Evaluation of the relationship between the fear of COVID-19 and mental status of female employees during the COVID-19 pandemic

Abstract

BACKGROUND:

The stress due to the COVID-19 pandemic has influenced the physical, mental, and social well-being of humans worldwide.

OBJECTIVE:

To evaluate the relationship between the fear of COVID-19 and mental state of female employees during the COVID-19 pandemic.

METHODS:

A cross-sectional study was conducted involving 726 participants. Data was collected using the Personal Information Form, fear of COVID-19 scale, and brief symptom inventory.

RESULTS:

The fear experienced by women during the social isolation and quarantine period was associated with depression, anxiety, somatization, obsessive-compulsive disorder, interpersonal sensitivity, hostility, phobic anxiety and paranoid experiences.

CONCLUSION:

Women, young people, the elderly and single individuals were most likely affected psychologically during the pandemic. Thus, interventions and psychological evaluations are recommended at an early stage to minimize this effect. Such interventions must be implemented considering the strategic planning and coordination of risk groups.

Keywords

Fear coronavirus psychology female employees

1 Introduction

According to reports from the World Health Organization (WHO) during the COVID-19 pandemic, measures taken have isolated people around the world and adversely affected them, in addition to over 1.8 million confirmed cases and 5,745,032 deaths [1, 2]. The stress due to the COVID-19 pandemic has affected the physical, mental, and social well-being of humans worldwide [3]. Some studies demonstrate that people who have experienced disasters, financial losses, and insecurity have suffered greatly [4, 5].

Recently, the scientific world has gained interest in exploring the impact of pandemics on mental health [6, 7]. When compared to natural disasters, major epidemics last longer and have more negative impacts on human mental health. The fear of COVID-19 is the leading mental problem in the pandemic [8]. However, this fear can be harmful if it becomes chronic or unbalanced, and can cause many psychological problems. Fear of disease rather than the disease itself is often the primary stress associated with a pandemic [9]. This fear is directly associated with the morbidity and mortality of the disease, apart from its rapid spread.

Emotional experiences in stressful conditions are affected by sex differences. Women generally experience more emotional and anxiety disorders. Anxiety disorders are characterized by anxiety or fear in response to a perceived threat [10]. COVID-19 changed the daily habits and stress levels of women. Fear is an emotion which is closely related to anxiety [11]. A recent review demonstrated that the quarantine caused many psychological and emotional disorders such as stress, anxiety, depression, fear, confusion, frustration, and boredom. During the COVID-19 pandemic, women experience more severe psychological disorders such as anxiety, depression and posttraumatic stress [12 –14]. The impact of COVID-19 on mental health varies with demographic parameters. Previous studies have shown that pandemics have a profound impact on the health of women and children [13, 15]. Although many studies have assessed the mental health status of the general population and high-risk groups (such as healthcare workers) during the COVID-19 pandemic [16 –18], there are few studies on women’s mental health. To the best of our knowledge, no study has evaluated the relationship between COVID-19 and mental health in women. Previous studies focusing on fear and risk in relation to epidemics and pandemics provide a guide for examining the current COVID-19 crisis in terms of fear, anxiety, threat, and their effect of mental health [19, 20]. Thus, we sought to examine the effect of this fear fueled by the rapid spread of COVID-19, the wide scope and long duration of the pandemic. More specifically, we sought to examine three interrelated questions:

What are the effects of the pandemic on the mental status of female employees in Turkey?

What is the level of fear caused by the disease among female employees in Turkey?

Is the concept of fear related to COVID-19 effective in mental health?

2 Materials and methods

2.1 Study design and setting

We performed a descriptive study to evaluate the relationship between the fear of COVID-19 and mental status of female employees from January to March 2022. We included 726 females in the study. The inclusion criteria were females who self-identified as female, who had Internet access via a safe computer/device, spoke Turkish, and possessed a job. Women below the age of 20 were also excluded due to the specific developmental needs of adolescence.

2.2 Data collection tools

These included the personal information form, fear of COVID-19 scale, and brief symptom inventory.

2.2.1 Personal information form

It consists of ten questions on sociodemographic characteristics such as age, gender, education level, and is developed from the literature [1 , 21].

2.2.2 Fear of COVID-19 scale

The scale developed by Ahorsu et al. and adapted by Satıcı, Göçet-Tekin, Deniz and Satıcı has been used to determine the fear experienced by the participants due to COVID-19. It consisted of a four-point Likert scale, seven items, and one dimension [22, 23]. It involves assertions on psychological and physiological responses to fear of COVID-19 (For example, I am very afraid of COVID-19, I am afraid of losing my life due to COVID-19, I cannot sleep because of the fear that I will contract COVID-19). The increase in the scores shows that the fear of COVID-19 is high. There is no reversing item in the scale. The total score obtained from the scale indicates the level of fear of COVID-19 experienced by the individual. The scores varied between 7 and 35. A high score on the scale means a high level of fear of COVID-19.

