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. 2023 Jan 29;10(5):3285–3294. doi: 10.1002/nop2.1580

Stress response and influencing factors among fever outpatients and children's families during normalisation of the COVID‐19 epidemic: A cross‐sectional study

Lifen Lu 1,2, Gui Zheng 1,✉, Jiangqin He 2, Hui Wu 2, Xueqian He 2, Huajun Yu 2, Siying Wu 2, Xiulan Shen 1,✉
PMCID: PMC10077366  PMID: 36710404

Abstract

Aim

To examine the demographic characteristics associated with stress response of fever outpatients and children's families during normalisation of the COVID‐19 epidemic and to examine the relationship between stress response, coping style and resilience.

Design

Online cross‐sectional study.

Methods

A total of 541 fever clinic participants from Yiwu, China, were recruited via WeChat from February to November 2021. Online self‐administered questionnaires were used to collect data. Data were analysed using t‐tests, one‐way analyses of variance, Pearson's correlation analyses and multiple linear regression analyses.

Results

There were apparent physical and emotional responses among the fever outpatients, especially the adult patients. The main coping style was negative coping, and the degree of psychological resilience was low. Income, comorbidities, religious beliefs, tenacity, negative coping and positive coping were independent predictors of stress response.

Keywords: adult patients, children's families, coping style, COVID‐19, fever clinic, psychological resilience, stress response

1. INTRODUCTION

In 2020, many people hoped that the COVID‐19 pandemic would be over quickly and that normal life would resume thereafter, similar to the severe acute respiratory syndrome outbreak in 2003. However, contrary to expectations, the pandemic continues, with new and more infectious variants continuing to increase global infection rates (Manchia et al., 2021). In particular, the emergence of the Delta and Omicron virulent strains has led to stricter restrictions on social interaction. On 2 August 2021, the Chinese government required patients with COVID‐19‐related symptoms, including fever, dry cough, fatigue, loss of the senses of smell and taste, nasal congestion, runny nose, sore throat, conjunctivitis, myalgia and diarrhoea, to complete a nucleic acid test for COVID‐19 in fever clinics and to not leave until a negative test result is obtained (https://www.sohu.com/a/480869517_707859). This meant that patients were forced to stay inside fever clinics and restricted their freedom while waiting for the test results. To better prevent and control the infection, our hospital incorporated the paediatric fever clinic into the adult fever clinic and divided it into an ‘adult area’ and a ‘children's area’ in October 2020. This realised the integrated closed‐loop management of fever clinic patients 10 months ahead of the policy of the Chinese government. Although these measures may be crucial to slow down the spread of COVID‐19, they have the potential to cause psychological imbalance, tension, anxiety and other negative psychological emotions to patients and children's families.

