---
id: "edwards-2025-covid-vaccines"
title: "Acceptance, perceptions, and compliance for COVID-19 vaccines among students attending a rural university: An interventional study using brief video messages"
authors:
  - "Amber L. Harris Bozer"
  - "Subi Gandhi"
  - "Dustin C. Edwards"
venue: "Journal of American College Health"
year: 2025
date: "2025-11-26"
doi: "10.1080/07448481.2025.2472184"
url: "/research/publications/10-1080-07448481-2025-2472184/"
pdf: "/research/publications/10-1080-07448481-2025-2472184/dustin-edwards-10-1080-07448481-2025-2472184.pdf"
openAccess: false
citedBy: 0
citedBySource: "OpenAlex, read 2026-09-12"
---
# Acceptance, perceptions, and compliance for COVID-19 vaccines among students attending a rural university: An interventional study using brief video messages

Short videos did not change COVID-19 vaccine attitudes among 298 students at a rural Texas university.

## Abstract

Objective: The purpose of this study was to examine the factors associated with vaccine compliance and the effectiveness of short-term video interventions on COVID-19 vaccine perceptions among students attending a state university located in rural Texas. Participants: A total of 298 students participated in an online survey. Methods: Students completed the COVID-19 Vaccine Acceptance Scale (COVID-VAC) and Perceptions of Vaccines Scale before and after watching one of three videos (neutral, educational, or disease effects). Results: Differences in vaccination status were observed for ethnicity and political leanings ( p p > 0.05). Conclusions: Short-term video interventions were ineffective in altering vaccine perceptions and improving acceptance of the COVID-19 vaccine in our study population. Impact of the type and duration of educational videos should be explored by future studies to combat vaccine hesitancy in future population-based studies.

## Full text

Machine-extracted from the PDF linked above. It carries the artifacts that come with reading a typeset two-column page: running heads, figure captions in the flow of the prose, and words broken across line ends. The abstract above is the registry's deposit and is the authoritative text.

Journal of American College Health
ISSN: 0744-8481 (Print) 1940-3208 (Online) Journal homepage: www.tandfonline.com/journals/vach20
Acceptance, perceptions, and compliance for
COVID-19 vaccines among students attending a
rural university: An interventional study using
brief video messages
Amber L. Harris Bozer, Subi Gandhi & Dustin C. Edwards
To cite this article: Amber L. Harris Bozer, Subi Gandhi & Dustin C. Edwards (2025) Acceptance,
perceptions, and compliance for COVID-19 vaccines among students attending a rural
university: An interventional study using brief video messages, Journal of American College
Health, 73:10, 4056-4070, DOI: 10.1080/07448481.2025.2472184
To link to this article: https://doi.org/10.1080/07448481.2025.2472184
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ReseaRch aRticle
Journal of american college HealtH
2025, Vol. 73, no. 10, 4056–4070
Acceptance, perceptions, and compliance for COVID-19 vaccines among
students attending a rural university: An interventional study using brief
video messages
amber l. harris Bozer, PhDa , subi Gandhi, PhDb and Dustin c. edwards, PhDc
aDepartment of neuroscience, tarleton State university, Stephenville, texas, uSa; bDepartment of medical lab Sciences, Public Health, and
nutrition Sciences, tarleton State university, Stephenville, texas, uSa; cDepartment of Biological Sciences, tarleton State university, Stephenville,
texas, uSa
ABSTRACT
Objective: the purpose of this study was to examine the factors associated with vaccine compliance
and the effectiveness of short-term video interventions on cOViD-19 vaccine perceptions among
students attending a state university located in rural texas. Participants: a total of 298 students
participated in an online survey. Methods: students completed the cOViD-19 Vaccine acceptance
scale (cOViD-Vac) and Perceptions of Vaccines scale before and after watching one of three videos
(neutral, educational, or disease effects). Results: Differences in vaccination status were observed for
ethnicity and political leanings (p < 0.05). the video interventions did not impact cOViD-Vac or
Perceptions of Vaccines scores over time (p > 0.05). Conclusions: short-term video interventions
were ineffective in altering vaccine perceptions and improving acceptance of the cOViD-19 vaccine
in our study population. impact of the type and duration of educational videos should be explored
by future studies to combat vaccine hesitancy in future population-based studies.
Introduction
Since Jan 1, 2020, there have been over one million deaths
(302 per 100,000) in the United States due to COVID-19.1
A substantial proportion of these fatalities could be attributed
to the inadequate uptake of COVID-19 vaccines by the gen-
eral population following their availability. Vaccines are
among the most significant public health achievements of
the twentieth century. They have eradicated smallpox, elim-
inated poliomyelitis in the Americas, and controlled other
infectious diseases, including measles, diphtheria, and
rubella.2 Despite this, vaccines now face renewed criticism as
segments of the public express dwindling confidence in their
effectiveness.3 Just prior to the coronavirus pandemic, the
World Health Organization declared vaccine hesitancy to be
a great public health threat in controlling and preventing
infectious diseases.4 Persistent vaccine hesitancy has limited
the public health response to COVID-19 and hampered herd
immunity efforts by immunization to reduce virus transmis-
sion at the community level.3,5,6 However, Morens and col-
leagues7 argue that achieving herd immunity may be
unattainable due to the genetic instability of SARS-CoV-2,
the virus responsible for COVID-19, which contrasts with
the relative stability of poliovirus and measles virus.
Although the safety and efficacy of the Pfizer and
Moderna COVID-19 vaccines have been repeatedly
demonstrated during clinical trials worldwide across all age
groups, a significant portion of the American population
remains hesitant to receive the vaccination.8,9 Complicating
immunization efforts, non-pharmaceutical interventions such
as state-mandated masking, social distancing, lockdowns,
quarantines, and testing have caused societal polarization
that extends to vaccine hesitancy and confidence, even
though stark evidence shows unvaccinated individuals have
higher incidence, hospitalization, and mortality rates due to
COVID-19 compared to those who are vaccinated.6,10–15
Several demographic and geopolitical factors have influ-
enced the COVID-19 vaccination rates in the U.S. Although
there has been significant acceptance and uptake of the
COVID-19 vaccine by those aged 50 and over, other age
groups lag in vaccination rates. One year after vaccine avail-
ability, 91% of those over the age of 65 and 82% of those
aged 50–64 were fully vaccinated compared to those aged
18–49 (69%), 12–17 (60%), and 5–11 (30%).1 Disparity in
the uptake of vaccines was also observed among different
racial/ethnic groups. In 2022, African Americans (57%)
lagged significantly behind Whites (63%), Asians (85%), and
Hispanics (65%) in receiving at least one dose of the
COVID-19 vaccination across 38 states, and the overall vac-
cination rate has since plateaued for all racial and ethnic
groups.1,16 Additional factors influencing the acceptance and
© 2025 taylor & francis group, llc
CONTACT amber l. Harris Bozer amberharrisbozer@gmail.com, amber.harris@go.tarleton.edu Department of neuroscience, tarleton State university,
Stephenville, tX, uSa.
Supplemental data for this article can be accessed online at https://doi.org/10.1080/07448481.2025.2472184.
https://doi.org/10.1080/07448481.2025.2472184
ARTICLE HISTORY
received 9 october 2023
revised 16 october 2024
accepted 19 february 2025
KEYWORDS
coViD-19; coViD-19 vaccine;
student vaccine perceptions;
vaccine acceptance; vaccine
hesitancy

