Our study showed that the stroke educational program indeed could improve
college students’ knowledge towards stroke pathophysiology, risk factors, warning
symptoms and action to take when a stroke was suspected, same as other studies.4-5, 12
Regardless of the type of assessment questionnaire, mean score of both classes
improved immediately after the educational program. Overall, such educational
program design successfully conveyed message to the students.
seemed extremely well in class A (close-ended questionnaire): all students in class A
could recognize three or more risk factors and at least one warning symptom. A
study in Brazil found that up to 38% of respondents were unable to identify any risk
factors and 41% any symptoms.13 Müller-Nordhorn J et al. reported that 32% of
respondents in Berlin were not able to name one stroke risk factor.5 In northwest
India, Pandian et al concluded that 23% of the subjects did not know a single warning
symptom, and 21% couldn’t identify even a single risk factor.4 However, the
aforementioned studies all used open-ended questionnaire for assessment. If we
checked the results of class B (open-ended questionnaire) for equal comparison, the
awareness/knowledge level of our subjects was still higher than that of other studies.
There was only 5% of students couldn’t name a single risk factor and 24% did not
know any warning symptom. Any attempt on cross-study comparisons has to be
done carefully since studies generally adopted different questionnaire or survey
methods.14
The original awareness status of students was presented in the pretest results, and
such information is important for goal setting of future educational program/campaign.
Reconsidering what we have done, it seems better to design the program/campaign
based on pretest results. For instance, “genetic factors” was included in the
nonmodifiable risk factors in ASA/AHA 2006 guideline,2 but we did not introduce it
during the educational program. The recognition rate of it then dropped after the
program, somehow to our surprise. Another example was cigarette smoking: its
recognition rate was the lowest in both classes during pretest. This may imply that
college students were not alert to smoking behavior in their daily life. Besides, 4%
of students in class B replied that “bloodletting” as an emergency treatment to stroke
and which is merely a myth.15 There were also students in class B replying weather,
stress, mood, alcohol intake and family history as risk factors, yet whether they are
risk factors were still controversial.16-30 All information collected during pretest
could help identify the deficiency or myths the students have and permit more
targeted education efforts.
Mean score of male students was not significantly different from that of female
students except in the posttest of class B (p<0.05). However, sample size was too
small (34 male and 22 female in class A; 12 male and 41 female in class B) for us to
assert that gender was a factor that has influence on the learning performance.
The coding rule may affect the evaluation of educational effects, so does the
questionnaire design. The overall average score of class A was higher than that of
class B. This might due to the fact that close-ended questionnaire somehow
reminded students of the message introduced on the slides. Moreover, we did not
so was to avoid underestimating the learning effect. If we coded these two risk
factors as correct, the mean score of posttest would be lower than present data since
the recognition rate of them dropped after the intervention. As a matter of fact, these
two risk factors were listed on the “how to prevent a stroke” section of the slide
instead of “risk factors” one. And since we did not measure students’ attitude
towards how to prevent stroke, we may not assert that students were not aware that
they can reduce risk of stroke through dietary control and physical exercise. This
was why we kept them neutral to not dilute the education effect of the other listed risk
factors.
Mean score of both classes declined in 12 weeks follow-up. This result implied
that education effect faded away with time as expected. Another study showed the
same tendency.9 How to maintain students’ knowledge of stroke was therefore
crucial, otherwise such education attempt was just a waste of time and resources. It
seems that we have to take action to reinforce students’ awareness. Observing the
world of advertising, there appears to be three underlying principles for getting
messages out to people, and ensuring messages to be remembered. They are (1)
consistency, (2) simplicity, and (3) repetition.31 In our educational program design,
we did arrange and list the key points of risk factors and warning symptoms on the
slide to simplify the message. The trade-off between simple and comprehensive
message came into question.32-33 To evaluate the repetition effect or how long
should the interval be still await further investigation.