2. BACKGROUND

The global crisis caused by the long‐term prevalence of COVID‐19 has affected the economy, social affairs, the environment and public health (Rahimi et al., 2021), which has led to stress in human life. Stress is considered a psychological expectation that causes worry or the need to adapt when facing changes and may be the result of the interaction between individuals and their environment or experiences (O’Connor et al., 2021). It has been proven to have short‐ and long‐term effects, ranging from the poor performance of stressor tasks in laboratory environments to the development of chronic diseases and mortality in large‐scale epidemiological studies (Crum et al., 2013; Keller et al., 2012; Liu et al., 2017). According to Lazarus (1993), stress is the result of excessive demands that cause discomfort in an individual's physiological, social and spiritual structures. It includes emotional, physical and behavioural responses. A stress reaction is mainly reflected in emotional experience and change, including negative emotions, such as anxiety, grief, or anger (Folkman & Lazarus, 1985). Zakeri et al. (2021) found that emotional disorders and mental health problems can affect employees’ work ability, leading to absenteeism, job hunting, increased accidents and decreased productivity. Somatic reaction refers to defensive and supportive changes in physiological functions, such as blood pressure, heart rate, cortisol level and immune function, which aim to avoid potential harm to the body by stressors (Sapolsky, 1993). Behavioural response refers to the decisions made by individuals when dealing with stressful events, such as actively and orderly performing tasks or stopping personal behaviours when events are difficult to complete (Folkman & Lazarus, 1985). Positive coping mechanisms can help improve an individual's adjustment ability in a stressful situation, while negative coping mechanisms can negatively impact such an ability. As a stressful event, the COVID‐19 pandemic has triggered many changes in human physiology, brain function, psychology and behaviour (Zhao et al., 2021). Resilience is suggested as a protective factor against psychological sequelae and refers to one's ability to respond and recover from adversities (Werneburg et al., 2018). It can be seen as a resource that provides and enhances coping mechanisms and includes the capacity to thrive, rather than merely survive, and to positively adapt to highly stressful environments (Cleary et al., 2018). When dealing with stressful events, some individuals with high psychological resilience can maintain or rapidly restore good mental health, while those with low psychological resilience cannot (Kalisch et al., 2017). At the neural level, resilience has been shown to be associated with functional connectivity between the regions involved in inhibitory control, emotional flexibility and coping (Shi et al., 2019; Spielberg et al., 2015). Different people have different stress reactions, coping styles and levels of psychological resilience.

The purpose of this study is to investigate the demographic characteristics related to stress response of fever outpatients and children's families during normalisation of the COVID‐19 epidemic and the relationship between coping style, resilience and stress response. The study's findings are expected to provide reference for formulating targeted intervention strategies and improving the public's response to public health emergencies.

3. METHODS

3.1. Design and participants

An online cross‐sectional design was used. Participants were recruited among patients and families of children who visited a fever clinic of a general hospital in Yiwu, China, following the convenient sampling method. There were a total of 628 patients recruited, including 330 adult fever outpatients and 298 children's families. The inclusion criteria were as follows: two or more fever outpatient visits within 15 days; age of ≥18 years and investigation in the adult area or age of ≥18 years and investigation in the children area; and ability to correctly understand the questionnaire and clearly express the response. The exclusion criteria were as follows: mental disorders, major traumatic events in the past 6 months and refusal to visit the clinic. Data with a response time of <120 s were rejected, and a total of 87 cases were collected. Among the cases, 541 copies were effectively recovered, and the qualification rate was 86.15%.

3.2. Measurements

The following demographic characteristics of the participants were evaluated: sex, age, BMI, place of residence, marital status, occupation, educational level, income, smoking history, drinking history, payment method, comorbidities, religious beliefs and other characteristics.

The Chinese version of the Stress Response Questionnaire (SRQ) was used for evaluation. The SRQ was prepared in 2005 by Professor Jiang Qianjin (Zhong et al., 2005) of Zhejiang University School of Medicine. The questionnaire contained 28 items and three dimensions, including 12 items on emotional response (SER), 12 items on physical response (SPR), and six items on behavioural response (SBR). The total score of the 28 items represented the degree of stress response. The possible responses to the items were as follows: ‘definitely yes/basically yes/moderately/basically not/definitely not’ and are scored with 5/4/3/2/1 point, respectively. A high score represented a serious stress reaction. The scale has been frequently used in China (Qiu et al., 2021; Wu et al., 2019; Yu & Zhang, 2007). The Cronbach's α in this study was 0.960.

The Chinese version of the Simplified Coping Style Questionnaire (SCSQ) was also used for evaluation. Xie (1998) used the SCSQ, which was prepared on the basis of domestic and foreign theories on coping styles. There were 20 items and two dimensions in the questionnaire. It included 12 items on positive responses and eight items on negative responses. The potential responses to these items were as follows: ‘not taking/occasionally taking/sometimes taking/often taking’ and are scored with 0/1/2/3 points, respectively. The score of coping tendency was calculated using a formula (Zou, 2014). If the score was >0 points, the participant was prompted to take a positive response, and if the score was <0 points, the participant was prompted to take a negative response. The scale has been well used in China (Cai et al., 2021; Fang et al., 2018) and has been shown to effectively reflect people's coping tendencies when they are stimulated by the outside world. The Cronbach's α in this study was 0.88.

