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sleep and stress

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Rise and Fall of Sleep Quantity and Quality With Student Experiences Across the First Year of University
Nancy L. Galambos and Andrea L. Howard

Jennifer L. Maggs

University of Alberta

The Pennsylvania State University

Covariations of self-reported sleep quantity (duration) and quality (disturbances) with affective, stressful, academic, and social experiences across the first year of university in 187 Canadian students (M age 5 18.4) were examined with multilevel models. Female students reported sleeping fewer hours on average than did male students. In months when negative affect and general levels of stress were higher, sleep quantity was lower. Poorer sleep quality was seen in students living away from home and reporting more financial stress at baseline. In addition, sleep quality was poorer in months when negative affect and general levels of stress were higher (attenuating the effect of financial stress) and better in months when students spent more days with friends. Three themes are presented to explore the mechanisms by which sleep quantity and quality rise and fall in tandem with experiences of the first year of university.

Poor sleep quantity (e.g., shorter sleep times) and
quality (e.g., waking up in the night) are related to
lower academic performance among adolescents of
all ages (Wolfson & Carskadon, 2003). At no other
time is this relation more relevant than when adolescents enter their first year of university and face new and more intense academic demands at the
same time as they may experience changes in family
and peer relations, finances, and living arrangements. Indeed, up to 70% of university students have regular sleep difficulties (Buboltz, Brown, & Soper,
2001), and first-year students have greater odds of
being poor sleepers than second-year students (Suen,
Hon, & Tam, 2008). Partial sleep deprivation (e.g.,
o5 hours sleep in 24 hours, Pilcher & Huffcutt, 1996)
and delayed sleep phase syndrome (i.e., difficulty in
getting to sleep and waking up at normal times; F. C.
Brown, Soper, & Buboltz, 2001) are not uncommon
among university students. The practice of pulling
an ‘‘all nighter’’ is associated with lower grade point averages (GPAs; Thacher, 2008), and less sleep and
lower quality sleep are associated with poorer mental health in university students (Buboltz et al., 2006). To learn more about sleep as it occurs naturally
during a demanding transitional period, we observed how ebbs and flows in sleep quantity and quality are accompanied by affective, stressful, academic, and social experiences as students negotiate their way through the first year of university.

In a recent study of students in their first semester
of university, day-to-day covariations of sleep
This research was supported by a Social Sciences and Humanities Research Council of Canada grant to N. Galambos and J. Maggs. Requests for reprints should be sent to Nancy L. Galambos,
Department of Psychology, University of Alberta, Edmonton, AB, Canada T6G 2E9. E-mail:

quantity and quality with other experiences (affect,
stress, academic demands, and social events) were
observed over 2 weeks (Galambos, Dalton, & Maggs,
2009). Higher sleep quantity (sleeping longer) was
associated with decreased negative affect and spending more time on schoolwork the next day. Higher sleep quality on a given night was related to higher
positive affect, lower negative affect, and lower stress
the next day. In addition, several daily experiences
predicted variation in students’ sleep. For example,
drinking alcohol that day and expecting to take a test
the next day predicted lower sleep quantity that night,
and higher positive affect during the day predicted
better sleep quality that night. These results were
similar to those of a daily experience study of ninthgrade students (Fuligni & Hardway, 2006). Less sleep at night was associated with higher anxiety, depressed
mood, and fatigue the next day, whereas more time
studying and higher stress during the day predicted
less sleep that night. These rare repeated-measures
studies of naturally occurring behaviors show that on
a day-to-day basis the extent to which students are
sleeping well may carry over into their academic and
psychological functioning and vice versa.
Daily experience studies are useful for learning
about relations between variables as they co-occur in
the short term, but leave open questions about similar
relations over the longer term. The first year of college
or university in particular is an important transitional
time in which adjusting to a new environment may
interfere with sleep, impacting academic performance
and well-being (Buboltz et al., 2006). To our know-

r 2010 The Authors
Journal of Research on Adolescence r 2010 Society for Research on Adolescence DOI: 10.1111/j.1532-7795.2010.00679.x


ledge, no study has examined covariations between
sleep and student experience in multiple assessments
across the first year of university, yet such a study is
important for understanding how sleep is related to
behavior and well-being during this period.
In the current study, modeled after the daily experience study of Ga-lambos et al. (2009), we followed 187 university students across five occasions from October to April of their first year. Given that

