Association of Self-Leadership With Acute Stress Responses and Acute Stress Disorders in Chinese Medics During the COVID-19 Pandemic: A Cross-Sectional Study.

Background: The outbreak of the highly infectious coronavirus disease 2019 (COVID-19) renders a huge physical and psychological risk to the public, especially to the medics. Additionally, self-leadership has proven to improve self-efficacy and mediate tension, such as nervousness and depression. Therefore, a cross-sectional survey was conducted to explore the association of self-leadership with acute stress responses (ASRs) and acute stress disorders (ASDs) in medics during the COVID-19 epidemic.
Methods: Self-reported online questionnaires were administered, and 627 participants were finally included. The data were analyzed using the univariate analysis and the logistical regression model to identify whether self-leadership and sociodemographic and epidemic characteristics were associated with mental health, including ASRs and ASDs.
Results: Initially, 790 medics responded. Of these, 627 remained after excluding for invalid questionnaires and those with a substantial amount of missing data. Therefore, the participation validity rate was 79.37%. Frontline medical staff (β = 0.338; p < 0.001), possibility of infection among people around the medic being mild (β = 0.141; p < 0.001), subjective estimation of epidemic duration being 3-6 months (β = 0.074; p < 0.05), self-sets (β = -0.022; p < 0.001), self-punishment (β = 0.229; p < 0.001), belief hypothesis and evaluation (β = -0.147; p < 0.05), and successful foresight (β = 0.105; p < 0.05) were statistically significant with ASRs. Marital status [adjusted odds ratio (AOR) =1.813; 95% CI (1.141, 2.881); p = 0.012], being a frontline worker [AOR = 25.760; 95% CI (14.220, 46.667); p < 0.001], visiting Hubei in the previous 14 days [AOR = 3.656; 95% CI (1.500, 8.911); p = 0.004], self-punishment [AOR = 1.352; 95% CI (1.180, 1.548); p < 0.001], and self-dialogue [AOR = 1.256; 95% CI (11.063, 1.483); p = 0.007] were the risk factors for ASD. Conversely, having frontline medical staff in one's family [AOR = 0.523; 95% CI (0.297, 0.923); p = 0.025], self-sets [AOR = 0.814; 95% CI (0.715, 0.826); p = 0.002], and belief hypothesis and evaluation [AOR = 0.796; 95% CI (0.672, 0.943); p = 0.038] were the protective factors.
Conclusion: The special working environment of the COVID-19 epidemic resulted in ASR and ASD. Notably, findings revealed a positive association between ASR symptoms and frontline medical staff, the subjective estimation of epidemic duration, self-punishment, and successful foresight. Nevertheless, marital status, having visited Hubei in the previous 14 days, and self-dialogue were the risk factors accounting for ASD symptoms. Surprisingly, having frontline medical staff in one's family, self-sets, and belief hypothesis and evaluation had potential benefits for ASD symptoms.

Introduction

The current outbreak of the novel coronavirus disease 2019 (COVID-19) was first discovered in Wuhan, Hubei Province, China at the end of 2019 (1). Highly infectious COVID-19 spreads from person to person, and the general public faces a huge threat of exposure that has physical and psychological implications (2). Due to varying clinical symptoms, patients are advised to isolate differently. As observed in China, for instance, (a) fever clinics at general hospitals admitted patients for early screening, (b) “Fang Cang” hospitals admitted those with mild symptoms, and whose influenza virus nucleic acid test results were negative, and (c) the intensive care unit (ICU) admitted those with obvious respiratory restrictions, and whose influenza virus nucleic acid tested positive for infection (3). While treating patients responsibly, medical workers face significant challenges. This is particularly true for the frontline medical staff, who directly engage with the COVID-19 patients. Along with existing work pressure, they may experience psychological distress, which leads to similar outcomes as those observed in other public emergencies (4). For example, prospective studies of the medical staff involved in the influenza A (H1N1) public health emergency indicated that their negative mental characteristics were heightened and mainly manifested as anxiety, fear, tension, and so forth (5). Further findings reported that they not only were burdened with heavy workloads but also experienced negative emotions (6). These included being away from their families and concern for the infected. Additionally, the affected medics were vulnerable to an ongoing viral epidemic situation that emulated an alarming emotional stressor. This was observed among the medical staff of a fever outpatient department, at a tertiary general hospital, during COVID-19 (6). Thus, it is necessary to study and research the psychological reactions of medics during such special circumstances.