The Chinese version of the Connor–Davidson Resilience Scale (CD⁃RISC) was also used for evaluation. The CD⁃RISC was compiled by Connor and Davidson (2003) and translated and revised by Yu and Zhang (2007). There were 25 items and three dimensions in the questionnaire. It included 13 items on tenacity, eight items on strength and four items on optimism. The total score of the 25 items indicated the degree of psychological resilience. The potential responses to the items were as follows: ‘never/rarely/sometimes/often/always’ and were scored with 0/1/2/3/4 points, respectively. A high score indicated good psychological resilience. The scale has been frequently used in China (Chen & Qiu, 2021; Han et al., 2021). The Cronbach's α in this study was 0.94.

3.3. Data collection

An online survey software from mainland China (https://www.wjx.cn) was used to create electronic questionnaires, which were distributed to the participants via WeChat, one of the most widely used social networking tools in China. Data were collected from February to November 2021. Only a single IP address could be used to access and complete the survey. The purpose and significance of the survey were explained to the participants before the survey. The participants were informed that the study would not collect any personal or identifying information; participation was completely voluntary; and whether they were willing to participate would not affect their diagnosis and treatment process. They completed the online questionnaire after providing informed consent.

3.4. Data analysis

SPSS version 26.0 was used for the analysis of data. Descriptive statistical analysis was conducted on the demographic characteristics, stress response, coping style and psychological resilience of the participants. Quantitative variables were presented as means ± standard deviations and categorical variables as frequencies. The t‐test or one‐way analysis of variance was used for comparison between different groups. Pearson's correlation coefficient was used to analyse the correlation between the stress response, coping style and resilience of the participants. A multiple linear regression analysis was performed to analyse the factors influencing the stress response. p‐values of <0.05 were regarded as statistically significant.

3.5. Ethical considerations

This study was approved by the ethics committee of our hospital.

4. RESULTS

A total of 541 participants completed the questionnaire, including 288 adult fever clinic patients and 253 children's families. The average age of the participants was 32.16 ± 7.36 years, and the male‐to‐female nursing student ratio was 53:47 (53% vs. 47%). During normalisation of the COVID‐19 epidemic, the participants’ average stress response score was 61.88 ± 21.25; coping tendency score, −0.03 ± 7.90; and psychological resilience score, 59.97 ± 19.55 (Table 1).

TABLE 1.

The scores of SRQ, SCSQ and CD⁃RISC

Questionnaires Dimensions Average score Total score
Mean SD Mean SD
SRQ SER 2.05 0.82 24.55 9.83
SPR 2.45 0.87 19.61 6.92
SBR 2.24 0.82 13.45 4.93
Total 2.21 0.76 61.88 21.25
SCSQ Positive response 1.86 0.62 22.28 7.46
Negative response 1.22 0.66 9.74 5.26
Coping tendency / / −0.03 7.90
CD⁃RISC Tenacity 2.35 0.87 30.52 11.26
Strength 2.61 0.84 20.87 6.70
Optimism 2.14 0.80 8.58 3.20
Total 2.40 0.78 59.97 19.55
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Abbreviations: CD⁃RISC, Connor‐Davidson Resilience Scale; SBR, behavioural reaction; SCSQ, Simplified Coping Style Questionnaire; SD, standard deviation; SER, emotional reaction; SPR, physical reaction; SRQ, Stress Response Questionnaire.

The participants with a single status (t = 2.089, p = 0.038), low income (F = 5.235, p = 0.001), smoking history (t = 2.37, p = 0.018), complications (t = 2.616, p = 0.014) and religious beliefs (t = 3.054, p = 0.002) had significantly higher SRQ scores than the other participants. There was no significant difference in sex, age, BMI, place of residence, occupation, drinking history and payment method (p > 0.05; Table 2).