JOuRnal Of ameRican cOlleGe health 4057
adoption of the COVID-19 vaccine include lower socioeco-
nomic status, limited educational attainment, rural residency,
female gender, and affiliation with the Republican Party.17–20
Geographical variations in vaccination rates have per-
sisted within the United States. Gandhi and Harris Bozer
(2020) studied rural college students’ compliance with the
flu vaccine. They found that despite having health insurance
coverage, only 30% were vaccinated against the flu,21 in
comparison to the national rates ranging from 8% to 39%.22
The primary factors contributing to low compliance were
identified as the low perceived threat (20%) and time con-
straints due to busy schedules (11%).21 The vaccination rates
for COVID-19 were much lower in rural areas compared to
urban areas,23 despite being administered free of charge
during the pandemic.24
Several studies have emerged regarding COVID-19 vacci-
nation rates and hesitancy among college students during
the pandemic. In a study conducted at a university (n = 457)
in Spring of 2021, among the unvaccinated students (n = 352),
nearly 53% indicated their intentionality to receive the
COVID-19 vaccine when made available to the college body.
Students who were healthcare workers and had a vaccinated
family member were more likely to receive the COVID-19
vaccine. Non-vaccinated students who discussed vaccine
information and had positive attitudes toward vaccination
showed greater willingness to get the COVID-19 vaccine.25
Another study conducted in a similar timeframe (n = 989)
demonstrated that perceived social norms significantly pre-
dicted vaccine hesitancy when accounting for demographics
and pandemic experiences.26 Other reasons noted in the lit-
erature for college students’ hesitancy toward the COVID-19
vaccine included unknown side effects, skepticism about
necessity, and mistrust toward the vaccine and the govern-
ment, along with infodemic and political affiliation.26–29
Purpose of the present study
When juxtaposed with their urban counterparts, individuals
residing in rural regions encounter considerable health ineq-
uities attributable to both individual and environmental
determinants.30 This phenomenon can be readily extrapo-
lated to the context of college students. According to the
Health Resources and Services Administration, rural areas
are defined as either micropolitan areas (with an urban core
population of 10,000–49,999 individuals) or counties situated
outside metropolitan areas (having an urban core of 50,000
or more individuals).31,32 This study examined college stu-
dents’ perceptions and attitudes toward vaccination during
the study period in a rural country with a population of less
than 45,000 residents.33
Upon the availability of COVID-19 vaccines, vaccination
rates were higher in urban areas than in rural areas.19,23
However, these prevalence studies were conducted in the
general population.19,23,34,35 Recognizing approaches that can
successfully enhance vaccine acceptance and lessen the
effects of pandemics among university students is essential,
considering that rural college students have different behav-
iors and health risk profiles compared to their counter-
parts,36–38 and generally possess a low perception of risk and
increased hesitancy regarding vaccine-preventable illnesses
like the flu and COVID-19.21,28 Furthermore, high popula-
tion density and close living conditions on college campuses
facilitate outbreaks, as studies indicate a strong correlation
between population density and COVID-19 transmission
rates.39
As college students are in the transition period between
late adolescence and early adulthood, video-based interven-
tions could aid in mitigating risk perception and hesitancy
toward COVID-19 and promote responsible steps to reduce
community spread in future pandemics. This premise is
derived from the efficacy of establishing appropriate atti-
tudes and intentions toward prevention behaviors, as demon-
strated in a systematic review of 38 studies that highlighted
the effectiveness of health promotion through video messag-
ing,40 as well as keeping the duration of the video in mind.41
Additionally, video messaging was an ideal intervention
during the pandemic era, where COVID-19 had demon-
strated high transmissibility rates at the community level.42
Our study aimed to identify and elucidate perceptions of
the COVID-19 vaccine among students attending a univer-
sity in rural Texas utilizing the following research questions:
a. What demographic factors differentiate individuals
who are COVID-19 vaccine-compliant versus those
who are non-compliant?
b. What type of vaccine-related video information will
positively change vaccine perceptions of COVID-19
(neutral, educational, flooding)?
Materials and methods
Participants
A University Institutional Review Board determined that the
proposed study was exempt before participants were recruited
for the study. Participants were recruited by sharing flyers,
emails (using a mass email system), and various social media
platforms during the Spring and Summer semesters of 2022.
Participants were required to meet the following criteria: be
at least 18 years of age, reside in the United States, be cur-
rently enrolled as a student, and provide informed consent
before completing the anonymous survey.
Sampling and study design
We used convenience sampling and an experimental design.
Participants were asked to complete survey questions in
Google Forms related to “perception of vaccines” and were
then randomized to one of the three interventional arms.
Following the video presentation, each group was adminis-
tered identical questions about their "perceptions of vac-
cines" they had previously answered before viewing
the videos.
i. The control/neutral group (arm 1) was exposed to a
video unrelated to the vaccination topic (https://www.
youtube.com/watch?v=I7STZsY_-Ps; 2:22 length).