This study is guided by the job demand–resource (JDR) model, suggesting that the work environment has job demands (e.g., workload and physical needs) that exhaust employees' cognitive resources, resulting in physical and mental health problems (e.g., depression, anxiety acute stress responses [ASRs], and acute stress disorders [ASDs]). There are many mental health problems to be correlated with the COVID-19 pandemic for medics, such as depression and anxiety. Anxiety and depression are a long-standing psychological state of patients, but ASR first appears after the stress disaster event, and its typical manifestations include three aspects, namely, consciousness change, behavior change, and mood change. The change in consciousness appeared earliest, mainly manifested as loss, disorientation, not knowing where you are, and being unable to perceive time and things around you clearly. Behavior change is mainly characterized by a significant decrease or increase in behavior and blindness. Additionally, the main manifestations of emotional changes are anxiety, depression, panic, numbness, shock, loss, anger, fear, sadness, despair, guilt, feeling at a loss as to what to do, and unable to deal with sudden disasters. So one of the main variables in this study is the emergency state made by medical staff in the emergency of COVID-19. Simultaneously, there are resources (e.g., psychological and organizational) that contribute to the achievement of work goals and the reduction of job demands. These job resources are associated with physiological and psychological gains that stimulate personal growth, learning, and development (e.g., decreasing ASR and ASD) (7). Therefore, the following was hypothesized:

Hypothesis 1: The special working environment of the COVID-19 epidemic requires medical staff to use a certain amount of physical energy, resulting in mental health problems.

Acute stress response refers to the sufferings that arise from an event during which individual experiences or perceives a death threat to themselves or another, the resulting response being brief, and not being accompanied by other mental symptoms (8). Although low to moderate levels of acute stress can be adaptive, the cumulative effects of chronic exposure to stress lead to negative outcomes (9). Moreover, these stressors can lead to anxiety, depression, ASD, and even posttraumatic stress disorder (PTSD) (10). Accurately determining whether an individual is experiencing ASR and implementing active psychotherapy can help prevent the development of ASD or more debilitating illnesses.

Acute stress disorder is defined as a condition characterized by three or more dissociative symptoms, reexperience of the traumatic incident, and avoidance. These symptoms show up 2 days to 4 weeks post-trauma (11). The quality of medical care services is directly affected by the psychological health of the medical staff. Moreover, poor mental health may compromise an individual's task effectiveness (8) and lead to decreased patient satisfaction, which not only diminishes treatment and rehabilitation but also can easily lead to doctor-patient disputes (12, 13). Therefore, this study also hypothesized the following:

Hypothesis 2: There are a number of sociodemographic variables that either help or hinder healthcare workers' achievement of work goals, impacting their mental health to a certain extent.

Self-leadership is defined as a process characterized by self-motivation and self-guidance. It is also a phase in which an individual accomplishes the self-direction and self-motivation needed to achieve positive behavioral change, including three conceptually distinct strategies: the behavior focus strategy (comprising the self-goal setting, self-observation, self-reward, self-punishment, and the self-cue subscales), the natural reward strategy (comprising the natural return subscale), and the constructive thinking strategy (comprising the self-dialogue, belief hypothesis and evaluation, and the successful foresight subscales). Strategies about constructive thought primarily account for the association of self-leadership with self-efficacy. A randomized controlled trial suggested that self-leadership had explicit impacts on individual efficacy (14), played a vital role in improving group performance, reduced turnover rate, and promoted competitiveness (15). Additionally, medics need prior self-management and self-leadership to communicate and offer medical assistance to patients and cope with all kinds of emergency problems related to various diseases daily, especially during the COVID-19 epidemic. Another study found that self-leadership strategy leads to the achievement of personal work goals (16), while another study identified that behavior-focused strategies are predominantly driven by conscientiousness, and conscientious individuals are disciplined and self-directed as the trait refers to “governing behavior across long timespans” (17). Meanwhile, it could also enhance execution ability. Individuals with strong self-leadership had highly active imaginations, were highly innovative, had lower levels of tension such as nervousness and depression, and improved the quality of their lives (18). Natural reward strategies primarily account for the association of self-leadership with job satisfaction and organizational commitment (17). Thus, the following was also hypothesized:

Hypothesis 3: A number of dimensions of self-leadership have potential benefits for ASR or ASD among medics.

Methods

Participants and Settings

This study included aiding medics who worked in the hospitals of Wuhan as nurses or doctors during the COVID-19 epidemic. They arrived from Shandong, Beijing, Fujian, Hubei, Chongqing, and Henan provinces between January and February 2020. Medical workers with professional certifications who volunteered to participate were also included in this study. The inclusion criteria of this study were as follows: (a) people who have professional certifications, (b) staff in the hospitals of Wuhan during the COVID-19 epidemic, and (c) people who volunteer to participate and sign the informed consent form specifically between January and February 2020. The exclusion criteria were as follows: (a) substantial amount of missing data and (b) being a non-frontline medic. Frontline medical staff was defined as individuals who had direct contact with confirmed or suspected cases of COVID-19 through diagnosis, treatment, nursing, nosocomial infection control, case sample collection, and pathogen detection. This study included only those medics who worked in hospitals as nurses or doctors and treated patients from Shandong, Beijing, Fujian, Hubei, Chongqing, and Henan provinces. The rest of the medics were classified as non-frontline medical staff. Finally, questionnaire responses from a total of 790 medical professionals were initially collected. Of these, 627 remained after excluding invalid questionnaires and those with a substantial amount of missing data. Thus, the participation validity rate was 79.37%, and the attrition rate was 20.63%.