TABLE 2.

Demographic characteristics of SRQ (n = 541)

Variables N (%) SRQ [mean (SD)] t/F p
Gender
Males 287 (53%) 60.69 (21.68) −1.379 0.168
Females 254 (47%) 63.22 (20.72)
Ages
0–30 255 (47.1%) 61.78 (22.78) 0.516 0.597
31–40 213 (39.4%) 61.22 (19.64)
40+ 73 (13.5%) 64.14 (20.37)
BMI
<18.5 45 (8.3%) 64.11 (25.89) 1.461 0.224
18.5–23.9 302 (55.8%) 62.20 (20.39)
24.0–27.9 126 (23.3%) 58.81 (19.30)
>28.0 68 (12.6%) 64.68 (24.65)
Address
City 328 (60.6%) 61.24 (21.63) −0.865 0.387
Rural 213 (39.4%) 62.86 (20.67)
Marriage
Single (unmarried, divorced and widowed) 131 (24.2%) 65.62 (24.57) 2.089 0.038
Married 410 (75.8%) 60.68 (19.96)
Children
Yes 270 (49.9%) 61.87 (21.18) −0.004 0.997
No 271 (50.1%) 61.88 (21.36)
Occupation
Student 7 (1.3%) 73.00 (38.38) 1.867 0.115
Worker 98 (18.1%) 64.07 (22.57)
Farmer 21 (3.9%) 63.43 (18.57)
Business enterprise 103 (19.0%) 57.56 (16.14)
Freelance 312 (57.7%) 62.26 (21.87)
Schooling
Primary school 21 (3.9%) 66.86 (19.13) 1.554 0.185
Junior high school 101 (18.7%) 63.04 (22.15)
High school/secondary school 136 (25.1%) 64.39 (20.53)
Undergraduate/college 270 (49.9%) 59.72 (21.35)
Master degree and above 13 (2.4%) 63.31 (20.75)
Income
<1000 6 (1.1%) 85.50 (39.12) 5.235 0.001
1000–3000 68 (12.6%) 67.04 (22.68)
3000–5000 190 (35.1%) 62.79 (21.07)
>5000 277 (51.2%) 59.47 (20.05)
Pay way
Medical insurance 333 (61.6%) 60.61 (20.45) −1.76 0.079
Self‐pay 208 (38.4%) 63.91 (22.37)
Smoke
Yes 120 (22.2%) 65.92 (22.51) 2.37 0.018
No 421 (77.6%) 60.73 (20.76)
Drink alcohol
Yes 215 (39.7%) 63.79 (22.80) 1.699 0.090
No 326 (60.3%) 60.62 (20.10)
Comorbidities
Yes 30 (5.5%) 73.97 (26.32) 2.616 0.014
No 511 (94.5%) 61.17 (20.73)
Religious belief
Yes 77 (14.2%) 68.68 (21.34) 3.054 0.002
No 464 (85.8%) 60.75 (21.05)
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Abbreviations: SD, standard deviation; SRQ, Stress Response Questionnaire.

The SRQ score of the participants ranged from 28 to 135 points. There were significant differences in the SRQ score (t = 8.984, p < 0.01), SPR score (t = 9.821, p < 0.01), SER score (t = 9.617, p < 0.01) and Chinese norm (Jiang, 2004) but no significant differences in the SBR score (p > 0.05; Table 3).

TABLE 3.

Comparison of SRQ and scores of each dimension with Chinese norm

Group N SRQ (mean [SD])
SER SBR SPR Overall
Fever clinic 541 24.55 (9.83) 13.45 (4.93) 19.61 (6.92) 61.88 (21.25)
Chinese norm 1323 20.49 (9.51) 13.20 (5.47) 16.69 (6.81) 53.67 (18.99)
t 9.617 1.193 9.821 8.984
p 0.000 0.233 0.000 0.000
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Abbreviations: EBR, behavioural reaction; SD, standard deviation; SER, emotional reaction; SPR, physical reaction; SRQ, Stress Response Questionnaire.