4058 a. l. haRRis BOZeR et al.
ii. The first experimental group (arm 2) was exposed to
an educational video produced by the Centers for
Disease Control and Prevention (CDC) highlighting
the advantages and importance of getting the
COVID-19 vaccine (https://www.youtube.com/
watch?v=NeQ9ssmwG3w; 1:38 length).
iii. The last group (arm 3) was the experimental/flood-
ing group (disease effects), which was exposed to a
video showing complications of COVID-19 in a hos-
pital setting. The video was produced by St. Charles
Health (https://youtu.be/Dz5ZVxWit3w; 2:20 length).
It demonstrated the pandemic’s social justice and iso-
lation aspects and depicted its burden on frontline
workers, patients, and the healthcare system overall.
Measures
A digital survey was administered containing the informed
consent, demographic, general vaccine, and monoclonal anti-
body infusion questions (see Table 1 for a full list of survey
questions and answer choices). Participants were only per-
mitted by the software to have 1 response to the survey.
Students may have received extra credit for survey comple-
tion from their instructors (in good faith), but no other
incentives or compensation occurred.
Instruments used in the study
a. COVID-19 Vaccine Acceptance Scale (COVID-VAC):
A six-item validated and reliable vaccination accep-
tance scale was employed, encompassing constructs
related to trust, risk, safety, and efficacy (see ques-
tions in Table 1).43 The items were measured on a
Likert Scale (Strongly agree = 1 to Strongly disagree =
5), with higher scores indicating more vaccine accep-
tance and lower indicating less vaccine acceptance. In
the current study, the reliability of the scale was
found to be high both before (α = 0.91, 95% CI 0.89–
0.92) and after (α = 0.91, 95% CI 0.90–0.93) the
administration of the video.
b. Perceptions of Vaccines Scale: A nine-item validated
and reliable scale was used to assess a participant’s
agreement with vaccines (see questions in Table 1),44
containing three answer choices (1 = agree “yes,”
2 = unsure, and 3 = disagree “no).” Higher scores on a
single question indicate greater disagreement with
that particular item. As with the COVID-VAC, this
scale demonstrated high reliability before (α = 0.88,
95% CI 0.86–0.90) and after (α = 0.90, 95% CI 0.89–
0.92) administration of the video.
Data analyses
Employing G*Power software, the optimal sample size for
statistical analyses was computed for the largest statistical
model (mixed ANOVA) a priori using 0.95 power
(probability of finding significance), keeping an alpha crite-
rion level of 0.05 and an expected effect size of 0.25
(n = 66).45–47 Data are presented as mean ± standard deviation.
Frequency data with less than five participants in each cell
were collapsed or excluded for analyses. Data were down-
loaded into Microsoft Excel for cleaning, coding, and orga-
nization, and SPSS and JASP software were used for statistical
computations.48–50
Demographic data and general questions were compared
across video groups using Chi-square analyses with an alpha
criterion of 0.05. The results include descriptive statistics for
the number of children and care status. A Chi-square anal-
ysis was run to compare the vaccination status of children
and participants and to compare vaccine status by demo-
graphic and general question variables.
To examine the influence of the short-term videos on
COVID-VAC scores, we computed mixed ANOVAs using
vaccine status (vaccinated/not vaccinated) and video group
(neutral/education/disease effects) as between-subjects fac-
tors and time (pre-survey/post-survey) as the within-subject
factor. The Bonferroni post hoc test was employed solely in
instances where there were main effects or interactions.
Responses to the Perceptions of Vaccines questions were
organized pre- and post-administration and segregated by
video group and vaccination status.
Results
General questions and vaccine-related questions
A total of 360 participants responded to the survey. Four
participants did not provide informed consent and were not
presented with survey questions, and three were excluded
for not completing the survey, leaving 353 for the final anal-
ysis. A total of 55 more participants were excluded because
they were not currently enrolled as students (alumni; n = 4,
former student/non-graduate; n = 1, full-time administrators;
n = 2, full-time faculty; n = 5, part-time faculty; n = 2, staff;
n = 11, and other/not reported; n = 30). However, analyses
that include these 55 participants (not specific to students)
can be viewed in Appendix B. After these exclusions, there
were 298 remaining participants for analyses.
Comprehensive demographic comparisons are available in
Table 2, with a breakdown in frequency and percentage for
each demographic category by video group. Chi-square anal-
yses to compare demographics by group indicated no differ-
ences between groups on demographic categories other than
political leanings (Table 2). Responses to the questions about
first responder status, military status, university affiliation,
and knowing someone hospitalized or died had too few
responses in some cells to run analyses. The mean age of
the sample was 28.28 (SD = 10.66, Range = 19–69), and par-
ticipants were aged 18–24 years (55.03%), 25–40 years
(29.87%), 40–55 years (12.42%), and 55+ years (2.68%). The
study did not include an item related to student classifica-
tions; however, the data revealed that 30% of students had
completed high school, 27.52% had completed Associate’s
degrees, 33.89% had completed Bachelor’s degrees, and
8.39% held professional degrees. Sample participants were
White (75.17%), Hispanic or Latino (9.73%), Two or More

JOuRnal Of ameRican cOlleGe health 4059
Table 1. full survey questions with possible responses.
Demographics questions
Age (in years)
open ended
How do you describe your gender identity?
female
male
transgender man
transgender woman
gender non-conforming
intersex
other
Prefer not to answer
Ethnicity
White
middle eastern or north african
Black or african american
native american or american indian and/or alaskan native
asian
native Hawaiin or Pacific islander
Hispanic or latino
two or more
other/unknown
Ethnicity
Hispanic or latino
not Hispanic or latino
Education
Some high school
High school
associate’s degree
trade school or vocational school
Bachelor’s degree
master’s degree
Professional or doctoral degree
Marital status
married
married, but not cohabitating
not married, but cohabitating
not married or cohabitating
Household income
less than $25,000
$25,000–$50,000
$50,000–$85,000
$85,000–$140,000
more than $140,000
Employment
employed, full-time
employed, part-time
retired
unemployed
How many children do you have?
(choose 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, or 10 or more)
Please provide the ages of your children (check all that apply).
not applicable
4 years and under
5–11 years old
12–17 years old
18 years and older
If you have children, are they under your care?
not applicable
no, not under my care
Yes, under my care part-time
Yes, under my care full-time
Political party
Democrat
independent
libertarian
republican
green party
other
What do you consider yourself mainly to be? (political ideology)
lightly conservative
moderately conservative
Highly conservative
neutral
lightly liberal
moderately liberal
(Continued)

4060 a. l. haRRis BOZeR et al.
Highly liberal
What is your affiliation with the university (check all that apply)?
not affiliated
university student
Staff
Part-time faculty
full-time faculty
Part-time administrator
full-time administrator
If you are a college student, what is your residency status?
not applicable
on-campus
off-campus
If you are a college student, what is your classification?
not applicable
freshman
Sophomore
Junior
Senior
Post-baccalaureate student
master’s student
Doctoral student
Do you have medical insurance?
Yes
no
Are you a first responder?
no, i am not a first responder
Yes, i am an emt
Yes, i am a paramedic
Yes, i am a firefighter or fire marshall
Yes, i am a police officer
Yes, i am a sheriff/sheriff’s deputy
Yes, i am a correctional officer
Yes, i am park police or a park ranger
Yes, i am another kind of first responder
What is your military status?
i am not currently in the military and have never served
i am not currently in the military, but have served previously
i am currently in the military
General Vaccines Questions
Have you received the COVID-19 vaccine?
Yes, fully vaccinated plus boostered
Yes, fully vaccinated
Yes, partially vaccinated
no, i plan to
no, i do not plan to
If you are vaccinated, which one did you receive?
not applicable – i am not vaccinated
Pfizer
moderna
Johnson & Johnson
Have you tested positive for COVID-19?
no
Yes, while unvaccinated
Yes, while vaccinated
Yes, both while unvaccinated and later while vaccinated
If you said yes to the previous question, how did you learn that you had COVID-19?
not applicable
from a home kit
from a medical provider
from symptoms alone
In the past, how many times did you test positive for COVID-19, with or without symptoms?
1
2
3 or more times
not applicable
If you have children, have they received the COVID-19 vaccine?
Yes
no
not applicable
What are your reasons for choosing to receive/not receive the COVID-19 vaccine?
open ended
Please check all of the following reasons that led to you not receiving the COVID-19 vaccine. Please check all that apply.
not applicable – i am vaccinated
Side effects
Table 1. continued.
(Continued)
Demographics questions

