Regional Differentiation and Influencing Factor Analysis of Residents’ Psychological Status during the Early Stage of the COVID-19 Pandemic in South China
Abstract
:1. Introduction
2. Methodologies
2.1. Study Settings
2.2. Questionnaire Design
- (1)
- Negative emotion. This dimensional question was set as “Do you feel anxious or depressed in the face of the severity of COVID-19?” (Q1), including 2 options: “yes” and “no”.
- (2)
- Psychological stress. This dimensional question was set as “If the maximum value of psychological stress is 100, what is your level of psychological stress due to COVID-19? (Q2), including 4 options: 0~30, 30~50, 50~80, and 80~100, which represent the stress ranges of very low stress, low stress, high stress, and very high stress, respectively.
- (3)
- Perception change. This dimensional question was set as “How has your perception of ‘human beings and nature in harmony’ changed as compared to before?” (Q3), including 4 options: decreased, no change, increased, and significantly increased.
- (4)
- Psychological crisis. This dimensional question was set as “Will you need psychological counseling when the pandemic is over?” (Q4), including 2 options: “yes” and “no”. If the interviewees chose “yes”, they needed to answer the question: “What kind of psychological counseling would you prefer?” (Q5), choosing at most 3 out of 5 options: “personal self-action psychological counseling”, “personal psychological counseling under professional guidance”, “collective communication counseling with homogeneous groups”, “communication counseling based on society and family relationship”, and “other types of counseling methods such as participating in religious status”.
2.3. Data Collection
2.4. Sample Description
2.5. Methods
2.5.1. Contingency Table and the Chi-Squared Test
2.5.2. Multiple Logistic Regression Analysis
3. Regional Differentiation of Residents’ Psychological Status
3.1. Rural–Urban Differentiation
- (1)
- Negative emotion. Among the valid questionnaires, 61.63% of residents did not experience negative emotions due to the pandemic, and only 38.37% of residents felt anxious or depressed by the pandemic. The proportions of residents with anxiety or depression in the countryside, towns, urban suburbs, and urban centers were 41.01%, 36.50%, 32.01%, and 39.58%, respectively (Figure 2a), showing a “high on both sides, low in the middle” tendency for the countryside/urban centers and town/urban suburbs.
- (2)
- Psychological stress. The proportions of the residents that assessed their level of psychological stress as being very low, low, high, and very high were 18.35%, 27.28%, 32.31%, and 22.03%, respectively, with those considering themselves to be under high and very high stress representing 54.34% of respondents. Thus, over half of the residents were under greater psychological stress. Residents from different geographical areas (countryside, towns, urban suburbs, and urban centers) showed the same distribution characteristics (Figure 2b). In particular, the psychological stress of residents in the countryside was relatively high, with 56.57% being under high and very high stress, followed by the residents of towns, urban suburbs, and urban centers (56.13%, 55.26%, and 51.37%, respectively). This indicated that psychological stress gradually increased from urban centers to the countryside.
- (3)
- Perception change. The outbreak of COVID-19 profoundly affected the “eating wild game animal” behavior of ordinary people, with a reconsideration of the relationship between human beings and nature. The statistical analysis showed that the percentages of interviewees with increased and significantly increased perceptions regarding “human beings and nature in harmony” than before were 25.42% and 52.04%, respectively, accounting for 77.46% of the total interviewees. Only 2.58% and 19.96% of the interviewees showed an attenuation in this perception or no change (Figure 2c). Residents in towns showed the most obvious perception change, with 80.06% of the residents being in increased agreement with the concept of “human beings and nature in harmony”, followed by the residents of the countryside. The proportion that showed a change in perception to increased or significantly increased was 76.36%, a value lower than that of the overall samples, but higher than that for the urban centers and urban suburbs.
- (4)
- Psychological crisis. The statistical analysis showed that 94.30% of the residents in southern China did not experience a psychological crisis (Figure 2d), Only 5.70% of residents needed psychological counseling, and more than 80% of the residents who had a psychological crisis chose to resolve it through personal self-action or communication with the general population, family members, and friends. The proportions of residents in the 4 types of regions that did not require psychological counseling were all over 90%, while the percentages of residents in the countryside and towns who did experience a psychological crisis were 7.07% and 7.67% respectively, values that were 2 and 3 times greater than for those from urban centers and urban suburbs, respectively.
3.2. Inter-Provincial Differentiation
- (1)
- Negative emotions. Residents feeling anxious or depressed accounted for 40.90% of the samples in Hainan Province, a value higher than that reported by residents of the Guangxi Zhuang Autonomous Region (39.66%) and Guangdong Province (35.44%).