Galambos and colleagues found relations of affect,
stress, academic demands, and social experiences
with sleep on a daily level, the current study also
asked for reports of sleep, affect, stress, academic
effort, and social behavior, but these reports were
designed to cover a broader span of time. In the
original daily study, between-persons factors such as
gender and living situation were not related to average sleep patterns (Galambos and colleagues), but they may be more influential in explaining variations
in sleep across the longer period of one academic
year. In this study we asked: (1) To what extent do
positive and negative affect, stress, academic effort,
and social experiences (i.e., spending time with
friends and alcohol use) covary with sleep quantity
and quality across months? (2) Do gender, living
situation, social support, financial stress, and high
school GPA predict average levels of sleep quantity
and quality across the first year of university?
Participants were 187 full-time first year students at a
large Canadian university participating in Making the
Transition II, a web-based study of health behaviors and
academic performance. Mean age at study outset was
18.4 years (SD 5 .44, range 5 17.3 – 19.8). Ethnicity was
72% White, 13% Asian, 6% mixed, 3% Indo-Canadian
(originating from India), and 5% another visible minority (e.g., Black, Arabic). Half (52%) lived with parents, 28% in campus residences, 14% in an apartment alone or with roommates, and 5% with relatives. Most

students (85%) lived in two-parent homes while growing up, and the majority of students’ mothers (73%) and fathers (74%) had completed college (i.e., 2-year
degree) or university (i.e., 4-year degree or higher).
Comparative data suggest that the sample is a good
cross section of first-year students in the university.
In fall 2005, 198 participants were recruited from
compulsory first-year English classes serving most


undergraduate students; Engineering students were
recruited separately due to different English requirements. Interested students meeting the criteria (full-time, first year in any college or university,
under age 20) attended an initial group session at
which they completed consent forms and pen-andpaper baseline questionnaires. Beginning in October 2005, participants completed up to seven monthly,
web-based questionnaires for which they were
offered CDN$10 each. An additional CDN$5 was
offered for completing the final questionnaire in
April, 2006. Each questionnaire was made available
to participants for 1 week at the beginning of each
month; questionnaires could be completed anytime
during that week. Data collection did not take place
during scheduled midterm or final exam periods.
Retention was high, with 88% of the 198 participants
completing at least four questionnaires. Sleep measures were administered in October, November, December, February, and April; therefore, analyses for the current study relied on data from these months.

Ten of the original participants were excluded from
analysis due to missing sleep measures on all occasions, and one was excluded due to multiple missing between-persons predictors at baseline, resulting in
the final sample of 187. Six missing data points for
between-persons predictors (four cases in which one
baseline predictor was missing; one case in which
two predictors were missing) were replaced using a
regression-based estimation procedure. Recruitment
of the sample continued past administration of the
October web-based questionnaire, resulting in an n
of 100 for October.
Sleep quantity and quality. The Pittsburgh Sleep
Quality Index (PSQI; Buysse, Reynolds, Monk,
Berman, & Kupfer, 1989) is a widely used measure
of subjective sleep experience in the previous month,
consisting of seven subscales (components) and
a global score. Scores on two components were
selected for the current study. Sleep quantity (or
duration) was measured by asking ‘‘During the past
month, how many hours of actual sleep did you get at
night? (This may be different than the number of
hours you spend in bed.)’’ The range in mean sleep
duration across waves was small (M 5 6.66, SD 5 1.18,
to M 5 6.90, SD 5 1.18). Sleep quality was assessed with
the sleep disturbances component. A stem question
asked: ‘‘During the past month, how often have you
had trouble sleeping because you . . .’’ followed by
nine problems (e.g., ‘‘wake up in the middle of the