JOuRnal Of ameRican cOlleGe health 4061
time
mistrust of science
mistrust of vaccines
mistrust in this specific vaccine
concerns about the vaccine development
not knowing where to receive the vaccine
lack of knowledge about the risks of the vaccine
i think the vaccine does not work
the vaccine causes the disease itself
my family does not want me to receive the vaccine
my religious leader(s) do not want me to receive the vaccine
i am concerned about the vaccine components
i have a superior immune system and do not need vaccines
Vaccines cause autism
other reasons not listed
What media modalities do you use for information about the COVID-19 vaccine? Please check all that apply
Social interactions and chatting
television – network news
television – political shows
facebook
instagram
linkedin
reddit
Snapchat
tiktok
twitter
Youtube
Scientific journal databases (Pudmed, Scopus, oViD, cochrane)
Scholar research profiles (researchgate, orciD, acedemia.com, google Scholar Profile)
other
On a scale of 1–10, how likely are you to receive the COVID-19 vaccine in the future?
(1–10)
Please check all that apply.
i have been hospitalized with coViD-19 in the past
i know someone who was hospitalized from coViD-19 but did not die
i know someone who was hospitalized from coViD-19 but did die
i know someone who was never hospitalized from coViD-19 but did die
i do not know anyone that has been hospitalized or died from coViD-19
Monoclonal Antibody Infusion Question
I would agree to receive a monoclonal antibody infusion
Yes
no
maybe
Perceptions of Vaccines Scale Questions
When new vaccines are made available to the general public, would you get vaccinated?
Yes
no
unsure
Only people who are having underlying medical problems or who are pregnant should be vaccinated.
agree
Disagree
unsure
I will use vaccines if the government recommends them.
agree
Disagree
Unsure
I will use vaccines if my doctor recommends it.
agree
Disagree
unsure
Vaccines will protect me from illness
agree
Disagree
unsure
I am concerned about the side effects of vaccines
agree
Disagree
unsure
I am concerned that vaccines have not been tested adequately
agree
Disagree
unsure
Table 1. continued.
(Continued)
General Vaccines Questions

4062 a. l. haRRis BOZeR et al.
(7.05%), Black or African American (5.37%), Asian (1.34%)
Native American or American Indian, and/or Alaskan Native
(1.01%), and Native Hawaiian or Pacific Islander (.34%). The
sample was primarily not Hispanic or Latino (79.87%).
Females were the primary respondents (72.82%), with the
remaining participants identifying as male (22.15%),
Transgender man (.34%), gender non-conforming (2.68%),
and prefer not to answer (2.01%).
With regards to parental status, a total of 210 (70.47%)
participants did not have children, 24 (8.05%) participants
had one child, 38 (12.75%) participants had 2 children, 14
(4.70%), participants had 3 children, 8 (2.69%) participants
had 4 children, and 4 (1.34%) participants had 5 or more
children. Of the participants with children, 11 reported that
the children were not under their care, 3 reported part-time
care, and 68 reported full-time care. A Chi-square compari-
son of child vaccination status by participant vaccination
status indicated that participants who received the COVID-19
vaccine were significantly more likely to vaccinate their chil-
dren, χ2 (1) = 27.16, p < 0.001. Specifically, tabulations were
as follows: vaccinated participants with children who had
received the vaccine (n = 34), vaccinated participants with
children who had not received the vaccine (n = 24), unvacci-
nated participants with children who had received the vac-
cine (n = 1), and unvaccinated participants with children
who had not received the vaccine (n = 29).
To assess the factors that differed between COVID-19
vaccine-compliant and non-compliant, Chi-square analyses
were conducted to compare demographics and general vari-
ables by vaccination status. The analyses revealed no differ-
ences in most demographics (p > 0.05). However, there were
differences in ethnicity, political party, and political ideology
across vaccination status (Table 3).
A large number of participants reported not being vacci-
nated (n = 97, 32.5% of the total sample). Vaccinated partic-
ipants (n = 201) reported receiving Moderna (n = 116; 57%),
Pfizer (n = 73; 36%), and Johnson & Johnson Vaccines (n = 9;
4%). Three participants were unsure about the type of vac-
cine they received. Vaccinated participants reported not test-
ing positive in the past (n = 105; 52%) and testing positive
(n = 43 while unvaccinated; 21%, n = 40 while vaccinated;
20%, and n = 13 while unvaccinated and later while
Vaccines may lead to illness in some people
agree
Disagree
unsure
Vaccines will stop the spread of illness
agree
Disagree
unsure
COVID-VAC Scale Questions
COVID-19 is a dangerous health threat
Strongly disagree
Disagree
unsure
agree
Strongly agree
COVID-19 can be prevented by vaccination
Strongly disagree
Disagree
unsure
agree
Strongly agree
The risks of COVID-19 disease are greater than risks of the vaccine
Strongly disagree
Disagree
unsure
agree
Strongly agree
The COVID-19 vaccines available to me are safe
Strongly disagree
Disagree
unsure
agree
Strongly agree
I trust that my government is able to deliver the COVID-19 vaccine to everyone, everywhere in my country, equally
Strongly disagree
Disagree
unsure
agree
Strongly agree
I trust the science behind the COVID-19 vaccines
Strongly disagree
Disagree
unsure
agree
Strongly agree
Table 1. continued.
Perceptions of Vaccines Scale Questions