- (2)
- Psychological stress. The psychological stress values of the residents in Hainan Province, Guangdong Province, and Guangxi Zhuang Autonomous Region were mainly concentrated in the range of 30~80, and the proportion of the residents under very high stress (80~100) was slightly greater than that relating to very low stress (0~30), indicating there are certain similar distribution characteristics of psychological stress among the provinces. Moreover, the residents of Hainan Province showed a relatively large difference in psychological stress as compared to the other two provinces, with a gap of 6.38% between residents with very high stress and very low stress, followed by the Guangxi Zhuang Autonomous Region (3.84%). Guangdong Province had the smallest gap (1.50%).
- (3)
- Perception change. One of the most fundamental changes caused by COVID-19 was the perception change in the residents. The basic principles of mutual restriction and a harmonious coexistence of “human and nature” were recognized by most residents. The statistical analysis showed that the residents of Hainan Province had the strongest sense of agreement with this perception, with 57.41% selecting the option of “very strong”, followed by residents of the Guangxi Zhuang Autonomous Region (55.44%), and residents of Guangdong Province (45.35%). The percentages of residents that selected the option of “decreased” or “no change” in perception in Guangdong Province, Guangxi Zhuang Autonomous Region, and Hainan Province were 25.97%, 20.47%, and 20.45%, respectively.
- (4)
- Psychological crisis. The survey results show that most of the residents in South China did not experience a psychological crisis. Only 5.70% residents needed psychological counseling. The proportion of the residents needing psychological counseling in Hainan Province was 6.38%, a value slightly higher than that of Guangdong Province (5.71%) and the Guangxi Zhuang Autonomous Region (4.90%).
3.3. Regional Differentiation
- (1)
- Negative emotion. The psychological status of residents in this dimension was relatively prominent, and the cities of Haikou, Guangzhou, Shenzhen, and Nanning showed a high frequency of occurrence regardless of whether the residents generated negative emotions or not. This phenomenon showed diffusion characteristics from the “growth pole” outwards in terms of spatial distribution (Figure 3a). However, the diffusion characteristics were different among the three provinces. The high-frequency areas in Hainan Province gradually extended from Haikou to both sides of the coast, while frequencies in Guangdong Province decreased from Guangzhou to its surrounding areas. The high-frequency areas in the Guangxi Zhuang Autonomous Region extended from Nanning to the areas adjacent to Guangdong Province.
- (2)
- Psychological stress. The psychological stress due to COVID-19 was mainly concentrated in the range of 30~80 in South China. The spatial distribution of the residents took on the characteristic of changing from weak to strong and then weak again with increased levels of psychological stress (Figure 3b). Such cities as Guangzhou, Shenzhen, and Haikou showed high frequencies of occurrence in different ranges of psychological stress. However, the spatial proximity relationship of residents’ psychological stress in other regions was not significant to their provincial capital cities, except for Guangzhou, Shenzhen, Dongguan, Zhuhai, and Huizhou, which showed regional agglomeration. Moreover, the spatial hierarchy differences in psychological stress in the range of 50~80 were the most obvious in South China.
- (3)
- Perception change. Visualizing the survey results regarding the sense of agreement with the perception that “human beings and nature in harmony” (Figure 3c), it can be seen that the stronger the sense of agreement with this perception, the more obvious the distribution characteristics of spatial aggregation. There was no spatial regularity in the spatial distribution of residents with a decreased sense of agreement, while the spatial distribution of the residents with no change or an increased or significantly increased sense of agreement showed a polarization effect centered on their provincial capital cities. For residents with a significantly increased sense of agreement in Hainan Province and the Guangxi Zhuang Autonomous Region there was a point-axis distribution centered on Nanning and Haikou which extended along the axis of the respective surrounding cities, while Guangdong Province showed a gradually weakened extension from the multiple poles of Guangzhou, Shenzhen, and Zhuhai to peripheral areas such as Foshan, Huizhou, and Dongguan.
- (4)
- Psychological crisis. As opposed to negative emotion, the spatial distribution of residents that needed psychological counseling showed a weak correlation with the provincial capital cities, with no characteristics of spatial hierarchy (Figure 3d). Moreover, the 12 cities of Guilin, Nanning, Guigang, Yulin, Guangzhou, Foshan, Dongguan, Shenzhen, Zhuhai, Haikou, Danzhou, and Sanya showed a high frequency of areas with residents who did not experience a psychological crisis. There were only 11 cities that were low-frequency areas in this regard. This indicated that residents of most cities in South China did not experience psychological crisis, in line with the statistical analysis results of the above-mentioned regions.