night or early morning,’’ ‘‘had bad dreams,’’ ‘‘have pain’’) rated on a 4-point scale ranging from 0 (not
during the past month) to 3 (three or more times a week).
Scores for the nine items were summed. Sums were
assigned values: 0 (sum of 0), 1 (sum from 1 to 9), 2
(sum from 10 to 18), and 3 (sum from 19 to 27). Mean
sleep disturbances across waves ranged from 1.03
(SD 5 .46) to 1.14 (SD 5 .56). This component was
selected because it is an indicator of sleep quality
that draws on concrete experiences and is sensitive to
variability between and within persons. The duration
and disturbances components of the PSQI have been
shown to distinguish healthy controls from clinical
samples (i.e., those with sleep, depressive, or
posttraumatic stress disorders; Backhaus, Junghanns,
Broocks, Riemann, & Hohagen, 2002; Buysse et al.,
1989; Calhoun et al., 2007).
Between-persons predictors (baseline measures). Gender was coded as female 5 0 (n 5 114), male 5 1 (n 5 73). Living situation was coded as
living with parents 5 0 (n 5 98) versus living away
from parents 5 1 (n 5 89). Social support was
measured with the mean of seven items (Galambos,
Barker, & Krahn, 2006). Participants were asked ‘‘When
you have problems, how much can you rely on each of
the following people for help?’’ ‘‘Mother,’’ ‘‘father,’’ ‘‘other family members,’’ ‘‘spouse, boyfriend,
girlfriend,’’ ‘‘friends,’’ ‘‘people at work,’’ and ‘‘others’’ were options. The extent to which each source
provided social support was rated on a scale ranging
from 0 (have no such person or not at all) to 3 (very much). Coefficient a was not computed, as it is not expected
that individuals with high support from one
source would necessarily have high support from
other sources (M 5 1.63, SD 5 .46, range 5 .43 – 2.86).
Financial stress was measured with the mean of eight
items from Pearlin and Schooler’s (1978) household
economic stress measure, which asks, ‘‘When you think
of your current financial situation, how do you feel?’’
followed by feelings such as ‘‘worried’’ and ‘‘tense.’’ Items were rated on a 4-point scale ranging from 1 (not
at all) to 4 (very), and were averaged (M 5 1.89,
SD 5 .68, range 5 1.00 – 3.88). Coefficient a was .92.
High school GPA, used for admission into university,
was obtained from the Registrar and was a percentage
score (M 5 85.30, SD 5 5.97, range 5 70.40 – 97.20).
Within-person predictors (time-varying covariates). The Positive and Negative Affect Schedule (Watson, Clark, & Tellegen, 1988) was adapted to
capture affective experience over the previous 2 weeks.
Participants were asked: ‘‘Over the last 14 days, on
how many days did you feel . . .?’’ The number of days

reported for each of 10 positive affect items (e.g.,
interested, proud) was summed, with a possible
range from 0 (no days in which any of 10 positive
emotions were experienced) to 140 (experienced all 10
positive emotions every day for 14 days). Positive
affect means across waves ranged from 62.66
(SD 5 32.10) to 72.04 (SD 5 29.36). The number of
days reported for each of 10 negative affect items
(e.g., distressed, hostile) was summed. Across waves,
negative affect ranged from 30.79 (SD 5 21.60) to 36.51
(SD 5 24.08). Coefficient as ranged across waves from
.92 to .95 for positive affect and .88 to .93 for negative
affect. A 14-day period was used because recall of
emotions over 14 days is accurate within 1 or 2 days
(N. R. Brown, Williams, Barker, & Galambos, 2007).
Stress was measured with the four-item version of
the Perceived Stress Scale (Cohen, Kamarck, &
Mermelstein, 1983). Participants were asked, ‘‘Over
the last 14 days, how often have you . . .’’ followed by items such as ‘‘felt that you were unable to control the important things in your life’’ and ‘‘felt confident about your ability to handle personal problems’’

(reverse coded). Participants rated these items on a
5-point scale, ranging from 0 (never) to 4 (very often),
and ratings were averaged. Across waves, stress
scores ranged from a mean of 1.79 (SD 5 .63) to 1.95
(SD 5 .74). Coefficient as ranged across waves from
.67 to .80.
Participants’ academic effort was measured with the
mean of two items which asked: ‘‘Over the last 14
days, on how many days did you . . . work as hard as
you could on your schoolwork’’ and ‘‘. . . avoid doing any schoolwork at all (other than attending classes).’’
The latter item was reverse scored. Coefficient a was
.62 for the two-item scale. Mean academic effort
ranged from 8.23 (SD 5 3.20) to 9.69 (SD 5 3.01).
There were two measures of students’ social
experiences. One item assessed the frequency with
which students socialized with friends: ‘‘Over the last
14 days, on how many days did you get together with
friends (for example, for coffee, a movie, a party,
etc.)?’’ Students got together with friends an average
of 4.76 (SD 5 3.48) to 5.43 (SD 5 3.72) days in a 2-week
period. Alcohol use was assessed by asking ‘‘Over the
last 14 days, on how many days have you had
alcoholic beverages to drinkFmore than just a few
sips?’’ Average days of alcohol use in 14 days ranged
from 1.31 (SD 5 1.75) to 1.84 (SD 5 2.49).
There were no significant correlations among between-persons predictors. Within-person intercorrelations (po.05) showed that more negative affect