Gaps usually exist between knowing and doing. There were several developed
models about individual health behavior discussing how to stimulate positive behavior
changes, such as Health Belief Model (HBM), Theory of Reasoned Action (TRA),
and the Transtheoretical Model (TTM).34 Individual perception/awareness precedes
their behavioral change. Our study provides evidence that through such educational
program, we may improve students’ perception/awareness. Based on the finding of
this pilot study, we may further apply aforementioned model to enhance behavioral
change.
This study has limitations. We took convenient samples instead of randomized
ones. The results may only be representative for participants with college education
in rural area. Also, due to insufficient samples, we didn’t adjust the results by
individual characteristics other than genders to avoid statistical bias. Care must be
taken to generalize these results to other populations.
In conclusion, there were still gaps of stroke knowledge among college students
to fill. A well-designed educational program, which takes into account the primary
awareness status of the targeted audience, may be an effective way to bridge the gap.
Therefore, conducting a large scale research to identify the deficiency of stroke
knowledge of targeted population for further educational program/campaign design
seems worthwhile.
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*All the numbers in this figure means number of students.
Figure 1. Flow of Students through the Study.
Class A (Close-Ended)
Table 1. Stroke Related Knowledge Named by Students in Class A. (n=56) Frequency (%)
Pretest Posttest 12 week follow-up
p value*
I.Where a stroke occurs-brain 46(82) 56(100) 55(98) 0.47 II Risk factors
Numbness or lack of feeling(on one side of the body)
*p value was from comparison of mean score of pretest between male and female participants by χ2 test.
Table 2. Comparison of Mean Score by Gender for Class A. (n=56)
Mean Score (Standard Deviation)
Gender
Frequency (%)
Pretest Posttest
12 weeks follow-up
Male 34(61) 15.0(1.98) 19.2(0.88) 17.5(1.44) Female 22(39) 14.5(1.57) 19.1(1.17) 17.2(1.37) Total 56(100) 14.8(1.83) 19.2(0.99) 17.4(1.41)
p value* 0.1563 0.4003 0.1840
*Student’s t test results of the equality of mean score between male and female participants.
Comparison of Mean Score (Class A)
8 9 10 11 12 13 14 15 16 17 18 19 20
pretest posttest 12 week follow-up Evaluation
Mean Score male
female
Figure 2. Comparison of Mean Score for Class A (tested by close-ended questionnaire)
Table 3. Stroke Related Knowledge Named by Students in Class B. (n=53) Frequency (%)
Pretest Posttest 12 week
follow-up
p value*
I.Where a stroke occurs-brain 42(80) 53(100) 52(98) 0.75 II Risk factors
Cardiopathy 13(25) 47(89) 37(70) 0.47
Cigarette smoking 6(11) 49(92) 34(64) 0.09
Hypertension 27(51) 53(100) 43(81) 0.47
High blood cholesterol level 17(32) 46(87) 20(38) 0.55
Obesity 14(26) 39(74) 34(64) 0.38
feeling(on one side of the body)
21(40) 45(85) 41(77) 0.87
Bloodletting 2(4) 0(0) 0(0) 0.35
*p value was from comparison of mean score of pretest between male and female participants by χ2 test.
Table 4 Comparison of Mean Score by Gender for class B. (n=53)
Mean Score (Standard Deviation)
Gender
Frequency (%)
Pretest Posttest
12 weeks follow-up
Male 12(23) 8.9(1.80) 17.8(1.60) 13.8(2.34) Female 41(77) 9.0(1.75) 18.9(1.13) 15.0(2.55)
Total 53(100) 8.9(1.75) 18.7(1.33) 14.7(2.54)
p value* 0.4034 0.0029 0.0640
*Student’s t test results of the equality of mean score between male and female participants.
Comparison of Mean Score (Class B)
8 9 10 11 12 13 14 15 16 17 18 19 20
pretest posttest 12 week follow-up Evaluation
Mean Score male
female
Figure 3. Comparison of Mean Score for Class B (tested by open-ended questionnaire)