4. Influencing Factor Analysis of Residents’ Psychological Status
4.1. Model Selection and Testing
- (1)
- Negative emotion. The values of the chi-squared test of variables such as gender, age, fixed income, nature of employment, occupation, and possibility of infection were ≤0.05, indicating that the spatial distribution of negative emotions showed a significant difference in the layers of the above independent variables. However, the spatial distribution of negative emotions was homogenous in the layers of education level and information reliability. Furthermore, the variables of gender and possibility of infection passed the likelihood ratio test and were correlated with negative emotions.
- (2)
- Psychological stress. The spatial distribution of psychological stress showed significant differences in the layers of seven independent variables besides the education level. Moreover, the values of the likelihood ratio tests of gender, information reliability, and possibility of infection were ≤0.05, indicating that they had statistically significant relationships with psychological stress.
- (3)
- Perception change. The values of the chi-squared tests of gender, education level, information reliability, and possibility of infection were ≤0.05, indicating that the spatial distribution of perception change showed significant differences in the layers of the above independent variables. Moreover, the values of the likelihood ratio test of gender, education level, and information reliability passed the likelihood ratio test and were correlated with perception change. However, factors such as age, fixed income, nature of employment, and occupation did not show a statistically significant correlation with perception change.
- (4)
- Psychological crisis. The factors of education level, occupation, information reliability, and possibility of infection passed the chi-squared test and likelihood ratio test, respectively, indicating that the spatial distribution of psychological crisis showed significant differences in the layers of the above independent variables. However, the psychological crisis variables showed a relatively weak correlation with the factors of gender, age, fixed income, and nature of employment.
4.2. Multiple Logistic Regression Analysis
- (1)
- Negative emotion. A significant positive correlation with no negative psychological emotions was shown in males as compared to females, with a coefficient of 0.28, indicating that the ratio of males that had non-negative emotions to those that had negative emotions was 1.32 times that of females. That is, females were more likely to feel anxiety or depression due to the pandemic. The chi-squared test and likelihood ratio test indicated that the possibility of infection factor was related to negative emotions, while the results of regression analysis further showed that the category of possibility of infection showed no differences regarding the impact of negative emotions.
- (2)
- Psychological stress. A significant negative correlation with psychological stress in the range of 30~80 was found in males as compared to females. The incidence of psychological stress in males in the range of 30~50 was 0.61 times that of females, decreasing to 0.57 times in the range of 50~80, indicating that females can withstand greater psychological stress than males in the range of 30~80. However, gender had an indistinguishable influence on psychological stress in the range of 80~100. Besides, the possibility of infection in the residents themselves had a significantly positive correlation with psychological stress, indicating that psychological stress gradually strengthened with the increasing possibility of infection. This was prominently shown in the range of very high psychological stress, indicating that viral infection was the main cause of psychological stress.
- (3)
- Perception change. The incidence values of perception change in males with regard to the relationship between human beings and nature from none, strong, to very strong were 0.49 times, 0.48 times, and 0.33 times those of females, respectively, indicating that the sense of agreement with this perception in females was stronger than that in males. The gender gap gradually strengthened with the increased sense of agreement. In terms of information reliability, there was a significant negative correlation with unchanged perceptions when the residents believed that the pandemic information was 100% true. However, the significant relationship changed from negative to positive and gradually increased along with the decrease in information reliability during the pandemic. For example, when the information reliability decreased to 80~100%, the incidence of those with a increased and significantly increased sense of agreement with this concept was 15.05 and 9.03 times that of those with a decreased sense of agreement, respectively, indicating that the sense of agreement regarding the relationship between human beings and nature increased with decreased information reliability. Compared with high-level education, residents with elementary school-level education or below were significantly and positively correlated with no change and significantly increased of perception change, indicating an obvious polarized phenomenon of percaption change among the resicents with low-level education.
- (4)
- Psychological crisis. The incidence of no psychological crises in residents with a junior high school and senior high school-level education was 0.36 times and 0.42 times that of residents with a college degree-level education or above, respectively, indicating that those with a college degree or above level of education were more likely to experience a psychological crisis and need psychological counseling. Therefore, residents with an elementary school-level education or below were significantly less likely to experience a psychological crisis. Residents who believed that they had no, little, or an uncertain possibility of infection were more likely to not experience psychological crises. Furthermore, students were significantly less likely to experience a psychological crisis as compared to those of other occupations.
