous estimation of the relationship between fatigue and alert-
ness. Although we did not find a similar study to help
interpret this result, this result may be supported by previous
studies showing a relationship between nurses’ fatigue, espe-
cially after the night shift, car accidents and near accidents,
injuries, and decision regret.13,41 We can presume that these
negative outcomes could be attributed to the nurses’ dimin-
ished attention. A decline in nurses’ attention at the end of the
shift has been supported in previous work.42-44
Although night-shift nurses started their shift with higher
alertness and less fatigue than day-shift nurses, their alert-
ness declined, and their fatigue increased significantly over
the course of the shift. These changes in fatigue and alert-
ness among night-shift nurses were not observed in day-
shift nurses. This result could be because night-shift nurses
managed to get a longer night’s sleep and some rest before
starting their shift, unlike day-shift nurses who must wake
up early to go to work. This interpretation is supported by
the work of Ganesan and colleagues in 2019, who noted that
compared with day-shift nurses, night-shift nurses had lon-
ger sleep hours, particularly before their first night shift.41
However, as the night shift progressed, the nurses experi-
enced increased fatigue and a deterioration in their alertness
levels; this result was not evident among day-shift nurses.
This result is aligned with previous work showing high
mean fatigue scores among night-shift nurses,10,13,14 which
could be attributed to being awake for a long time and work-
ing against their circadian rhythm. Furthermore, the night
shift usually has fewer patient care activities, so that, nurses
are more sedentary, which could decrease alertness.42 In
Figure 1. Nurses’ alertness scores (measured by the Readiband)
during the day and night shifts.
Figure 2. Nurses’ self-reported fatigue during the day and night
shifts.
Farag et al. 293
contrast, most active patient care activities and rounds occur
during the day shift,42 so that, nurses are more active and
mentally engaged, which could increase their alertness. The
decline in nurses’ alertness during the night shift has also
been reported in other studies.41,43
Despite the significant inverse relationship between fa-
tigue and alertness, nurse fatigue, but not alertness, was sig-
nificantly associated with medication errors and near misses.
This counterintuitive result could be attributed to the multi-
faceted nature of fatigue.5-10 Nurses’ alertness is a mental
state, and the alertness score was calculated using sleep met-
rics. In contrast to the alertness score, when nurses self-
reported their fatigue level, they responded to their perceived
overall feeling, including their physical and mental fatigue.
Thus, their fatigue score captured various aspects of fatigue.
Thus, we could infer that medication errors and near misses
are not only attributed to nurses’ alertness level but also to
the nurses’ overall physical and mental state. From another
perspective, fatigue was measured using a subjective self-
report measure, so that, some nurses may have overestimated
or underestimated their fatigue level. The absence of a rela-
tionship between nurses’ alertness and errors was noted in an
earlier work where there was no association between nurses’
level of vigilance and increased risk of errors.44
Overall, these findings may suggest that self-reported
measures of fatigue may be more valid measures to evaluate
the relationship between fatigue and medication errors than
wearable objective measures. While future research is needed
to confirm this finding, our findings suggest using EMA to
measure nurses’ self-reported fatigue at work appears to be a
feasible, acceptable, and useful strategy to inform interven-
tions designed to prevent fatigue-induced medication errors.
Because fatigue is measured in real-time, it has the added
advantage of avoiding recall bias that could be associated
with the frequently used fatigue measures. Future studies
testing optimal fatigue prevention intervention among nurses
are needed.
Strengths and Limitations
There are a few limitations to report. This study was con-
ducted in one Midwestern hospital with a small sample size,
so that, we cannot generalize the study results. It is possible
that medication errors and near misses were underreported,
which is consistent with prior studies and our previous work.45
The underreporting of medication errors and near misses lim-
its our ability to confirm the relationship between medication
errors and fatigue. Fatigue was measured using a self-report
measure. However, using EMA to measure fatigue over time
could account for possible recall bias; and many fatigue stud-
ies have used daily logs and self-report measures to evaluate
nurses’ fatigue. Despite these limitations, this study is one of
few studies to (1) use an objective measure to capture nurse
alertness, (2) use EMA to evaluate nurse fatigue at multiple
points throughout the shift, and (3) evaluate the relationship
between fatigue and alertness. Another strength is that we col-
lected medication error data at the end of the shift rather than
asking nurses to recall if they had an error during the previous
day or week. This approach accounts for possible recall bias.
Although the final sample consisted of 90 participants, this
number is slightly larger than previous studies that have eval-
uated nurse alertness using wearable devices.
Conclusion and Implications for Future Research
Nurse fatigue has long been considered an important issue.
However, with the ongoing nursing shortage, this issue is
prevalent and is likely to continue in the near and possibly
long-term future. Consistent with earlier work linking nurse
fatigue to negative patient outcomes, the results of this study
supported the relationship between nurse fatigue and medi-
cation errors and near misses. New to the literature is the
empirical evidence supporting the significant relationship
between nurse fatigue and the decline in nurses’ alertness.
However, the counterintuitive relationship between nurses’
alertness and medication errors and near misses requires fur-
ther investigation.
Declaration of Conflicting Interests
The author(s) declared no potential conflicts of interest with respect
to the research, authorship, and/or publication of this article.
Funding
The author(s) disclosed receipt of the following financial support
for the research, authorship, and/or publication of this article: The
study was funded by a grant from the National Council State Board
of Nursing (R91013) and the Healthier Workforce Center of the
Midwest (HWCM). HWCM is supported by cooperative agreement
no. U19OH008868 from the Centers for Disease Control and
Prevention (CDC)/National Institute for Occupational Safety and
Health (NIOSH).
ORCID iD
Amany Farag https://orcid.org/0000-0001-9324-1097
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