JOuRnal Of ameRican cOlleGe health 4063
Table 2. chi square comparisons of demographic data and general questions by video group.
Sample
(N = 298)
neutral group
(n = 97)
education group
(n = 128)
effects group
(n = 73)
Variable n % n % of group n % of group n % of group χ2 p
Age 1.84 0.934
18–24 years 164 55.03% 55 56.70% 72 56.25% 37 50.68%
25–40 years 89 29.87% 28 28.87% 38 29.69% 23 31.51%
40–55 years 37 12.42% 11 11.34% 16 12.50% 10 13.70%
55+ 8 2.68% 3 3.09% 2 1.56% 3 4.11%
Ethnicity 3.34 0.911
Black or african am. 16 5.37% 5 5.15% 5 3.91% 6 8.22%
Hispanic or latino 29 9.73% 10 10.31% 14 10.94% 5 6.85%
two or more 21 7.05% 6 6.19% 9 7.03% 6 8.22%
White 224 75.17% 73 75.26% 96 75.00% 55 75.34%
collapsed category* 8 2.69% 3 3.09% 4 3.13% 1 1.37%
asian* 4 1.34% 1 1.03% 2 1.56% 1 1.37%
middle eastern or north
african*
0 0.00% 0 0.00% 0 0.00% 0 0.00%
nat. am. or am. indian
and/or alaskan nat.*
3 1.01% 2 2.06% 1 0.78% 0 0.00%
nat. Hawaiian or Pacific
islander*
1 0.34% 0 0.00% 1 0.78% 0 0.00%
Ethnicity 6.79 0.147
Hispanic or latino 54 18.12% 17 17.53% 29 22.66% 8 10.96%
not Hispanic or latino 238 79.87% 77 79.38% 96 75.00% 65 89.04%
other^ 6 2.01% 3 3.09% 3 2.34% 0 0.00%
Gender 2.36 0.671
female 217 72.82% 70 72.16% 91 71.09% 56 76.71%
male 66 22.15% 20 20.62% 31 24.22% 15 20.55%
collapsed category* 15 5.03% 7 7.22% 6 4.69% 2 2.74%
transgender man* 1 0.34% 1 1.03% 0 0.00% 0 0.00%
gender non-conforming* 8 2.68% 6 6.19% 2 1.56% 0 0.00%
Prefer not to answer 6 2.01% 0 0.00% 4 3.13% 2 2.74%
Education 9.31 0.157
collapsed category* 90 30.20% 30 30.93% 43 33.59% 17 23.29%
Some high school* 2 0.67% 2 2.06% 0 0.00% 0 0.00%
High school diploma* 84 28.19% 27 27.84% 41 32.03% 16 21.92%
trade school or
vocational school*
4 1.34% 1 1.03% 2 1.56% 1 1.37%
associate’s degree 82 27.52% 31 31.96% 33 25.78% 18 24.66%
Bachelor’s degree 101 33.89% 33 34.02% 38 29.69% 30 41.10%
collapsed category* 25 8.39% 3 3.09% 14 10.94% 8 10.96%
master’s degree** 23 7.72% 3 3.09% 12 9.38% 8 10.96%
Professional or doctoral
degree**
2 0.67% 0 0.00% 2 1.56% 0 0.00%
Marital status 0.41 0.816
collapsed category* no 159 53.36% 50 51.55% 71 55.47% 38 52.05%
not married or
cohabitating*
158 53.02% 50 51.55% 71 55.47% 37 50.69%
married, but not
cohabitating*
1 0.34% 0 0.00% 0 0.00% 1 1.37%
collapsed category*Yes 139 46.64% 47 48.45% 57 44.53% 35 47.95%
married* 92 30.87% 28 28.87% 39 30.47% 25 34.25%
not married, but
cohabitating
47 15.77% 19 19.59% 18 14.06% 10 13.70%
Household income 6.84 0.554
less than $25,000 110 36.91% 36 37.11% 48 37.50% 26 35.62%
$25,000–$50,000 63 21.14% 22 22.68% 20 15.63% 21 28.77%
$50,000–$85,000 48 16.11% 14 14.43% 25 19.53% 9 12.33%
$85000–$140,000 52 17.45% 17 17.53% 25 19.53% 10 13.70%
more than $140,000 25 8.39% 8 8.25% 10 7.81% 7 9.59%
Employment 4.29 0.637
employed, full time 109 36.58% 32 32.99% 44 34.38% 33 45.21%
employed, part time 106 35.57% 38 39.18% 46 35.94% 22 30.14%
retired 6 2.01% 3 3.09% 2 1.56% 1 1.37%
unemployed 77 25.84% 24 24.74% 36 28.13% 17 23.29%
Political party 19.9 #0.011
Democrat 64 21.48% 20 20.62% 29 22.66% 15 20.55%
independent 59 19.80% 31 31.96% 17 13.28% 11 15.07%
libertarian 17 5.71% 2 2.06% 12 9.38% 3 4.11%
republican 104 34.90% 28 28.87% 44 34.38% 32 43.84%
other 54 18.12% 16 16.49% 26 20.31% 12 16.44%
Political ideology 17.6 0.127
lightly conservative 37 12.42% 9 9.28% 17 13.28% 11 15.07%
moderately conservative 72 24.16% 22 22.68% 34 26.56% 16 21.92%
Highly conservative 25 8.39% 5 5.16% 9 7.03% 11 15.07%
neutral 58 19.46% 17 17.53% 29 22.66% 12 16.44%
(Continued)