The Cronbach’s Alpha internal consistency coefficient of the scale is 0.90 [22, 23]. The Cronbach’s Alpha value of the scale has been found as 0.90 in this study.

2.2.3 Brief symptom inventory (BSI)

BSI is a self-assessment inventory developed by Derogatis. It consists of a 53-item scale selected from items of the Symptom Checklist (SCL-90-R) that has 90 items, so as to capture psychiatric disorders [24]. It is a multidimensional symptom screening scale developed to capture some psychological disorders. Participants answered the question of how many of these symptoms you experienced for the past week. (For example, irritability and trembling, getting angry easily, disturbances in appetite, feeling lonely). The Turkish adaptation of the scale was performed by Şahin and Durak. The BSI is a Likert-type scale. Each item is answered in terms of the options “not at all/ a little/ moderately / a lot / extremely” and it is ranged as 0, 1, 2, 3, 4 points, respectively. The BSI consists of nine subscales such as somatization (S), obsession-compulsion (OC), interpersonal sensitivity (IS), depression (D), anxiety disorder (AD), hostility (H), phobic anxiety (PA), paranoid thought (PD), and Pychoticism (P). Higher total scores from the scale determine the increase in the individual’s psychological symptoms.

The Cronbach Alpha internal consistency coefficient of the BSI is 0.94, and the coefficients for the subscales range from 0.65 to 0.84 (somatization = 0.78, obsession-compulsion = 0.74, interpersonal sensitivity = 0.74, depression = 0.84, anxiety disorder = 0.81, hostility = 0.76, phobic anxiety = 0.82, paranoid thought = 0.75 and Pychoticism = 0.65) [25].

The Cronbach’s Alpha value of the BSI in this study is 0.98, and the coefficients for the subscales range from 0.80 and 0.90 (somatization = 0.80, obsession-compulsion = 0.80, interpersonal sensitivity = 0.82, depression = 0.90, anxiety disorder = 0.81, hostility = 0.80, phobic anxiety = 0.82, paranoid thought = 0.81 and Pychoticism = 0.80).

2.3 Data collection

Together with the support of a professional survey company, data collection tools were distributed to the users who are members of many social media groups on the Internet. Informed consent form was included in the link sent to the participants. Participants who have agreed to participate in the study clicked the “I agree to participate in the study” button before filling out the questionnaires. Participants who accepted the questionnaire answered the data collection tools online. The questionnaire is applied by taking the necessary precautions so as not to allow more than one answer.

A post-hoc power analysis was performed with the G-Power Data Analysis program based on data from previous studies in order to determine the sample size. We used independent samples t-test at 95%confidence interval, where in p < 0.05 was considered significant. The effect size of the study was moderate (0.5), its power was 0.96, and the sample was a good representative of the population [21].

2.4 Statistical analysis

SPSS Statistics version 22.0 (SPSS Inc., Chicago, IL, USA) software was used to analyze data. The categorical variables were presented as numbers and percentages, whereas the continuous variables were presented as arithmetic mean and standard deviation. The Kolmogorov–Smirnov test was conducted to determine the nature of distribution of data. The t-tests, F-tests, and Pearson correlation analyses are used to determine the relationship between the independent and dependent variables. The reliability coefficients (Cronbach’s alpha) of the scales were determined through reliability analysis. The results have been evaluated at a 95%confidence interval. Statistical significance was defined as a p-value less than 0.05 for all analyses.

3 Results

3.1 Sociodemographic and some characteristics of the participants

The mean age of the participants in the study was 25.06±1.29 years (20–76); 613 (84.4) women were in the range of 20–29 years, 533 (73.4%) had a university degree or higher, 618 (85.1%) were single, 625 (86.1%) do not have children, and 43 (5.9%) of those who had children previously had two children (Table 1).