The scores of the SRQ and all dimensions were negatively correlated with the scores of the CD‐RISC and all dimensions (p < 0.01). The scores of the SRQ and each dimension had different correlations with those of the SCSQ and each dimension. The scores were negatively correlated with positive coping and coping tendency (p < 0.01). Only the SRQ (p < 0.05) and SER scores (p < 0.01) were positively correlated with negative coping, in contrast to the SPR and SBR scores (Table 4).

TABLE 4.

Correlation between SRQ, CD⁃RISC and SCSQ

Questionnaires Dimensions SRQ
SER SPR SBR Total
CD⁃RISC Tenacity −0.471** −0.467** −0.481** −0.496**
Strength −0.455** −0.417** −0.436** −0.465**
Optimism −0.388** −0.410** −0.354** −0.413**
Total −0.491** −0.479** −0.484** −0.513**
SCSQ Positive response −0.429** −0.434** −0.471** −0.469**
Negative response 0.139** 0.039 0.045 0.093*
Coping tendency −0.529** −0.466** −0.506** −0.538**
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Note: ** p < 0.01; * p < 0.05.

Abbreviations: CD⁃RISC, Connor‐Davidson Resilience Scale; SBR, behavioural reaction；SCSQ, Simplified Coping Style Questionnaire; SER, emotional reaction; SPR, physical reaction; SRQ, Stress Response Questionnaire.

The hierarchical multiple regression model was used to analyse the SRQ scores and influencing factors among the fever outpatients and children's families during normalisation of the COVID‐19 epidemic. There was no evidence of significant multicollinearity found in the model. The error range of this model was 0.243–0.997 (>0.10), and the variance expansion factor was 1.003–4.110 (<10). In the first‐step hierarchy with the demographic characteristics, the explanatory power was 6.3% (F = 8.255, p < 0.001). Among the input variables, the positive predictors of the SRQ score were single status, low income, smoking history, complications and religious beliefs. In model 2, the CD⁃RISC score related to the SRQ score was added, and the explanatory power increased by 24.7%, reaching 30.8% (F = 31.093, p < 0.001). Toughness and optimism were also predictive factors of the SRQ score. Finally, the explanatory power of the model improved by 10.2%, reaching 41.0% (F = 38.544, p < 0.001) when the SCSQ score was added to the model. Positive coping and negative coping significantly affected the SQR score (p < 0.001; Table 5).

TABLE 5.

Multiple linear regression analysis of SRQ of participants (n = 541)

Variables B SE β t p 95%Cl
Lower Upper
Model 1
Constant 91.517 6.849 13.363 0.000 78.064 104.970
Marriage −4.960 2.068 −0.100 −2.398 0.017 −9.023 −0.898
Income −4.195 1.214 −0.146 −3.455 0.001 −6.580 −1.810
Smoke −4.714 2.150 −0.092 −2.193 0.029 −8.937 −0.490
Comorbidities 9.438 3.948 0.102 2.391 0.017 1.683 17.193
Religious belief 7.389 2.553 0.122 2.894 0.004 2.373 12.405
Model 2
Constant 115.146 6.133 18.775 0.000 103.098 127.194
Marriage −3.882 1.781 −0.078 −2.179 0.030 −7.381 −0.382
Income −2.119 1.054 −0.074 −2.010 0.045 −4.191 −0.048
Smoke −5.062 1.864 −0.099 −2.716 0.007 −8.723 −1.401
Comorbidities 10.398 3.397 0.112 3.061 0.002 3.724 17.071
Religious belief 7.083 2.196 0.117 3.225 0.001 2.769 11.398
Tenacity −0.686 0.123 −0.363 −5.600 0.000 −0.927 −0.445
Strength −0.230 0.226 −0.072 −1.018 0.309 −0.673 0.214
Optimism −0.718 0.339 −0.108 −2.120 0.034 −1.384 −0.053
Model 3
Constant 113.322 5.919 19.145 0.000 101.694 124.951
Marriage −2.981 1.652 −0.060 −1.805 0.072 −6.226 0.264
Income −1.947 0.975 −0.068 −1.997 0.046 −3.862 −0.031
Smoke −2.901 1.737 −0.057 −1.670 0.095 −6.313 0.511
Comorbidities 8.908 3.141 0.096 2.836 0.005 2.737 15.079
Religious belief 6.194 2.031 0.102 3.050 0.002 2.204 10.184
Tenacity −0.562 0.114 −0.297 −4.905 0.000 −0.786 −0.337
Strength 0.067 0.213 0.021 0.313 0.754 −0.351 0.484
Optimism −0.544 0.318 −0.082 −1.712 0.088 −1.168 0.080
Positive response −1.090 0.116 −0.383 −9.385 0.000 −1.318 −0.862
Negative response 0.907 0.150 0.224 6.035 0.000 0.612 1.202
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Note: Marriage, Income, Smoke, Comorbidities, Comorbidities as control variables included in structural equation model.

