| Literature DB >> 35155334 |
Ning Qin1,2, Shuangjiao Shi3, Yinglong Duan4, Guiyue Ma2, Xiao Li2, Zhiying Shen1, Shuhua Zhang4,2, Aijing Luo5, Zhuqing Zhong1,2,5.
Abstract
BACKGROUND: College students are at a high risk of being infected with COVID-19, and they are one of the key population clusters that should be vaccinated. The present study aimed to investigate the knowledge, attitudes, and practices (KAP) toward COVID-19 vaccination among Chinese college students, and to determine the relationships among social media use, eHealth literacy, and KAP toward COVID-19 vaccination among Chinese college students.Entities:
Keywords: COVID-19 vaccination; KAP (knowledge; and practices); attitudes; eHealth literacy; media use; social media
Mesh:
Substances:
Year: 2022 PMID: 35155334 PMCID: PMC8829334 DOI: 10.3389/fpubh.2021.754904
Source DB: PubMed Journal: Front Public Health ISSN: 2296-2565
Socio-demographic characteristics and univariate analysis for KAP toward COVID-19 vaccination.
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| −3.96 | <0.001 | ||
| Male | 2,261 (59.74) | 39.44 ± 5.79 | ||
| Female | 1,524 (40.26) | 40.16 ± 5.23 | ||
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| 3.33 | 0.019 | ||
| 18–20 | 2,173 (57.41) | 39.79 ± 5.65 | ||
| 21–23 | 981 (25.92) | 39.31 ± 5.54 | ||
| 24–26 | 441 (11.65) | 40.19 ± 5.35 | ||
| ≥27 | 190 (5.02) | 40.16 ± 5.41 | ||
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| 3.07 | 0.002 | ||
| Medical | 536 (14.16) | 40.42 ± 5.28 | ||
| Non-Medical | 3,249 (85.84) | 39.62 ± 5.62 | ||
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| 1.43 | 0.240 | ||
| Undergraduate | 2,867 (75.75) | 39.67 ± 5.66 | ||
| Post-Graduate | 728 (19.23) | 39.80 ± 5.28 | ||
| PhD | 190 (5.02) | 40.36 ± 5.44 | ||
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| 23.71 | <0.001 | ||
| Good | 3,129 (82.67) | 40.00 ± 5.56 | ||
| Pretty good | 590 (15.59) | 38.65 ± 5.47 | ||
| Average level, fairly poor, poor | 66 (1.74) | 36.86 ± 4.68 | ||
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| −0.57 | 0.568 | ||
| Yes | 16 (0.42) | 38.94 ± 5.43 | ||
| No | 3769 (99.58) | 39.74 ± 5.58 | ||
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| 3.57 | 0.013 | ||
| Impossible | 793 (20.95) | 40.15 ± 5.67 | ||
| Not likely | 2530 (66.84) | 39.56 ± 5.55 | ||
| Possible | 455 (12.02) | 39.88 ± 5.58 | ||
| Most likely or certainly | 7 (0.18) | 43.71 ± 1.80 |
t, Two-sample t-test; F, one-way analysis of variance.
Social media use, eHealth literacy, and KAP toward COVID-19 vaccination.
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| The score for weekly time spent on social media | 1.82 ± 1.19 |
| The total score for weekly frequencies in using various types of social media platforms | 8.89 ± 3.18 |
| The mean score for weekly frequencies in using official social media | 2.54 ± 1.10 |
| The mean score for weekly frequencies in using medical professional social media | 1.71 ± 1.00 |
| The mean score for weekly frequencies in using the public social media | 2.54 ± 1.17 |
| The mean score for weekly frequencies in using aggregated social media | 2.10 ± 1.08 |
| The total score for eHealth literacy | 46.61 ± 8.16 |
| The mean score for self-perception domain | 3.90 ± 0.75 |
| The mean score for information acquisition domain | 4.02 ± 0.69 |
| The mean score for interactive judgment domain | 3.70 ± 0.80 |
| The total score for KAP toward COVID-19 vaccination | 39.73 ± 5.58 |
| The mean score for knowledge domain | 3.07 ± 0.76 |
| The mean score for attitude domain | 3.39 ± 0.60 |
| The mean score for practice domain | 3.47 ± 0.63 |
M, Mean; SD, standard deviation.
The correlation analysis for social media use, eHealth literacy, and KAP toward COVID-19 vaccination.
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| Weekly time spent on social media | 0.117 | 0.023 | 0.133 | 0.104 |
| Frequencies in using varying social media | 0.168 | 0.017 | 0.144 | 0.215 |
| Official social media | 0.172 | 0.023 | 0.154 | 0.208 |
| Medical professional social media | 0.097 | 0.033 | 0.062 | 0.116 |
| Public social media | 0.132 | 0.008 | 0.122 | 0.167 |
| Aggregated social media | 0.086 | −0.013 | 0.078 | 0.133 |
| Total score for eHealth literacy | 0.377 | 0.007 | 0.382 | 0.467 |
| Self-Perception | 0.358 | 0.000 | 0.377 | 0.437 |
| Information acquisition | 0.383 | 0.014 | 0.391 | 0.462 |
| Interactive judgment | 0.297 | 0.002 | 0.287 | 0.384 |
P < 0.001.
Multiple linear regression analysis on KAP toward COVID-19 vaccination among Chinese college students (N = 3,785).
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| Constant | 26.656 | 0.866 | – | 30.774 | <0.001 |
| Gender | 0.716 | 0.174 | 0.063 | 4.125 | <0.001 |
| Health status | −0.740 | 0.193 | −0.058 | −3.828 | <0.001 |
| Time spent on social media use | 0.208 | 0.073 | 0.044 | 2.845 | 0.004 |
| Frequencies in using official social media | 0.508 | 0.089 | 0.100 | 5.732 | <0.001 |
| Frequencies in using professional medical social media | −0.127 | 0.094 | −0.023 | −1.356 | 0.175 |
| Frequencies in using the public social media | 0.203 | 0.086 | 0.043 | 2.377 | 0.018 |
| Frequencies in using aggregated social | −0.207 | 0.092 | −0.040 | −2.243 | 0.025 |
| Self-Perception of eHealth literacy | 0.333 | 0.066 | 0.135 | 5.064 | <0.001 |
| Information acquisition of eHealth literacy | 0.400 | 0.046 | 0.248 | 8.618 | <0.001 |
| Interactive judgment of eHealth literacy | 0.003 | 0.040 | 0.002 | 0.066 | 0.947 |
F = 62.527, P < 0.001, adjusted R.