days were associated with more stressful (r 5.66) and
alcohol use days (r 5.13), and with fewer positive
affect (r 5 À.22) and academic effort days (r 5 À.19).
More positive affect days were related to fewer
stressful days (r 5 À .45) and more academic effort
(r 5.22), socializing (r 5.32), and alcohol use days
(r 5. 11). Additionally, a higher number of stressful
days was associated with fewer days of academic
effort (r 5 À.18) and socializing (r 5 À.15); more academic effort days were related to fewer days of alcohol use (r 5 À.18) and socializing (r 5 À .15; all po.05). Finally, socializing and drinking were associated on the same days (r 5.30, po.05). Multilevel models (HLM 6.06; Raudenbush &

Bryk, 2002) examined between-persons (stable) and
within-person (time-varying) predictors of sleep
quantity (Table 1) and quality (Table 2). First, unconditional means models (not shown) containing no between-persons predictors or time-varying covariates determined the proportions of between-persons and within-person variance in sleep quantity and

quality. Second, effects of gender, living situation,
social support, financial stress, and high school GPA
(assessed at baseline) on the intercept (the sleep


measure averaged across months) were examined
(Model 1). Third, affective (positive, negative),
stressful, academic, and social (socialized, used
alcohol) experience variables were included as withinperson predictors in Models 2 through 5, respectively, to examine how sleep covaried with these experiences across months, controlling for betweenpersons differences. Concerns about having sufficient statistical power and including more covariates than time points led us to test separate models rather

than an integrative model including all covariates
(Singer & Willett, 2003). Between-persons and withinperson predictors were grand-mean centered, except for dichotomous variables. Full information maximum likelihood estimation was used to generate parameter estimates and to preserve cases containing within-person missing values. Intercepts in all models were estimated as randomly varying

across persons, and the slopes of all time-varying
covariates were specified as nonrandomly varying.
Likelihood-ratio tests assessed the significance of
the differences in fit between the unconditional
means model and Model 1 and between Model 1 and
Models 2 through 5.

Results of Multilevel Models Predicting Covariation of Sleep Quantity and Affective, Stressful, Academic, and Social Experiences Across Months, Controlling for Between-Persons Effects on the Intercept

Model 1
Fixed Effect


Intercept (average)
effects on intercept
Living situation
Social support
Financial stress
À 0.20Ã
High school GPA
Recent experiences
(time-varying covariates)
Positive affect
Negative affect
Academic effort
Used alcohol
w2 (df)
10.58 (5)
Comparison model

Model 2 Affect

Model 3 Stress





Model 4

Model 5 Social













À 0.12
À 0.00


À 0.13


À 0.22


À 0.21


À 0.01Ã


À 0.23Ã

À 0.02


83.47Ã (2)
Model 1

127.06Ã (1)
Model 1

5.53Ã (1)
Model 1

Note. Coeff 5 unstandardized coefficient; SE 5 standard error; UnMs 5 unconditional means model; N 5 187. a
Male 5 1.
Away from parents 5 1.

À 0.02
25.44Ã (2)
Model 1



Results of Multilevel Models Predicting Covariation of Sleep Quality and Affective, Stressful, Academic, and Social Experiences Across Months, Controlling for Between-Persons Effects on the Intercept Model 1

Fixed Effect


Model 2 Affect

Intercept (average)
effects on intercept
À 0.05
Living situationb
Social support
Financial stress
High school GPA
À 0.00
Recent experiences
(time-varying covariates)
Positive affect
Negative affect
Academic effort
Used alcohol
w2 (df)
21.94Ã (5)
Comparison model

Model 3 Stress





Model 4 Academic


Model 5 Social










À 0.07
À 0.00


À 0.03
À 0.00


À 0.06
À 0.00


À 0.06
À 0.00


À 0.00



À 0.01


50.78Ã (2)
Model 1

73.24Ã (1)
Model 1

9.53Ã (1)
Model 1

À 0.01Ã
13.34Ã (2)
Model 1

Note. Coeff 5 unstandardized coefficient; SE 5 standard error; UnMs 5 unconditional means model; N 5 187. A higher score on sleep quality indicates poorer quality (i.e., higher sleep disturbance). a

Male 5 1.
Away from parents 5 1.