Study Design

A convenience sampling method was employed due to the particularity of infection of the COVID-19 epidemic situation in this cross-sectional survey. All the participants responded to the electronic Stanford Acute Stress Reaction Questionnaire (SASRQ) and the Revised Self-leadership Questionnaire (RSFQ) using questionnaire star or WeChat mini-programs. Electronic informed consent forms were voluntarily signed and provided by the participants prior to that.

Measurements

Sociodemographic and Epidemic Situation

The sociodemographic data included the participants' gender, age, marital status, profession, work experience, and education level. Additionally, respondents disclosed whether they worked as frontline medical staff, whether they had any frontline medical staff in their families, the possibility of their own infection, the possibility of infections of people around them, the epidemic situation in the local context, whether they have visited Hubei last 14 days, and the subjective estimation of epidemic duration.

Stanford Acute Stress Reaction Questionnaire

The SASRQ was used to measure the degree of ASR or ASD in medical staff due to COVID-19. This scale has 30 items categorized into five obviously contrasting symptoms with the following subscales: separated (10), reexperience (6), avoidance (6), hyperarousal (6)„ and impairment in functioning (2). The participants express their degree of experience with a five-point Likert scale ranging from 0 (no experience) to 5 (always experience) (19). The higher the total score, the greater the severity of ASR. As a consequence, a total score of ≥ 40 along with a doctor's diagnosis, which abides by the Diagnostic and Statistical Manual of Mental Disorders-IV (DSM-IV), indicates ASD (20). The Cronbach's alpha for the internal consistency of the SASRQ was 0.80–0.95, indicating appropriate reliability and validity (21). The inclusion criteria of the psychiatric doctor were as follows: (a) having a professional qualification certificate, (b) having experience in diagnosing a psychiatric disorder in a specialized hospital, (c) and volunteering to participate and sign the informed consent form. The exclusion criteria were having < 5 years of working experience. Furthermore, despite the DSM-IV being replaced by DSM-5, the former is still being commonly used by Chinese doctors. Therefore, the DSM-IV was used in this research. The criteria specified in DSM-5 for diagnosing ASD shall be applied in the next research.

Revised Self-Leadership Questionnaire

The RSFQ is a multidimensional self-report questionnaire testing self-leadership. This study employed a Chinese version, translated by Wang Wen (22). This version consisted of 35 items for three conceptually distinct strategies: (1) the behavior focus strategy (comprising self-goal setting, self-observation, self-reward, self-punishment, and self-cue subscales), the natural reward strategy (comprising the natural return subscale), and the constructive thinking strategy (comprising the self-dialogue, belief hypothesis and evaluation, and successful foresight subscales). Responses to the items were measured on a grade of 1–5. The total score ranged from 35 to 175, with higher scores revealing a higher degree of self-leadership. The Cronbach's alpha for internal consistency was 0.963, indicating acceptable reliability and validity (22).

Statistical Analyses

The univariate analysis of ASR in medical staff was checked using the t-statistic and one-way analysis of variance. Additionally, a hierarchical regression analysis was undertaken to identify the explanatory power of the sociodemographic variables, epidemic situation, and self-leadership data variability in relation to the medics' ASR. Unstandardized coefficient (β), standard error (SE), standardization coefficient (β), t-value, p-value, tolerance, and VIF are reported, respectively. Moreover, the binary logistics regression model was computed for the variables that caused ASD in the medical staff. The p-value was deemed statistically significant at p ≤0.05. All the data were analyzed using the IBM SPSS version 22.0 software.

Results

Demographic and Work-Related Characteristics

Table 1 presents the sociodemographic and work-related characteristics of the participants. The majority of the participants were nurses (n = 366; 58.37%), and 41.63% were doctors (n = 261). Most were women (n = 453; 72.25%). Notably, the number of frontline medical staff was 230 (36.68%). Furthermore, there was a statistically significant difference in gender, age, marital status, frontline medical staff worker status, presence or absence of frontline medical worker relative, the possibility of one's own infection, the possible infection of people around oneself, the epidemic in the local context, having visited Hubei in the previous 14 days, and the subjective estimation of the epidemic duration (all p < 0.05).

Table 1

The univariate analysis of the medical staff's ASR due to COVID−19 (n = 627).