5. Discussion
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Variable Name | Variable Assignment | N | % |
---|---|---|---|
Gender | 1 = male | 684 | 41.01 |
2 = female | 984 | 58.99 | |
Age | 1 = under 20 years old | 272 | 16.31 |
2 = 20~29 years old | 728 | 43.65 | |
3 = 30~39 years old | 401 | 24.04 | |
4 = 40~49 years old | 195 | 11.69 | |
5 = 50~59 years old | 58 | 3.48 | |
6 = over 60 years old | 14 | 0.84 | |
Residence | 1 = countryside | 495 | 29.68 |
2 = town | 326 | 19.54 | |
3 = urban suburb | 228 | 13.67 | |
4 = urban center | 619 | 37.11 | |
Education level | 1 = primary school and below | 5 | 0.30 |
2 = junior high school | 43 | 2.58 | |
3 = senior high school or technical secondary school | 114 | 6.83 | |
4 = junior college and above | 1506 | 90.29 | |
Possibility of infection | 1 = none | 316 | 18.94 |
2 = low | 571 | 34.23 | |
3 = no idea | 525 | 31.47 | |
4 = high | 254 | 15.23 | |
5 = very high | 2 | 0.12 | |
Fixed income | 1 = yes | 858 | 51.44 |
2 = no | 810 | 48.56 | |
Nature of employment | 1 = no fixed unit | 106 | 6.35 |
2 = public sector | 619 | 37.11 | |
3 = private sector | 323 | 19.36 | |
4 = no job | 620 | 37.17 | |
Occupation | 1 = employees of enterprises and institutions | 582 | 34.89 |
2 = middle-level and above leading cadres | 147 | 8.81 | |
3 = entrepreneurs | 59 | 3.54 | |
4 = students | 709 | 42.51 | |
5 = farmers | 12 | 0.72 | |
6 = retires | 17 | 1.02 | |
7 = others | 142 | 8.51 | |
Information reliability | 1 = 100% | 232 | 13.91 |
2 = 80~100% | 817 | 48.98 | |
3 = 60~80% | 526 | 31.53 | |
4 = 40~60% | 73 | 4.38 | |
5 = 20~40% | 14 | 0.84 | |
6 = under 20% | 6 | 0.36 |
Dimensions | Options | Hainan Province | Guangdong Province | Guangxi Zhuang Autonomous Region |
---|---|---|---|---|
Negative emotion | Yes | 40.90 | 35.44 | 39.66 |
No | 59.10 | 64.56 | 60.34 | |
Psychological stress | 0~30 | 16.32 | 20.27 | 17.91 |
30~50 | 28.52 | 27.48 | 25.59 | |
50~80 | 32.46 | 30.48 | 34.75 | |
80~100 | 22.70 | 21.77 | 21.75 | |
Perception change | Decrease | 2.63 | 3.00 | 1.92 |
No change | 17.82 | 22.97 | 18.55 | |
Increase | 22.14 | 28.68 | 24.09 | |
Significant increase | 57.41 | 45.35 | 55.44 | |
Psychological crisis | Yes | 6.38 | 5.71 | 4.90 |
No | 93.62 | 94.29 | 95.10 |
Variables | Negative Emotion(Q1) | Psychological Stress(Q2) | Perception Change(Q3) | Psychological Crisis(Q4) | ||||
---|---|---|---|---|---|---|---|---|
χ2 | LR | χ2 | LR | χ2 | LR | χ2 | LR | |
Gender | 0.004 | 0.005 | 0 | 0 | 0 | 0 | 0.513 | 0.789 |
Age | 0.009 | 0.436 | 0 | 0.179 | 0.183 | 0.43 | 0.834 | 0.62 |
Education level | 0.174 | 0.534 | 0.139 | 0.109 | 0.003 | 0 | 0 | 0.024 |
Fixed income | 0.022 | 0.129 | 0.002 | 0.704 | 0.424 | 0.856 | 0.811 | 0.417 |
Nature of employment | 0.017 | 0.795 | 0.021 | 0.533 | 0.1 | 0.329 | 0.491 | 0.93 |
Occupation | 0.001 | 0.215 | 0.005 | 0.687 | 0.201 | 0.527 | 0 | 0 |
Information reliability | 0.389 | 0.262 | 0 | 0.006 | 0 | 0 | 0 | 0.05 |
Likelihood of infection | 0 | 0 | 0 | 0 | 0 | 0.133 | 0 | 0.002 |
Variable | Category | Negative Emotion(Q1) | Psychological Stress(Q2) | ||||||
---|---|---|---|---|---|---|---|---|---|