Sleep Quantity
The unconditional means model determined that 45%
of the variation in sleep quantity was within the person and 55% was between persons. Models 1 through 5 showed that gender was a reliable predictor of sleep
quantity, with men reporting that they slept longer
hours than did women. Greater financial stress was a
significant between-persons predictor of fewer average
sleep hours only in Model 1. Turning to the withinperson models, negative affect and stress were significant negative predictors of variation in sleep quantity across months. That is, students reported

sleeping fewer hours in months they experienced more
negative affect and in months they experienced higher
stress. Model 1 did not provide a better fit to the data
than did the unconditional means model, but Models 2
through 5 fit the data better than did Model 1.
Sleep Quality
The unconditional means model showed that 67% of
the variation in sleep quality was within the person
whereas 33% was between persons. Living away
from home was a robust predictor of more frequent
sleep disturbances. In Model 3 only, higher social

support predicted more sleep disturbance, an unexpected finding. Financial stress was a significant predictor of lower sleep quality (i.e., higher sleep
disturbances). However, this association was not
observed with negative affect (Model 2) and perceived stress (Model 3) in the models, both of which were related to more sleep disturbance. Sleep quality
was also lower in months that students socialized on
fewer days (Model 5). Model 1 fit the data better than
did the unconditional means model, and Models 2
through 5 all fit better compared with Model 1.
Adaptation to increased academic demands, new
social horizons, and the semiautonomous living
provided by the transition to university bring significant opportunities and challenges as the late adolescent progresses toward adulthood (Schulenberg & Maggs, 2002). Obtaining a sufficient quantity of

high quality sleep is necessary for optimal academic
performance, physical health, and psychological
well-being, yet too few students new to university
get adequate sleep (Buboltz et al., 2006). Fluctuations
in sleep quantity and quality in the first year of


university may reflect a process of adaptation as
students go about learning how to cope with new
demands. Observing how personal circumstances
upon entering university predict variations in sleep
across the first year of university provides a window
into understanding the adaptational process that
may be useful for identifying mediational targets for
programs designed to facilitate this transition and
improve retention. Furthermore, learning how sleep
quantity and quality rise and fall from month to
month in tandem with affective, stressful, academic,
and social experiences improves understanding of
the possible short-term costs and benefits of sleep
behaviors. Using a within-person design following
students multiple times across their first year of
university, this study provides new insights into
predictors of sleep quantity and quality. Three major
themes are suggested.
First, university students who were more independent from parental care experienced lower quality sleep. That is, those living away from home and those experiencing greater burdens due to financial

stress had more sleep disturbances. Possible mechanisms underlying these associations include external impediments to sleep such as living in shared and
potentially noisy accommodations or needing to
work longer hours in paid employment, as well as
internal states, such as homesickness (Beck, Taylor, &
Robbins, 2003) or anxiety about paying bills (Furr,
Westefeld, McConnell, & Jenkins, 2001). Greater
anxieties about finances or leaving home could also
explain why female students slept less than male
students, a gender difference consistent with epidemiological and university student research showing a higher prevalence of insomnia and other sleep difficulties among women than men (Buboltz et al., 2001; Buysse et al., 2008; Tsai & Li, 2004). One practical

implication for universities is to introduce living
options such as substance-free and noise controlled
residences to reduce impediments to sleep. An implication for students and parents is to put sleep into the equation when they discuss the costs and benefits
of living away from home and of incurring the need
for employment simultaneous with postsecondary
education. During economic recessions, such tradeoffs are likely to be widespread, underscoring the need for reliable student financial aid.
Second, times with more negative affect and stress
coincided with times of less positive sleep. Withinperson associations (i.e., intercorrelations) among the time-varying predictors, similarly, showed that
when negative affect and stressful experiences were
higher, academic effort was lower. Moreover, alcohol
use was greater in months with more negative affect.