Variable		n (%)	SASRQ Scale (Mean ± SD)	T/F	P
Gender	Male	174 (27.75)	23.09 ± 22.89	−1.998	0.046†
	Female	453 (72.25)	27.07 ± 22.13
Age (years)	19~35	341 (54.39)	25.05 ± 21.27	4.057	0.018‡
	36~59	253 (40.35)	28.30 ± 23.98
	≥60	33 (5.26)	17.52 ± 18.42
Marital status	Unmarried	215 (34.29)	21.85 ± 19.46	9.580	0.000‡
	Married	389 (62.04)	27.38 ± 23.26
	Divorced/widowed	23 (3.67)	40.61 ± 24.61
Profession	Nurse	366 (58.37)	25.41 ± 22.46	−0.734	0.464†
	Doctor	261 (41.63)	26.74 ± 22.30
Work experience (years)	≤ 5	277 (44.18)	24.06 ± 20.23	2.677	0.070‡
	6~10	110 (17.54)	29.80 ± 26.12
	>10	240 (38.28)	26.41 ± 22.75
Education level	Below undergraduate	79 (12.60)	26.23 ± 24.275	2.057	0.284‡
	Bachelor's degree	285 (45.45)	27.79 ± 22.84
	Master's degree or above	263 (41.95)	23.92 ± 21.18
Frontline medical staff	Yes	230 (36.68)	39.76 ± 26.43	11.433	<0.001†
	No	397 (63.32)	18.03 ± 14.70
Frontline medical staff in a family	Yes	130 (20.73)	30.31 ± 27.30	2.132	0.034†
	No	497 (79.27)	24.83 ± 20.79
The possibility of one's own infections	None	146 (23.29)	22.01 ± 22.25	12.909	<0.001‡
	Most	343 (54.70)	23.59 ± 19.54
	Some	115 (18.34)	35.71 ± 26.02
	A little	23 (3.67)	37.78 ± 26.78
The possibility of infections of people around oneself	None	88 (14.04)	18.31 ± 20.82	16.266	<0.001‡
	Most	378 (60.28)	23.61 ± 19.51
	Some	146 (23.29)	35.25 ± 26.59
	A little	15 (2.39)	39.93 ± 22.58
The epidemic situation in the local context	Tending to steady	310 (49.44)	23.50 ± 20.56	3.139	0.025‡
	At the peak	59 (9.41)	27.83 ± 23.72
	Rising	137 (21.85)	30.28 ± 24.75
	Uncertainty	121 (19.30)	26.50 ± 22.82
Have visited Hubei last 14 days	Yes	46 (7.34)	38.20 ± 23.51	3.893	<0.001†
	No	581 (92.66)	25.00 ± 22.03
The subjective estimation of epidemic duration (month)	One	224 (35.73)	22.97 ± 21.01	4.280	0.005‡
	Two to three	350 (55.82)	26.77 ± 21.83
	Three to six	43 (6.86)	30.93 ± 28.57
	Half a year	10 (1.59)	43.40 ± 30.29
ASR	-	-	25.97 ± 22.38	-	-
Self-leadership	-	-	124.73 ± 17.90	-	-

p-value of independent samples t-statistic.

p-value of one-way analysis of variance, total dfe = 626.

SASRQ, Stanford Acute Stress Reaction Questionnaire; ASR, acute stress response; COVID−19, coronavirus disease 2019.

Correlation Between the ASR and Self-Leadership in Medics

These results are demonstrated in Table 2. The Pearson's correlation coefficient was used to analyze the relationship between medical personnel's self-leadership and ASR, and statistically significant positive correlations were found between the ASR scores and the self-punishment scores (r = 0.160, p < 0.001). There was, however, a negative correlation to self-sets scores (r = −0.141, p < 0.001), natural return scores (r = −0.084, p < 0.05), and belief hypothesis and evaluation (r = −0.133, p < 0.001).

Table 2

Pearson's coefficient correlations between the ASR and self-leadership in medics.

	1	2	3	4	5	6	7	8	9	10	11	12	13	14	15
1. Total scores of SASRQ	1
2. Dissociation (SARSQ)	0.937**	1
3. Re-experience (SARSQ)	0.918**	0.794**	1
4. Avoidance (SARSQ)	0.892**	0.798**	0.799**	1
5. Hyper-arousal (SARSQ)	0.900**	0.777**	0.812**	0.716**	1
6. Impairment in functioning (SARSQ)	0.739**	0.666**	0.637**	0.585**	0.648**	1
7. Self-sets (RSFQ)	−0.141**	−0.172**	−0.077	−0.133**	−0.111**	−0.102*	1
8. Self-observation (RSFQ)	−0.063	−0.088*	−0.034	−0.074	−0.022	−0.049	0.751**	1
9. Self-reward (RSFQ)	−0.064	−0.075	−0.045	−0.063	−0.055	−0.021	0.528**	0.489**	1
10. Self-punishment (RSFQ)	0.160**	0.131**	0.141**	0.129**	0.200**	0.080*	0.439**	0.545**	0.363**	1
11. Self-cue (RSFQ)	−0.031	−0.037	−0.020	−0.003	−0.050	−0.009	0.526**	0.435**	0.344**	0.274**	1
12. Natural return (RSFQ)	−0.084*	−0.094*	−0.051	−0.068	−0.075	−0.087*	0.780**	0.775**	0.552**	0.527**	0.459**	1
13. Self-dialogue (RSFQ)	0.052	0.039	0.082*	0.043	0.044	0.013	0.584**	0.540**	0.518**	0.424**	0.439**	0.588**	1
14. Belief hypothesis and evaluation (RSFQ)	−0.133**	−0.147**	−0.076	−0.111**	−0.135**	−0.108**	0.763**	0.691**	0.545**	0.467**	0.442**	0.772**	0.599**	1
15. Successful foresight (RSFQ)	0.028	0.020	0.052	0.016	0.035	−0.023	0.666**	0.646**	0.501**	0.472**	0.427**	0.700**	0.640**	0.652**	1
16. Total scores of RSFQ	−0.043	−0.064	−0.006	−0.042	−0.027	−0.049	0.873**	0.840**	0.695**	0.636**	0.594**	0.899**	0.759**	0.852**	0.834**