No | 30~50 (Low) | 50~80 (High) | 80~100 (Very High) | ||||||
B | Exp(B) | B | Exp(B) | B | Exp(B) | B | Exp(B) | ||
Gender | 1 | 0.279 * | 1.322 | −0.492 * | 0.611 | −0.560 * | 0.571 | −0.241 | 0.786 |
Possibility of Infection | 1 | 1.12 | 3.066 | 17.543 * | 4.15 × 107 | 17.418 * | 3.67 × 107 | −0.607 | 0.545 |
2 | 1.09 | 2.975 | 17.984 * | 6.46 × 107 | 17.957 * | 6.29 × 107 | −0.715 | 0.489 | |
3 | 0.435 | 1.546 | 18.494 * | 1.08 × 108 | 18.675 * | 1.29 × 108 | −0.569 | 0.566 | |
4 | −0.014 | 0.986 | 18.100 * | 7.26 × 107 | 18.810 * | 1.48 × 108 | −0.316 | 0.729 | |
Information reliability | 1 | −0.501 | 0.606 | −0.4 | 0.67 | −0.541 | 0.582 | ||
2 | 0.927 | 2.527 | 0.981 | 2.666 | 0.665 | 1.944 | |||
3 | 0.865 | 2.374 | 0.751 | 2.119 | 0.367 | 1.443 | |||
4 | 1.079 | 2.941 | 0.774 | 2.167 | 0.401 | 1.494 | |||
5 | −0.023 | 0.978 | 0.383 | 1.466 | −0.349 | 0.705 | |||
Variable | Category | Perception Change(Q3) | Psychological Crisis(Q4) | ||||||
No change | Increased | Significantly increased | No | ||||||
B | Exp(B) | B | Exp(B) | B | Exp(B) | B | Exp(B) | ||
Gender | 1 | −0.708 * | 0.493 | −0.733 * | 0.48 | −1.124 * | 0.229 | ||
Information reliability | 1 | −2.335 * | 0.097 | −0.098 | 0.907 | −0.835 | 0.434 | −0.369 | 0.691 |
2 | 1.196 * | 3.307 | 2.711 * | 15.048 | 2.201 * | 9.032 | 0.638 | 1.892 | |
3 | 1.462 * | 4.313 | 3.218 * | 24.977 | 2.489 * | 12.053 | 1.058 | 2.882 | |
4 | 2.278 * | 9.754 | 3.181 * | 24.068 | 2.869 * | 17.617 | 0.714 | 2.041 | |
5 | 0.343 | 1.409 | 0.978 | 2.658 | 0.911 | 2.488 | 0.099 | 1.104 | |
Education level | 1 | 18.533 * | 1.12 × 108 | −0.449 | 0.639 | 16.303 * | 1.20 × 107 | 17.442 * | 3.76 × 107 |
2 | 0.225 | 1.252 | −0.726 | 0.484 | −0.724 | 0.485 | −1.032 * | 0.356 | |
3 | −0.255 | 0.775 | −0.635 | 0.53 | −0.428 | 0.652 | −0.870 * | 0.419 | |
Risk perception | 1 | 2.604 * | 13.511 | ||||||
2 | 3.008 * | 20.242 | |||||||
3 | 2.709 * | 15.013 | |||||||
4 | 1.026 | 2.789 | |||||||
Occupation | 1 | 0.541 | 1.717 | ||||||
2 | 0.347 | 1.415 | |||||||
3 | 0.835 | 2.305 | |||||||
4 | 0.729 * | 2.073 | |||||||
5 | −1.003 | 0.367 | |||||||
6 | 18.204 | 8.05 × 107 |
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Cheng, Y.; Chen, Y.; Xue, B.; Zhang, J. Regional Differentiation and Influencing Factor Analysis of Residents’ Psychological Status during the Early Stage of the COVID-19 Pandemic in South China. Int. J. Environ. Res. Public Health 2021, 18, 11995. https://doi.org/10.3390/ijerph182211995
Cheng Y, Chen Y, Xue B, Zhang J. Regional Differentiation and Influencing Factor Analysis of Residents’ Psychological Status during the Early Stage of the COVID-19 Pandemic in South China. International Journal of Environmental Research and Public Health. 2021; 18(22):11995. https://doi.org/10.3390/ijerph182211995
Chicago/Turabian StyleCheng, Yeqing, Yan Chen, Bing Xue, and Jinping Zhang. 2021. "Regional Differentiation and Influencing Factor Analysis of Residents’ Psychological Status during the Early Stage of the COVID-19 Pandemic in South China" International Journal of Environmental Research and Public Health 18, no. 22: 11995. https://doi.org/10.3390/ijerph182211995