This suggests that students experience difficulties in
multiple areas of their lives simultaneously. Campus
health, residence life, and academic staff should be
cognizant of the potential pile-up of difficulties
during students’ first year. Although we cannot
disentangle cause and effect, students may need assistance to prevent or overcome downward spirals when stressors, negative affect, drinking to cope, and
inadequate sleep co-occur. Initial results from a social support intervention designed to ease the transition to university have been promising; students who participated reported less depression, more

social support, and higher adjustment to university
(Pratt et al., 2000).
Third, positive experience (e.g., positive affect,
academic effort) generally did not predict sleep
quantity or quality, but there was covariation of sleep
quality with socializing. Several explanations can be
advanced. On the one hand, postsecondary students
rank social and academic goals as most important,
and the majority of their time is allocated to these
pursuits (Maggs, 1997). The lack of associations of
sleep with social and academic experiences may
suggest that students generally manage these demands successfully, at least as evidenced by their sleep quantity and quality over the longer term. Alternatively, like other health domains such as nutrition and exercise, sleep may be a behavior for which enough is enough. That is, once sufficient rest is

obtained, positive experiences may not co-occur with
higher quality sleep. Finally, the effect size linking
positive experiences with sleep may simply be more
Some results were consistent with the daily diary
study of Galambos et al. (2009), specifically, that
sleep, negative affect, and stress go hand-in-hand. In
the daily diary study, negative affect and stress increased on days when sleep was of lower quantity or quality the night before. That sleep quantity and
quality were associated in the same direction at the
monthly level speaks to the robustness of the relations among sleep, negative affect, and stress. These daily and monthly associations may be capturing the
process by which sleep relates to mental health
difficulties over the longer term. If days of poor sleep
run into months of poor sleep, the cumulative effects
are likely to be dire. These results underscore the
seriousness of inadequate sleep among university
The positive association between socializing and
better sleep quality echoes the daily diary finding
that socializing preceded a longer night’s sleepFbut
is counter to the finding that more sleep at night was
associated with reduced socializing the next day. The



relation between positive sleep and activities with
friends, whether on a daily or monthly level, may
reflect the relaxing effects of socializing. It may be
good for students’ sleep if they regularly spend time
with friends. In the daily study, students who slept
longer not only reduced their socializing the next
day but they increased time spent on schoolwork,
suggesting that they chose academic work over
friends on days they had the energy to do so. In the
daily study, alcohol use seemed to interfere with
sleep quality on a daily level, but no such association
emerged here. Similarly, academic effort was unrelated to sleep in this study, but school demands covaried with sleep in complex ways in the daily diary study. Such differences between the monthly and

daily results highlight the capacity for daily diary
studies to capture temporal relations that may be
missed in studies covering longer spans of time, but
also suggest that some day-to-day relations may not
emerge as reliable longer-term associations. Additionally, the time frame for the measurement of sleep (past month) did not overlap completely with measures of student experience (past 14 days), which could have attenuated the magnitude of month-tomonth covariations. Limitations include reliance on subjective rather

than objective sleep indicators and collection of data
at only one university. Possible directions for research
include using actigraph recordings to gather objective
sleep data, conducting studies at multiple sites and
with non-students to enhance generalizability, measuring sleep and other experiences over the same time period, examining covariations of sleep with a
wider range of student experiences (e.g., romantic
relationships), following students into their later
university years to determine how sleep affects academic performance, and evaluating the effectiveness of psychoeducational interventions (e.g., sleep hygiene instructions; Buboltz et al., 2006) for improving students’ sleep.

Sleep difficulties among university students can
have profound academic, mental health, and behavioral consequences. This study bolsters a growing literature on student sleep that has implications for students and universities. For example, the link

between sleep, stress, and negative affect may
inform the practices and recommendations of
on-campus counseling and health care. It may also
encourage administrators to consider students’ sleep
needs when scheduling classes and examinations
(Buboltz et al., 2006). The seasoned professor has
learned to caution students not to sacrifice sleep for
the sake of a few extra hours of study time before an
exam, but universities must first begin to acknowl-

edge and address student sleep as a significant
health concern for these cautions to carry weight in
the classroom.
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