p < 0.005 (double-tailed),

p < 0.001 (double-tailed), all based on Pearson's coefficient correlations.

SASRQ, Stanford Acute Stress Reaction Questionnaire scale; RSFQ, Revised Self-Leadership Questionnaire; ASR, acute stress response.

Hierarchical Regression Analysis of Influencing Factors of ASR

A hierarchical regression analysis was conducted to explore the predictive power of the sociodemographic variables, epidemic situation, and the self-leadership data on medics' ASR. These results are presented in Table 3. Notably, independent variables of those with statistical differences were included in the first step, from the univariate analysis of the medical staff's ASR due to COVID-19 (as shown in Table 1). The second step entailed entering the nine self-leadership dimensions. The results showed that the selected variables had an impact on the model, and all independent variables could explain 34.9% of the total variation. General data, such as frontline medical worker status (β = 0.338, p < 0.001), the possibility of people around you getting infected being mild (β = 0.141, p < 0.001), and the subjective estimation of the epidemic duration (3–6 months) (β = 0.074, p < 0.05), could explain 25.1% of the total variation. Self-sets (β = −0.022, p < 0.001), self-punishment (β = 0.229, p < 0.001), belief hypothesis and evaluation (β = −0.147, p < 0.05), and successful foresight (β = 0.105, p < 0.05) were statistically significant.

Table 3

Hierarchical regression analysis of influencing factors of ASR in medics (n = 627).

Variables	Unstandardized coefficient (B)	Standard error(SE)	Standardization coefficient (β)	t	P	Tolerance	VIF
Constant	−13.711	7.989		−1.716	0.087
Gender	1.273	1.725	0.025	0.738	0.461	0.909	1.100
Frontline medical staff	18.023	1.747	0.388	10.315	0.000	0.767	1.304
Frontline medical staff in a family	−2.171	1.988	−0.039	−1.092	0.275	0.836	1.197
Have visited Hubei last 14 days	5.405	3.048	0.063	1.773	0.077	0.859	1.164
Age (middle−agedpeople)a	−0.306	1.896	−0.007	−0.162	0.872	0.627	1.594
Age (theelderly)b	−1.743	3.705	−0.017	−0.471	0.638	0.793	1.261
Marital status (married)c	2.994	1.983	0.065	1.510	0.132	0.586	1.707
Marital status (divorced/widowed)d	6.129	4.257	0.052	1.440	0.150	0.847	1.180
The epidemic situation in the local context (atthepeak)e	2.450	2.816	0.032	0.870	0.385	0.803	1.245
The epidemic situation in the local context (rising)f	3.063	2.051	0.057	1.494	0.136	0.756	1.323
The epidemic situation in the local context (uncertainty)g	0.463	2.066	0.008	0.224	0.823	0.816	1.225
The possibility of infections of people around oneself (most)h	2.875	2.234	0.063	1.287	0.199	0.454	2.201
The possibility of infections of people around oneself (some)i	7.486	2.685	0.141	2.788	0.005	0.421	2.374
The possibility of infections of people around oneself (alittle)j	9.346	5.325	0.064	1.755	0.080	0.819	1.220
The subjective estimation of epidemic duration (twoorthreemonths)k	1.308	1.642	0.029	0.796	0.426	0.816	1.226
The subjective estimation of epidemic duration (threeorsixmonths)l	6.505	3.271	0.074	1.989	0.047	0.794	1.259
The subjective estimation of epidemic duration (halfayear)m	10.604	6.179	0.059	1.716	0.087	0.906	1.104
Self-sets	−1.605	0.466	−0.222	−3.446	0.001	0.262	3.816
Self-observation	0.669	0.593	0.068	1.128	0.260	0.302	3.309
Self-reward	−0.419	0.382	−0.048	−1.097	0.273	0.567	1.765
Self-punishment	2.295	0.413	0.229	5.555	0.000	0.636	1.573
Self-cue	0.384	0.516	0.030	0.744	0.457	0.673	1.485
Natural return	−0.481	0.527	−0.061	−0.913	0.362	0.239	4.179
Self-dialogue	0.892	0.463	0.093	1.926	0.055	0.469	2.131
Belief hypothesis and evaluation	−1.393	0.566	−0.147	−2.463	0.014	0.303	3.301
Successful foresight	0.718	0.361	0.105	1.989	0.047	0.386	2.593

The first step: R = 0.521, R .