4064 a. l. haRRis BOZeR et al.
Sample
(N = 298)
neutral group
(n = 97)
education group
(n = 128)
effects group
(n = 73)
Variable n % n % of group n % of group n % of group χ2 p
lightly liberal 26 8.73% 8 8.25% 14 10.94% 4 5.48%
moderately liberal 45 15.10% 18 18.56% 15 11.72% 12 16.44%
Highly liberal 35 11.75% 18 18.56% 10 7.81% 7 9.59%
Medical insurance
status
insured 251 84.23% 77 79.38% 108 84.38% 66 90.41% 3.82 0.148
not insured 47 15.77% 20 20.62% 20 15.63% 7 9.59%
*collapsed within analysis due to small numbers per cell: #statistically significant at 0.05.
Table 2. continued.
Table 3. Participant data by vaccination status.
Sample
(N = 298)
not vaccinated
(n = 97)
Vaccinated
(n = 201)
Variable n % n % n % χ2 p
Age 1.99 0.575
18–24 years 164 55.03% 52 53.61% 112 55.72%
25–40 years 89 29.87% 32 32.99% 57 28.36%
40–55 years 37 12.42% 12 12.37% 25 12.44%
55+ 8 2.69% 1 1.03% 7 3.48%
Ethnicity
Black or african am. 16 5.37% 2 2.06% 14 6.97% 14.27 .006*
Hispanic or latino 29 9.73% 4 4.12% 25 12.44%
two or more 21 7.05% 4 4.12% 17 8.46%
White 224 75.17% 86 88.66% 138 68.66%
collapsed category* 8 2.69% 1 1.03% 7 3.48%
Ethnicity 5.07 0.080
Hispanic or latino 54 18.12% 11 11.34% 43 21.39%
not Hispanic or latino 238 79.87% 83 85.57% 155 77.11%
other^ 6 2.01% 3 3.09% 3 1.49%
Gender 5.73 0.057
female 217 72.82% 67 69.07% 150 74.63%
male 66 22.15% 28 28.87% 38 18.91%
collapsed category* 15 5.03% 2 2.06% 13 6.47%
Education 3.44 0.328
collapsed category
(high school or trade school)
90 30.20% 30 30.93% 60 29.85%
associate’s degree 82 27.52% 28 28.87% 54 26.87%
Bachelor’s degree 101 33.89% 35 36.08% 66 32.84%
collapsed category
(professional degree)
25 8.39% 4 4.12% 21 10.45%
Marital status 1.39 0.239
collapsed category
(not married, not cohabitating)
159 53.36% 47 48.45% 112 55.72%
collapsed category
(married, cohabitating)
139 46.64% 50 51.55% 89 44.28%
Household income 7.79 0.100
less than $25,000 110 36.91% 27 27.84% 83 41.29%
$25,000–$50,000 63 21.14% 27 27.84% 36 17.91%
$50,000–$85,000 48 16.11% 15 15.46% 33 16.42%
$85000–$140,000 52 17.45% 17 17.53% 35 17.41%
more than $140,000 25 8.39% 11 11.34% 14 6.97%
Employment 1.87 0.600
employed, full time 109 36.58% 39 40.21% 70 34.83%
employed, part time 106 35.57% 32 32.99% 74 36.82%
retired 6 2.01% 3 3.09% 3 1.49%
unemployed 77 25.84% 23 23.71% 54 26.87%
Political party 76.36 <.001*
Democrat 64 21.48% 1 1.03% 63 31.34%
independent 59 19.80% 14 14.43% 45 22.39%
libertarian 17 5.70% 8 8.25% 9 4.48%
republican 104 34.90% 64 65.98% 40 19.90%
other 54 18.12% 10 10.31% 44 21.89%
Political ideology 113.91 <.001*
lightly conservative 37 12.42% 10 10.31% 27 13.43%
moderately conservative 72 24.16% 43 44.33% 29 14.43%
Highly conservative 25 8.39% 24 24.74% 1 0.50%
neutral 58 19.46% 17 17.53% 41 20.40%
lightly liberal 26 8.73% 2 2.06% 24 11.94%
moderately liberal 45 15.10% 1 1.03% 44 21.89%
Highly liberal 35 11.75% 0 0.00% 35 17.41%
Medical insurance status 0.333 0.564
insured 251 84.23% 80 82.47% 171 85.08%
not insured 47 15.77% 17 17.53% 30 14.93%

JOuRnal Of ameRican cOlleGe health 4065
vaccinated; 6%). Unvaccinated participants reported not test-
ing positive (n = 37; 38%) and testing positive (n = 60; 62%).
Participants reported learning that they had contracted
COVID-19 from a home kit (n = 27; 9%), from a medical
provider (n = 108; 36%), and from symptoms alone (n = 20;
7%). Participants reported testing positive once (n = 120;
40%), twice (n = 34; 11%), or three or more times (n = 7; 2%).
Participants were asked to rate their likelihood of receiv-
ing the COVID-19 vaccine using a 10-point Likert scale.
The mean for vaccinated participants was 8.76 (SD = 2.69),
while the mean for unvaccinated participants was 1.57
(SD = 1.26). Vaccinated participants were more likely to
accept the monoclonal antibody infusion in the future
(p < 0.001). Among the vaccinated participants, 48% (n = 97)
reported that they were willing to receive the infusion, 6%
(n = 12) stated that they were not willing, and 46% (n = 92)
were unsure or maybe willing. In contrast, among the unvac-
cinated participants, 11% (n = 11) expressed willingness to
receive the infusion, 49% (n = 48) were not willing, and 39%
(n = 38) were unsure or maybe willing.
To assess reasons for vaccine uptake, open-ended data
collected on reasons for not receiving the vaccine and media
modalities for obtaining information about the vaccine are
available in Appendix A. The top reason selected for not
receiving the vaccine was mistrust in this specific vaccine
(n = 75), followed by concerns about the vaccine develop-
ment (n = 70), potential side effects (n = 68), doubts about
the vaccine’s effectiveness (n = 53), worries about the vaccine
components (n = 45), time constraints (n = 29), lack of knowl-
edge about the vaccine risks (n = 28), other unlisted reasons
(n = 27), mistrust of science (n = 23), mistrust of vaccines
(n = 20), belief that the vaccine causes the disease itself
(n = 19), a belief in one’s superior immune system that makes
vaccines unnecessary (n = 16), the belief that vaccines cause
autism (n = 10), family opposition to vaccination (n = 8), and
difficulty finding a location to receive the vaccine (n = 1).
Vaccine acceptance scales
To assess which video type could positively enhance vaccine
perceptions, vaccine acceptance scales were administered
(COVID-VAC and Perceptions of Vaccines Scale). The mixed
ANOVA results for comparing COVID-VAC scores, using
vaccine status and video group as the between-subjects fac-
tors and time as the within-subject factor, are presented in
Table 4. The absence of three-way interactions (p > 0.05) sug-
gests that none of the short-term videos effectively increased
vaccine acceptance. Table 5 provides frequency tabulations of
perceptions of vaccine questions, categorized by video group
and vaccination status, at different intervals over time.
Discussion
Main findings of this study
The primary objectives of this study were to (a) identify
the demographic factors associated with COVID-19 vac-
cine compliance in a sample of students attending a rural
university and (b) determine what type of vaccine-related
video information would positively change vaccine per-
ceptions. College students often participate in risky behav-
iors and often make unhealthy lifestyle decisions,36,51 so
investigating factors that may contribute to vaccine hesi-
tancy in this group is of interest. The first research objec-
tive was to assess the demographic factors associated with
vaccine compliance, and an assessment of factors that dif-
fer between vaccinated and unvaccinated groups was con-
ducted. Findings indicated no significant differences in
vaccination status across most demographics or insurance
status; however, differences across vaccination status were
observed for ethnicity, political party, and political ideol-
ogy, as corroborated by the recent literature.40,52,53
Approximately 45% of our sample was composed of par-
ticipants who identified as conservative. A 2022 study
supports our finding that more conservative political ide-
ologies in rural areas contribute to vaccine hesitancy and
that tailored interventions that specifically appeal to the
interests of these groups (and address common miscon-
ceptions) are an avenue to be explored to facilitate over-
coming negative perceptions of the vaccine.54–56 These
findings suggest selective factors may influence vaccina-
tion status, a finding that merits further investigation in
samples of students attending a rural university.
When factors associated with vaccine hesitancy are dis-
covered, encouraging health literacy is a solution. Health lit-
eracy refers to the capacity to seek out, comprehend, assess,
and use health information from diverse communication
channels. It plays a crucial role in recognizing misinforma-
tion, including fake news.57 Montagni and colleagues found
that individuals with a limited ability to distinguish fake
news are more likely to be hesitant to receive the COVID-19
vaccine and educational and political interventions are sug-
gested.57 Similarly, vaccine literacy warrants consideration
when examining the relationships between vaccines and
their uptake.58
Among the participants in this study, 97 individuals
(32.5% of the total sample) reported not being vaccinated
against COVID-19. Additionally, a substantial portion (70%)
of the participants in the study did not have children.
However, the findings revealed a crucial relationship between
COVID-19 vaccination and the vaccination status of partic-
ipants’ children. Specifically, those who had been vaccinated
against COVID-19 were more likely to have vaccinated their
children as well. This trend may suggest that a parent’s vac-
cination status could influence the vaccination of their
children.59
Pertaining to the second research objective to determine
what type of vaccine-related video information would posi-
tively change vaccine perceptions, evaluations of video inter-
ventions were conducted. Previous literature has indicated
that short-term media interventions (using platforms such as
YouTube and TikTok) degrading vaccines can negatively
impact vaccine perceptions and acceptance/uptake.60,61 The
current study data indicate that short-term media educa-
tional video interventions were not enough to positively
enhance vaccine acceptance/uptake in this sample of