Table 1
Sociodemographic and some characteristics of the participants (N = 726)

Variables n %

Age group

20–29 613 84.4

30–39 41 5.6

40–49 42 5.8

>50 years 30 4.1

Educational status

Primary education 45 6.2

High school 148 20.4

University degree or higher 533 73.4

Marital status

Single 618 85.1

married 108 14.9

Status of having children

Yes 101 13.9

No 625 86.1

Number of children

1 26 3.6

2 43 5.9

>3 32 4.4

Variables	n	%
Age group
20–29	613	84.4
30–39	41	5.6
40–49	42	5.8
>50 years	30	4.1
Educational status
Primary education	45	6.2
High school	148	20.4
University degree or higher	533	73.4
Marital status
Single	618	85.1
married	108	14.9
Status of having children
Yes	101	13.9
No	625	86.1
Number of children
1	26	3.6
2	43	5.9
>3	32	4.4

3.2 Distribution of fear of COVID-19 scale, somatization, obsessive-compulsive disorder, interpersonal sensitivity, depression scores according to the sociodemographic characteristics of women

Age group (F = 0.073, p = 0.975), educational status (F = 1.420, p = 0.242), marital status (t = –1.251, p = 0.214), having children (t = 0.402, p = 0.701), and the mean fear of COVID-19 scale scores did not differ among the participants (Table 2).

Table 2
Distribution of fear of COVID-19 Scale, somatization, obsessive-compulsive disorder, interpersonal sensitivity, depression scores according to the sociodemographic characteristics of the participants (N = 726)

Variables Fear of COVID-19 Somatization Obsessive-compulsive Interpersonal Depression

Age group Scale disorder sensitivity

20–29 18.0±5.6 5.1±4.4 6.6±5.7 3.9±3.8 6.6±6.1

30–39 17.7±5.9 F = 0.073 3.8±3.1 F = 1.663 5.0±4.2 F = 5.689 2.5±2.0 F = 5.499 4.4±4.2 F = 6.681

40–49 17.7±6.7 P = 0.975 3.7±3.0 p = 0.174 3.2±2.9 p = 0.001^* 1.9±1.2 p = 0.001^* 2.8±1.8 p = 0.000^*

>50 years 17.9±4.7 5.5±4.9 5.9±4.3 3.4±3.3 6.2±5.6

Educational status

Primary education 18.9±6.0 5.1±4.2 4.5±4.1 2.9±2.0 4.5±4.5 F = 1.947

High school 17.2±6.6 F = 1.420 4.7±3.7 F = 0.195 5.5±5.5 F = 4.554 3.5±2.8 F = 1.402 5.8±5.3 p = 0.143

University degree or higher 17.7±5.7 p = 0.242 5.1±4.3 p = 0.823 6.6±5.6 p = 0.011^* 3.8±3.7 p = 0.247 6.5±5.9

Marital status

single 17.5±5.9 t = –1.251 5.1±4.3 t = 1.065 6.5±5.6 t = 3.256 3.9±3.7 t = 3.093 6.6±6.0 t = 3.732

married 18.3±5.9 p = 0.214 4.5±3.6 p = 0.305 4.6±4.0 p = 0.001^* 2.7±2.2 p = 0.001^* 4.2±3.2 p = 0.000^*

Status of having children

Yes 17.9±6.2 t = 0.402 5.1±4.3 t = –0.795 6.5±5.6 t = –3.270 3.9±3.7 t = –3.059 6.5±6.0 t = –3.493

No 17.6±5.9 p = 0.701 4.6±3.7 p = 0.450 4.6±3.9 p = 0.000^* 2.6±2.3 p = 0.001^* 4.3±4.2 p = 0.000^*

Variables	Fear of COVID-19	Somatization	Obsessive-compulsive	Interpersonal	Depression
20–29	18.0±5.6		5.1±4.4		6.6±5.7		3.9±3.8		6.6±6.1
30–39	17.7±5.9	F = 0.073	3.8±3.1	F = 1.663	5.0±4.2	F = 5.689	2.5±2.0	F = 5.499	4.4±4.2	F = 6.681
40–49	17.7±6.7	P = 0.975	3.7±3.0	p = 0.174	3.2±2.9	p = 0.001^*	1.9±1.2	p = 0.001^*	2.8±1.8	p = 0.000^*
>50 years	17.9±4.7		5.5±4.9		5.9±4.3		3.4±3.3		6.2±5.6
Educational status
Primary education	18.9±6.0		5.1±4.2		4.5±4.1		2.9±2.0		4.5±4.5	F = 1.947
High school	17.2±6.6	F = 1.420	4.7±3.7	F = 0.195	5.5±5.5	F = 4.554	3.5±2.8	F = 1.402	5.8±5.3	p = 0.143
University degree or higher	17.7±5.7	p = 0.242	5.1±4.3	p = 0.823	6.6±5.6	p = 0.011^*	3.8±3.7	p = 0.247	6.5±5.9
Marital status
single	17.5±5.9	t = –1.251	5.1±4.3	t = 1.065	6.5±5.6	t = 3.256	3.9±3.7	t = 3.093	6.6±6.0	t = 3.732
married	18.3±5.9	p = 0.214	4.5±3.6	p = 0.305	4.6±4.0	p = 0.001^*	2.7±2.2	p = 0.001^*	4.2±3.2	p = 0.000^*
Status of having children
Yes	17.9±6.2	t = 0.402	5.1±4.3	t = –0.795	6.5±5.6	t = –3.270	3.9±3.7	t = –3.059	6.5±6.0	t = –3.493
No	17.6±5.9	p = 0.701	4.6±3.7	p = 0.450	4.6±3.9	p = 0.000^*	2.6±2.3	p = 0.001^*	4.3±4.2	p = 0.000^*