The second step: R = 0.591, R .

Variable assignment: gender: 0 = male, 1 = female; frontline medical staff: 0 = no, 1 = yes.

Frontline medical staff in family: 0 = no, 1 = yes; Have visited Hubei in the previous 14 days: 0 = no, 1 = yes.

0 = age 19–35 years, 1 = 36–59 years;

0 = age 19–35 years, 1 = ≥60 years.

0 = unmarried, 1 = married.

0 = unmarried, 1 = divorced or widowed.

0 = the local epidemic situation is steadying, 1 = the local epidemic situation is at the peak.

0 = the local epidemic situation is steadying, 1 = the local epidemic situation is rising.

0 = the local epidemic situation is steadying, 1 = the local epidemic situation is uncertain.

0 = there is no possibility that people around you will be infected, 1 = the possibility of people around you getting infected is the most.

0 = there is no possibility that people around you will be infected, 1 = the possibility of people around you getting infected is mild.

0= there is no possibility that people around you will be infected, 1 = the possibility of people around you getting infected is low.

0 = the subjective estimation of epidemic duration is that it will last 1 month, 1 = the subjective estimation of epidemic duration is that it will last 2–3 months.

0 = the subjective estimation of epidemic duration is that it will last 1 month, 1 = the subjective estimation of epidemic duration is that it will last 3–6 months.

0 = the subjective estimation of epidemic duration is that it will last 1 month, 1 = the subjective estimation of epidemic duration is that it will last 3–6 months.

ASR, acute stress response.

Binary Logistics Regression Analysis of Influencing Factors of ASD

Tables 4, 5 demonstrate the results of factors influencing ASD. Markedly, there were 146 participants with ASD, accounting for 23.29% of the included participants (according to the SASRQ and the doctor's diagnosis). The risk factors for ASD were as follows: medics who were married compared with those who were not: [adjusted odds ratio (AOR) =1.813, 95% CI (1.141, 2.881), p = 0.012]; frontline medical staff compared with nonfrontline medical staff [AOR = 25.760, 95% CI (14.220, 46.667), p < 0.001]; and the medical staff who had been to Hubei in the previous 14 days [AOR = 3.656, 95% CI (1.500, 8.911), p = 0.004] compared with those who had not. Furthermore, regarding self-leadership, self-punishment [AOR = 1.352, 95% CI (1.180, 1.548), p < 0.001] and self-dialogue [AOR = 1.256, 95% CI (11.063, 1.483), p = 0.007]were also the risk factors. Conversely, protective factors were having frontline medical staff in the family [AOR = 0.523, 95% CI (0.297, 0.923), p = 0.025], self-sets [AOR = 0.814, 95% CI (0.715, 0.826), p = 0.002], and belief hypothesis and evaluation [AOR = 0.796, 95% CI (0.672, 0.943), p = 0.038].

Table 4

Binary logistics regression analysis of influencing factors of ASD in medics (n = 627).