4066 a. l. haRRis BOZeR et al.
Table 4. coViD-19 vaccine acceptance survey results by video group and vaccination status.
no vaccine Vaccine main effects interactions
Question
mean
(pre)
Standard
deviation
(pre)
mean
(post)
Standard
deviation
(post)
mean
(pre)
Standard
deviation
(pre)
mean
(post)
Standard
deviation
(post) Status group time
Status*
group
Status*
time
time*
group
3-way
interaction
COVID-19 is a dangerous health threat <.001** 0.793 0.577 0.989 .017* 0.18 0.237
neutral video group 3.36 1.20 3.45 1.15 1.62 0.91 1.71 1.06
education video group 3.29 1.04 3.33 1.02 1.71 0.99 1.52 0.82
effects video group 3.29 1.27 3.52 1.29 1.77 0.98 1.64 0.91
COVID-19 can be prevented by vaccination <.001** 0.467 0.027* 0.868 0.034* 0.822 0.983
neutral video group 4.23 0.76 4.26 0.73 2.29 1.16 2.12 1.14
education video group 4.24 0.77 4.20 0.82 2.24 1.19 2.02 1.13
effects video group 4.33 0.80 4.33 0.73 2.52 1.26 2.31 1.25
The risks of COVID-19 disease are greater
than the risks of the vaccine
<.001** 0.217 0.051 0.243 0.293 0.195 0.398
neutral video group 3.94 1.06 3.84 1.04 1.41 0.68 1.47 0.66
education video group 3.58 1.06 3.58 1.03 1.59 1.06 1.53 0.95
effects video group 4.05 0.87 3.76 0.94 1.79 1.05 1.67 1.02
The COVID-19 vaccines available to me are safe <.001** 0.162 0.286 0.915 0.828 0.229 0.351
neutral video group 3.90 0.79 4.00 0.82 1.70 0.80 1.85 0.90
education video group 3.82 1.05 3.91 0.97 1.72 0.90 1.66 0.79
effects video group 4.10 0.94 4.05 1.07 2.00 0.91 2.00 0.84
I trust that my government is able to deliver
the COVID-19 vaccine to everyone
everywhere in my country, equally
<.001** 0.339 <.001** 0.725 0.774 0.455 0.882
neutral video group 3.61 1.28 3.58 1.29 3.00 1.34 2.92 1.29
education video group 3.47 1.38 3.36 1.33 2.74 1.14 2.59 1.16
effects video group 3.52 1.33 3.38 1.32 3.08 1.22 2.96 1.20
I trust the science behind the COVID-19 vaccines <.001** 0.081 0.111 0.468 0.231 0.030* 0.725
neutral video group 4.26 0.82 4.36 0.76 1.67 0.87 1.65 0.81
education video group 4.22 0.93 4.09 1.04 1.77 1.00 1.63 0.93
effects video group 4.38 0.81 4.38 0.81 2.15 1.11 2.06 0.98
Note. *Statistically significant at p < .05. **Statistically significant at p < .001. Survey items 1–6, based on responses ranging from strongly agree = 1 to strongly disagree = 5.

JOuRnal Of ameRican cOlleGe health 4067
Table 5. Perceptions of vaccines split by group and vaccination status.
neutral group
(n = 97)
education group
(n = 128)
effects group
(n = 73)
not vaccinated
(n = 31)
Vaccinated
(n = 66)
not vaccinated
(n = 45)
Vaccinated
(n = 83)
not vaccinated
(n = 21)
Vaccinated
(n = 52)
Question Pre n/post n Pre n/post n Pre n/post n Pre n/post n Pre n/post n Pre n/post n
When new vaccines are made available to the general public, would you get vaccinated?
agree 0/0 45/46 1/0 56/63 1/1 30/37
unsure 11/12 17/16 23/19 25/19 10/7 19/12
Disagree 20/19 4/4 21/26 2/1 10/13 3/3
Only people who are having underlying medical problems or who are pregnant should be vaccinated.
agree 6/7 2/4 3/2 1/1 1/1 2/1
unsure 7/4 3/2 12/9 11/8 6/3 4/3
Disagree 18/20 61/60 30/34 71/74 14/17 46/48
I will use vaccines if the government recommends them.
agree 0/0 33/32 0/0 37/42 0/0 22/23
unsure 5/7 22/23 6/6 32/27 3/3 24/24
Disagree 26/24 11/11 39/39 14/14 18/18 6/5
I will use vaccines if my doctor recommends it.
agree 3/4 57/58 10/10 74/75 1–2 43/45
unsure 9/11 9/7 15/14 8/6 9/7 9/6
Disagree 19/16 0/1 20/21 1/2 11/12 0/1
Vaccines will protect me from illness
agree 11/8 58/56 20/15 64/71 5/4 45/46
unsure 4/11 5/8 13/15 11/6 5/6 3/1
Disagree 16/12 3/2 12/15 8/6 11/11 4/5
I am concerned about the side effects of vaccines
agree 27/26 24/21 32/38 38/30 17/20 23/23
unsure 1/2 9/11 4/0 15/12 2/1 11/6
Disagree 3/3 33/34 9/7 30/41 2/0 18/23
I am concerned that vaccines have not been tested adequately
agree 29/27 18/18 36/36 15/14 18/20 18/13
unsure 1/4 8/4 4/4 21/18 2/0 7/13
Disagree 1/0 40/44 5/5 47/51 1–1 27/26
Vaccines may lead to illness in some people
agree 29/28 40/39 38/33 54/53 18/17 29/29
unsure 2/3 12/11 6–7 17/7 2–3 8–6
Disagree 0/0 14/16 1–5 12/23 1–1 15/17
Vaccines will stop the spread of illness
agree 7/6 50/53 16/13 61/65 4/4 38/42
unsure 7/9 8/6 11/15 10/8 3/7 5/6
Disagree 17/16 8/7 18/17 12/10 14/10 9/4