Model 1: R 2 = 0.072, adjusted R 2 = 0.063, F = 8.255, p < 0.001.

Model 2: R 2 = 0.319, adjusted R 2 = 0.308, F = 31.093, p < 0.001.

Model 3:R 2 = 0.421, adjusted R 2 = 0.410, F = 38.544, p < 0.001.

Abbreviations: CI, confidence interval; SRQ, Stress Response Questionnaire.

Further analysis showed that there was a significant difference in the SRQ score (t = −3.386, p = 0.001) between the children's families and the adult outpatients. The score of each dimension of the SRQ also significantly differed between them: SPR (t = −2.034, p = 0.042), SBR (t = −2.564, p = 0.011) and SER (t = −4.026, p = 0.000).

The CD‐RISC and SCSQ scores significantly differed only for optimism (t = 2.055, p = 0.040) and coping tendency (t = 2.074, p = 0.039; Table 6).

TABLE 6.

Comparison of SRQ, CD‐RISC and SCSQ in different groups

Variables Children's family (N = 253) Adult (N = 288) t p
(Mean [SD]) (Mean [SD])
SPR 2.37 (0.82) 2.52 (0.90) −2.034 0.042
SBR 2.15 (0.75) 2.33 (0.87) −2.564 0.011
SER 1.90 (0.71) 2.18 (0.88) −4.026 0.000
SRQ 58.65 (18.83) 64.72 (22.83) −3.386 0.001
Strength 2.68 (0.79) 2.55 (0.88) 1.859 0.064
Tenacity 2.38 (0.82) 2.31 (0.91) 0.84 0.401
Optimism 2.22 (0.78) 2.08 (0.81) 2.055 0.040
CD⁃RISC 61.27 (18.30) 58.83 (20.54) 1.460 0.145
Positive response 1.90 (0.59) 1.82 (0.65) 1.536 0.125
Negative response 1.19 (0.67) 1.24 (0.65) −0.773 0.44
Coping tendency 0.72 (7.84) −0.69 (7.91) 2.074 0.039
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Abbreviations: CD⁃RISC, Connor‐Davidson Resilience Scale; SBR, behavioural reaction；SD, standard deviation SER, emotional reaction；SPR, physical reaction; SRQ, Stress Response Questionnaire.

5. DISCUSSION

Although many studies have shown that the prevalence of serious infectious diseases will increase the incidence of mental health problems, it is difficult to find relevant literature or empirical research to evaluate fever outpatients and children's families facing psychological challenges.