Variable	ASD		β	Crude odds ratio (95% CI)	P
	Yes	No
	n (%)	n (%)
Gender
Male	116 (79.50)	337 (70.1)	-	1
Female	30 (20.5)	144 (29.9)	0.137	1.146 (0.630, 2.085)	0.654
Age (years)
19~35	70 (47.9)	271 (56.3)	-	1
36~59	74 (50.7)	179 (37.2)	0.963	2.619 (0.434, 15.826)	0.294
≥60	2 (1.4)	31 (6.4)	1.011	2.749 (0.470, 16.083)	0.262
Marital status
Unmarried	31 (21.1)	184 (38.3)	-	1
Married	100 (68.5)	289 (60.1)	−1.772	0.170 (0.041, 0.704)	0.012
Divorced/widowed	15 (10.3)	8 (1.7)	−1.777	0.308 (0.082, 1.154)	-
Frontline medical staff
No	125 (85.6)	104 (21.6)	-	1
Yes	21 (14.4)	377 (78.4)	3.382	29.429 (15.373, 56.336)	0.000
Frontline medical staff in a family
No	39 (26.7)	91 (18.9)	-	1
Yes	107 (73.3)	390 (81.1)	−0.659	0.517 (0.277, 0.965)	0.025
The possibility of one's own infections
None	28 (19.2)	118 (24.5)	-	1
Most	66 (45.2)	277 (57.6)	1.900	6.689 (0.996, 44.929)	0.051
Some	45 (30.8)	70 (14.6)	1.146	3.146 (0.517, 19.148)	0.214
A little	7 (4.8)	16 (3.3)	0.970	2.638 (0.452, 15.390)	0.281
The possibility of infections of people around oneself
None	11 (7.5)	77 (16.0)	-	1
Most	75 (51.4)	303 (63.0)	−1.125	0.325 (0.039, 2.717)	0.299
Some	55 (37.7)	91 (18.9)	−0.245	1.022 (0.112, 5.489)	0.805
A little	5 (3.4)	10 (2.1)	0.208	1.518 (0.181, 8.364)	0.831
The epidemic situation in the local context
Tending to steady	59 (40.4)	251 (52.2)	-	1
At the peak	16 (11.0)	43 (8.9)	−0.230	0.794 (0.398, 1.585)	0.514
Rising	42 (28.8)	95(19.8)	0.145	1.155 (0.353, 3.783)	0.811
Uncertainty	29 (19.9)	92 (19.1)	0.282	1.326 (0.586, 2.997)	0.498
Have visited Hubei last 14 days
No	24 (16.4)	22 (4.6)	-	1
Yes	122 (83.6)	459 (95.4)	1.174	3.235 (1.161, 9.009)	0.025
The subjective estimation of epidemic duration (month)
One	44 (30.1)	180 (37.4)	-	1
Two to three	81 (55.5)	269 (55.9)	−1.313	0.269 (0.036, 2.030)	0.203
Three to six	16 (11.0)	27 (5.6)	−1.283	0.277 (0.038, 2.015)	0.205
Half a year	5 (3.4)	5 (1.0)	0.012	1.102 (0.120, 8.523)	0.991
Self-sets	-	-	−0.252	0.777 (0.660, 0.915)	0.002
Self-observation	-	-	0.051	1.052 (0.858, 1.069)	0.624
Self-reward	-	-	−0.069	0.933 (0.815, 1.069)	0.321
Self-punishment	-	-	0.313	1.368 (1.170, 1.598)	0.000
Self-cue	-	-	0.082	1.085 (0.902, 1.306)	0.387
Natural return	-	-	−0.019	0.981 (0.812, 1.187)	0.846
Self-dialogue	-	-	0.252	1.286 (1.066, 1.553)	0.007
Belief hypothesis and evaluation	-	-	−0.215	0.806 (0.662, 0.982)	0.038
Successful foresight	-	-	0.010	1.010 (0.888, 1.147)	0.884

ASD, acute stress disorder.

Table 5

Binary logistics regression analysis of influencing factors of ASD in medics (n = 627).

Variable	ASD		β	Adjusted odds ratio AOR (95% CI)	P
	Yes	No
	n (%)	n (%)
Married	100 (68.5)	289 (60.1)	0.595	1.813 (1.141, 2.881)	0.012
Frontline medical staff (Yes)	21 (14.4)	377 (78.4)	3.249	25.760 (14.220, 46.667)	0.000
Frontline medical staff in a family (Yes)	107 (73.3)	390 (81.1)	−0.648	0.523 (0.297, 0.923)	0.025
Have visited Hubei last 14 days (Yes)	122 (83.6)	459 (95.4)	1.296	3.656 (1.500, 8.911)	0.004
Self-sets	-	-	−0.206	0.814 (0.715, 0.826)	0.002
Self-punishment	-	-	0.301	1.352 (1.180, 1.548)	0.000
Self-dialogue	-	-	0.228	1.256 (1.063, 1.483)	0.007
Belief hypothesis and evaluation	-	-	−0.228	0.796 (0.672, 0.943)	0.038

ASD, acute stress disorder.

Discussion

Medics' persistent vulnerability to the suffering of others may be related to the destructive outcomes of their psychological distress (23). These outcomes may reasonably be enhanced during a public health emergency, as healthcare professionals engage with patients subjected to various maladies. Notably, the findings of this study demonstrated that compared with the - healthcare workers, the frontline medics were highly likely to experience ASR. Furthermore, medics around people with the potential to be infected were more likely to experience ASR. Additionally, longer durations of the epidemic show a higher probability of ASR.

Specifically, this study demonstrates that self-punishment (positively), successful foresight (positively), self-sets (negatively), and belief hypothesis and evaluation (negatively) were closely related to the ASR of the medical staff. These conclusions were also manifested in the hierarchical regression analysis model. Moreover, self-punishment was defined as a negative process of self-correction that aims for perfect results based on self-leadership. Previous literature has proposed that chronic self-punishment could account for strong feelings of guilt and persistent self-denial, which is not conducive to work efficiency (24, 25). Self-blame, however, contributed to greater stress in a catastrophic manner, for example, if the body is in a state of stress for a long time, it leads to suffering from stress diseases such as gastric ulcers (21, 26). Notably, COVID-19 necessitates that medics offer 24-h monitoring of the infected patients' vital signs and conditions, which is higher intensive attention than what is normative (27). Unfortunately, in cases where there are no improvements, medics might undergo self-punishment, followed by depression and loss of confidence in the work, inducing ASR.