4068 a. l. haRRis BOZeR et al.
students attending a rural university, as evidenced by the
lack of interaction between status, group, and time in the
comparison of COVID-VAC scores and the lack of change
over time in the frequency tabulations of the Perceptions of
Vaccines Scale.
What is already known
Approximately 60 million people in the U.S. reside in rural
counties, constituting approximately 20% of the population.23
During the pandemic, disparities in vaccination rates began
to emerge, with rural residents exhibiting lower vaccination
rates since the beginning of 2021.19 Since January 2021,
youth and adults residing in rural areas exhibited lower vac-
cination rates compared to their urban and suburban coun-
terparts.62 Furthermore, the 2021 CDC report uncovered
that 75.4% of the U.S. urban population (≥ 5 years old)
received one dose of the Janssen vaccine or two doses of
Pfizer-BioNTech or Moderna vaccine compared to only
58.5% of the U.S. rural population.19 On the other hand, the
number of weekly COVID-19 cases per 100,000 population
in rural areas began to surpass that of urban areas in
October 2020.63 This inverse relationship between the low
vaccination rates and increasing case rates could potentially
create a critical situation, leading to elevated morbidity and
mortality rates associated with COVID-19 among rural pop-
ulations. The literature suggests that rural dwellers are more
likely to have comorbidities, are older on average, and are
less likely to have health insurance.23 As such, disparities in
the COVID-19 vaccine uptake between urban and rural
areas could hinder progress in ending the pandemic and
future pandemics.8,19,23,64 This further highlights the impor-
tance of prioritizing mitigation efforts in both geographic
areas during large-scale outbreaks.
A national survey conducted during the coronavirus pan-
demic, but before vaccines for COVID-19 were available,
showed that approximately 3 in 10 adults were unsure about
taking the vaccine and 1 in 10 did not intend to be vacci-
nated, indicating that targeted and multipronged efforts would
be needed to increase vaccination rates.65 The literature reveals
conflicting evidence regarding the impact of educational vid-
eos and blogs on vaccine-related attitudes. A prior research
examining (HPV) vaccine perceptions and behaviors among
East Coast University in the US revealed that individuals
exposed to negative blog content viewed the vaccine as less
reliable, developed more unfavorable opinions about it, and
were less likely to get vaccinated. Conversely, exposure to the
positive blog did not change vaccine-related risk perceptions,
attitudes, or intentions.66 This indicates that negative details
regarding vaccine effectiveness and safety have a larger influ-
ence on the vaccine adoption habits of university students
than positive ones.66 In contrast, a different investigation
revealed that Southern US university students exhibited higher
levels of knowledge, perception, and intentions regarding both
video and text-based information for the meningitis B vac-
cine.67 Although vaccine attitudes were studied using
video-based interventions during the pandemic involving a
different study population,68 to our knowledge, this is the first
interventional study conducted among college students during
the pandemic to assess COVID-19 vaccine uptake attitudes
with video messaging intervention.
Limitations
Limitations of the current study include the potential for infor-
mation bias due to the self-report design of the study and the
inability to yield nuanced insights due to the inadequate num-
ber of responses received for variables such as first responder
status, military status, and university affiliation. Since the study
survey was deployed via the internet or in a digital format via
social media, we may have excluded some individuals, espe-
cially rural older adults with limited digital literacy. Only 3%
of our study sample were over the age of 55 years, which
reduces the likelihood of age affecting the study results signifi-
cantly.69 The convenience sampling design of the study may
have compromised the generalizability of our findings to a cer-
tain extent, along with the classification (undergraduate versus
graduate students), as we lacked an item related to this ques-
tionnaire that would allow for comparisons with other vaccine
hesitancy profiles examined at different universities.
Furthermore, like other work investigating vaccine hesitancy in
rural samples,40 our study sample comprised a slightly higher
proportion of white students (75.17%). Our sample also
included Hispanic (9.73%), Black (5.37%), and Asian (1%) stu-
dents. The current university’s demographic composition con-
sists of 64% white, 23% Hispanic or Latino, 8% Black students,
and 1.34% Asian students. The mean age of the sample was
28 years, and while about 55% of participants were aged
18–24 years (55.03%), there were older students in the sample.
This may not reflect the age distribution of all universities.
Future research
Future research could seek to unravel the findings related to
significant differences in ethnicity, political party, and polit-
ical ideology across vaccinated and unvaccinated students.
Elucidating the contingencies that may influence vaccination
status merits further investigation. More comprehensive
studies should be conducted to ascertain the nature and
length of educational videos for creating health promotion
campaigns aimed at addressing vaccine hesitancy, particu-
larly among adolescents and young adults.
Conclusion
Our analysis revealed that short informative videos did not
improve rural university student attitudes toward COVID-19
vaccine acceptance, highlighting a need for more targeted and
extended interventions to increase compliance with novel vac-
cines. Some key factors influencing vaccination, such as eth-
nicity, political party, and ideology, need further validation
through other robust studies among college students. Factors
such as these and others could be uncovered for targeted
interventions to decrease vaccine hesitancy during public
health emergencies such as the COVID-19 pandemic.

JOuRnal Of ameRican cOlleGe health 4069
Conflict of interest disclosure
The authors have no conflicts of interest to report. The authors confirm
that the research presented in this article met the ethical guidelines,
including adherence to the legal requirements, of the USA and received
approval from the Institutional Review Board of Tarleton State
University.
Funding
No funding was used to support this research and/or the preparation
of the manuscript.
ORCID
Amber L. Harris Bozer http://orcid.org/0000-0003-0487-5237
Subi Gandhi http://orcid.org/0000-0003-0269-2082
Dustin C. Edwards http://orcid.org/0000-0001-6409-8041
Data availability statement
Data will be made available on Open Science Framework.
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