In the analysis of the demographic characteristics in this study, the single participants had serious stress responses (Ma et al., 2020; Sabir et al., 2022; Zhang & Ma, 2020), which may be related to the lack of support and care from family members. The high‐income participants could reduce their stress reaction (Galea et al., 2020; Holman et al., 2020; Satici et al., 2021). The low‐income participants were worried about being unable to work normally owing to diseases and epidemics, which could affect their normal life. The smokers showed a greater stress response than the non‐smokers, which may be related to the fever clinic being a ‘smoke‐free area’. When smoking is prohibited, the physiological needs of smokers is not met, resulting in a stronger stress response. Meanwhile, the participants with comorbidities had a high stress response, which may be related to the increased pressure of the presence of long‐term chronic diseases and negative news (e.g., patients with COVID‐19 with comorbidities have a higher mortality rate than their counterparts). The participants with religious beliefs responded more to pressure, which is inconsistent with previous research. For example, during the Ebola outbreak, religion was one of the largest factors that promote mental health (Rabelo et al., 2016). Information on religious beliefs helped improve the mental health of survivors of the H1N1 epidemic (McCauley et al., 2013). Holding religious beliefs was a means for survivors of the Ebola outbreak to regain self‐empowerment and transcend themselves (Matua & Wal, 2015). This finding may be related to the different nationalities and cultural backgrounds of participants and research methods of studies. It may also be related to the social inhibition caused by the long‐term prevalence of COVID‐19, which has restricted believers’ access to religious activities (Kołodziejczyk et al., 2021). Although some studies have shown that sex, age, BMI, occupation and educational level are associated with stress response (Rosi et al., 2021; Sabir et al., 2022; Taylor et al., 2020), our research failed to obtain strong evidence.

After considering other influencing factors, we found that the correlation between marital status, smoking history and stress reaction ‘disappeared’. We believe that the differences between the factors related to stress response in various studies may reflect the complexity of modern human living environments.

The total score of the stress responses among the study participants was higher than the Chinese norm (Jiang, 2004) and that among patients in emergency observation rooms (Huang et al., 2017). There were significant differences between the physical and emotional response scores and the Chinese norm. Patients may feel stressed when facing staff wearing special protective equipment (isolation or protective clothing) or helpless when waiting for the nucleic acid test report. Medical staff should pay more attention to the adjustment of these symptoms of fever outpatients and actively provide increased emotional support. There was no difference between the behavioural response score and the Chinese norm, indicating that the participants had good behaviour control ability.

Furthermore, there was a significant negative correlation between stress response and all dimensions of resilience. Notably, after considering other influencing factors, we noted that the correlation between strength, optimism and stress response ‘disappeared’. Toughness was found as a predictor of stress response. The tenacity level could be improved through effective personalised strategies, and the negative impact of negative psychology could be reduced.

The score of the coping tendency of the participants was −0.03 ± 7.90 (<0), indicating that the participants should adopt negative coping strategies. However, the score was almost zero, which could not prove that the participants negatively responded to pressure. In the analysis, there was a significant positive correlation between stress response and positive coping (p < 0.01) and a significant negative correlation between stress response and negative coping (p < 0.05). Negative coping was the main factor influencing stress response (Zhong et al., 2005), which is different from previous reports. This indicates that COVID‐19 in China has been well‐controlled, leading to generally good coping mechanisms within the population.

We further found that there was a significant difference in the stress response between the adult outpatients and the children's families (p < 0.05). However, in the CD‐RISC and SCSQ, only optimism (t = 2.055, p = 0.040) and coping tendency (t = 2.074, p = 0.039) had significant differences between the groups. The children's families were more optimistic and preferred positive coping, while the adult outpatients preferred negative coping. This is an unexpected result. The age of patients in fever clinics in China is usually ≥14 years. Patients younger than 14 years typically visit paediatric clinics. Our hospital implements a closed‐loop diagnosis and treatment process in a unified treatment area for all fever outpatients, which does not affect the satisfaction of children's families. This is related to the 24‐h paediatric specialist visits arranged in the children's area. There, children can complete a series of diagnosis and treatment while waiting for their nucleic acid test report. For children without special requirements (e.g., experts), satisfaction will not be affected. However, because adult patients may have various specialised diseases, they must wait for their nucleic acid test report before referral, which could prolong the waiting time. The two groups of participants in the study had different medical experiences, leading to significant differences in the data analysis results.