Interestingly, successful foresight was positively correlated with the occurrence of ASR in medical staff. Furthermore, it belongs to constructive thought pattern strategies, referring to imaging the process of one's success before taking action (28). Generally, as the results indicate, if a person could imagine the positive results in advance, it is easier to succeed than when imagining the negative results. In other words, this kind of imagination also produces definite psychological pressure to a certain extent, which can indirectly lead to the occurrence of ASR (29). Additionally, self-sets and belief hypothesis and evaluation were parts of behavior-focused strategies and constructive thought pattern strategies, respectively, and both of them had negative associations with the occurrence of ASR in the medics (30). First, the self-sets enable effective guidance of self-behavior management, and reasonable goals can effectively motivate individuals. In contrast, unrealistic goals could produce a negative effect as well (31). Aspects of evidence signified, distinctly, that medics could set reasonable goals that are challenging but achievable for themselves (32), thereby fostering motivation and enhancing individual performance, effectively preventing the incident of ASR during the COVID-19 treatment (4). On the contrary, belief and hypothesis evaluation refers to the process of analyzing one's immediate thoughts and behavior, including abandoning negative beliefs and assumptions, and establishing positive self-communication and thinking strategies. Conspicuously, the medical staff had successfully developed the ability to provide correct judgments about their individual behavior and thinking in daily work; therefore, this variable is a protective factor for the occurrence of ASR (33).

Meanwhile, the COVID-19 medics not only suffered from heavy work pressure but also responded to the stressors of COVID-19 at the psychological level. According to this cross-sectional survey, approximately 23% of the medics were involved in ASD due to the severity of judgments during the COVID-19 epidemic. According to prior research, 5.3% of examined medical staff had ASD symptoms during war emergencies (34), in line with the previous literature, which revealed that 9% of the survivors of traffic accidents experienced ASD. Patients with ASD symptoms were more likely to develop PTSD than were other individuals (35). Therefore, psychologists should pay attention to the ASD of medical staff and prevent the development of PTSD.

Noticeably, the findings of this study showed that married medics were nearly twice as likely to develop ASD as unmarried ones. The reason might be that married medics faced both work and family stressors (36). Furthermore, the frontline medical workers were at a 25 time higher risk than were non-frontline healthcare workers. In most cases, the patient that was treated by the frontline medic was seriously ill and needed continuous monitoring of their vital signs, further increasing the risk of exposure despite the wearing of protective clothing and goggles, as well as other protective measures that caused physical discomfort (37). Regarding self-leadership, the higher the score of self-punishment and self-dialogue of the medical staff, the more likely it was to lead to ASD. Besides, several protective factors included the frontline medical staff having a family, self-sets, and belief hypothesis and evaluation of ASD. The general public was unable to make correct judgments about the sudden onset of COVID-19. However, it should be noted that if there was a frontline medical worker at home, they could provide family members about the knowledge regarding the dangers of the virus, as well as the precautionary measures to protect themselves through personal experience. Moreover, a strong sense of professional self-identity can prevent ASD development.

Limitations

While these findings are noteworthy, this study has several limitations. First, the exact causality of the cross-section of the study remains to be considered, such as the relationship between medics' self-leadership and ASR. Nevertheless, since the fact-to-face investigation is not allowed owing to the COVID-19 epidemic, the data of this study were mainly collected through subjective self-administered questionnaires; objective indicators to measure the level of ASR can be adopted in future research. Moreover, the sampling method was convenient sampling; therefore, generalization needs caution. Randomization can be adopted in future research to clarify the relationship between the self-leadership of healthcare workers and ASR or ASD.

Interpretation Within the Context of the Wider Literature

In response to the COVID-19 epidemic, healthcare workers (especially frontline workers) shoulder the significant responsibility of treating patients, in addition to work pressure, which may also be compounded by psychological pressure. However, they often focus on patients and ignore their own physical and mental health. Our research showed that healthcare workers had ASR, and more than 20% suffered from ASD. Exclusion of general demographic data, self-sets, self-punishment, belief hypothesis and evaluation, and successful foresight were statistically significant with ASR. Furthermore, risk factors accounting for ASD were self-punishment and self-dialogue. Conversely, self-sets and belief hypothesis and evaluation were the protective factors.

Conclusion

The special working environment of the COVID-19 epidemic resulted in ASR and ASD. Notably, findings revealed a positive association between ASR symptoms and frontline medical staff, the subjective estimation of epidemic duration, self-punishment, and successful foresight. Nevertheless, marital status, having visited Hubei in the previous 14 days, and self-dialogue were the risk factors accounting for ASD symptoms. Surprisingly, having frontline medical staff in one's family, self-sets, and belief hypothesis and evaluation had potential benefits for ASD symptoms.

Data Availability Statement

The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.

Ethics Statement

The studies involving human participants were reviewed and approved by the procedures of this study abided by the Ethics Committee of Shandong Provincial Qianfoshan Hospital, Shandong University (Number [2020] (S517)). The patients/participants provided their written informed consent to participate in this study.

Author Contributions

CX and YJ conceived and supervised the study. RJ and LZ collected and analyzed the data. GL and RW performed and checked the results. The initial and final versions of the manuscript were written and read by all authors.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher's Note

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