6. LIMITATIONS

This study has some limitations. First, it is difficult to determine any causal relationship between variables through a cross‐sectional study design; therefore, further longitudinal research is required. Second, the small sample size and sampling time may limit the accuracy of the findings. A multi‐centre, large‐sample study is needed in the future to verify the current findings. Third, self‐reporting of answers to the questionnaires may affect the reliability of the findings. Finally, as Yiwu, China, is a small commodity city with a large proportion of high‐income and freelance workers, the results may not be applicable to other regions.

7. CONCLUSION

Our results show that there are apparent physical and emotional responses among fever outpatients, especially adult patients. Complications, religious beliefs, income, tenacity, positive coping and negative coping are predictive factors of stress response. The people‐oriented service concept should be followed, and the problem must be solved based on patients’ perspectives. Managers need to optimise the nucleic acid detection process for COVID‐19, for example, by authorising pre‐examination nurses and self‐help functions and the laboratory to prioritise detection to shorten the nucleic acid detection time. The specialised medical treatment process must be optimised for fever clinic patients. Outpatients must not need to wait after passing the nucleic acid test report for COVID‐19, and various specialised outpatient services must provide free online consultation, video diagnosis and treatment and other measures to transfer stress assessment to the adaptive benefits of stress (Jamieson et al., 2018; Liu et al., 2019). Managers should simultaneously improve the spatial layout of fever clinics (e.g., rebuild spacious and bright indoor and outdoor medical environments). They must also strengthen publicity by posting a simple medical treatment flow chart at the entrance and exit and the waiting area and broadcasting the national epidemic prevention and control policy, medical treatment process, priority rights of fever clinic patients and other measures through television. Medical staff need to introduce the treatment process actively and gently, listen to patients’ complaints, guide appropriate activities inside and outside the home, timely identify people with complications, religious beliefs, and low income, and provide personalised psychological support.

AUTHOR CONTRIBUTIONS

All authors have made substantial contributions to the manuscript to meet criteria for authorship and have reviewed and agreed to the final version. Lifen Lu，Gui Zheng performed the study and the design, led statistical analysis and drafting of the manuscript, and revising it for critically important intellectual content, and approved the final version for submission. Jiangqin He and Hui Wu facilitated data collection, literature, and each made substantial contributions to drafting the manuscript and revising it for critically important intellectual content and approved the final version. Xueqian He, Huajun Yu and Siying Wu the data collection and approved the final version for submission. Xiulan Shen played a decisive role in the overhaul of the paper and approved the submission of the final version. All authors agree that Xiulan Shen is the second corresponding author.

FUNDING INFORMATION

The study was funded by the Department of Education of Zhejiang Province (No: Y202045703).

CONFLICT OF INTEREST

The authors declare that they have no conflict of interest.

ETHICS STATEMENT

This study was approved by the Ethics Committee of the Fourth Affiliated Hospital Zhejiang University School of Medicine (K2021036).

ACKNOWLEDGEMENT

We thank the patients, families and Jiahui Chen, Chunting Zhu project nurses for their contributions.

Lu, L. , Zheng, G. , He, J. , Wu, H. , He, X. , Yu, H. , Wu, S. , & Shen, X. (2023). Stress response and influencing factors among fever outpatients and children's families during normalisation of the COVID‐19 epidemic: A cross‐sectional study. Nursing Open, 10, 3285–3294. 10.1002/nop2.1580

Contributor Information

Gui Zheng, Email: zhenggui1119@zju.edu.cn.

Xiulan Shen, Email: shenxl@zju.edu.cn.

DATA AVAILABILITY STATEMENT

The data that support the findings of this study are available from the corresponding author upon reasonable request.

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Data Availability Statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.

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