^*p < 0.05; t = t statistic of t test; F = F statistic of analysis of variance.

There was no statistically significant difference between the participants’ age group (F = 1.663, p = 0.174), educational status (F = 0.195, p = 0.823), marital status (t = 1.065, p = 0.305), having children (t = –0.795, p = 0.450), and mean somatization scale scores (p < 0.05) (Table 2).

Age group (F = 5.689, p = 0.001), educational status (F = 4.554, p = 0.011), marital status (t = 3.256, p = 0.001), having children (t = –3.270, p = 0.000), and the mean obsessive-compulsive disorder scale scores (p < 0.05) were different. It was determined that the obsessive-compulsive disorder scale scores of the participants in the age group of 20–29 years, those with university and higher education, those who were single, and those who had children were higher (Table 2).

While there was no statistically significant difference between the participants’ educational status (F = 1.402, p = 0.247) and the mean IS scale scores, there was a statistically significant difference between the participants’ age group (F = 5.499, p = 0.001), marital status (t = 3.093, p = 0.001), having children (t = –3.059, p = 0.001), and the mean IS scale scores (p < 0.05). It was determined that the IS scale scores of the participants in the 20–29 age group, those who were single, and those who had children were higher (Table 2).

While there was no statistically significant difference between the participants’ educational status (F = 1.947, p = 0.143) and their mean Depression scale scores, there was a statistically significant difference between the participants’ age group (F = 6.681, p = 0.000), marital status (t = 3.732, p = 0.000), having children (t = –3.493, p = 0.000), and their mean IS scale scores (p < 0.05). It was determined that participants in the 20–29 age group, those who were single, and those who had children had higher Depression scale scores (Table 2).

3.3 Distribution of AD, Hostility, PA Paranoid Thought, Psychoticism scores according to the sociodemographic characteristics of women

The participants’ educational status (F = 1.947, p = 0.143), having children (t = 1.431, p = 0.138), and their AD scale mean scores did not differ among the study participants; however, the age group (F = 4.234, p = 0.006), marital status (t = –2.582, p = 0.007), and their AD scale mean scores (p < 0.05) differed among the participants (Table 3).

Table 3
Distribution of Anxiety disorder, hostility, phobic anxiety paranoid thought, psychoticism scores according to the sociodemographic characteristics of the participants (N = 726)

Variables Anxiety disorder Hostility Phobic anxiety Paranoid thought Psychoticism

Age group

20–29 5.2±5.2 5.1±4.8 3.8±3.5 5.2±4.5 4.0±4.0

30–39 4.4±3.4 F = 4.234 3.1±3.0 F = 5.362 3.2±2.5 F = 2.169 3.8±3.4 F = 3.965 3.1±2.7 F = 4.125

40–49 3.1±2.8 p = 0.006^* 3.4±2.9 p = 0.001^* 3.1±2.4 p = 0.090 3.2±3.0 p = 0.008^* 2.8±2.1 p = 0.006^*

>50 years 5.2±4.9 4.3±4.3 4.2±4.8 5.6±4.5 4.3±4.1

Educational status

Primary education 4.5±4.5 4.3±4.1 4.3±4.1 4.5±4.1 3.9±3.5

High school 4.7±4.3 F = 1.947 5.2±4.7 F = 0.402 3.6±3.1 F = 1.579 4.4±4.3 F = 3.512 3.7±3.5 F = 0.655

University degree or higher 5.2±5.2 p = 0.143 4.9±4.6 p = 0.669 3.8±3.4 p = 0.207 5.3±4.4 p = 0.030^* 3.9±3.9 p = 0.520

Marital status

single 5.1±5.1 t = –2.582 5.1±4.8 t = 3.074 3.5±3.0 t = –1.072 5.2±4.4 t = 2.587 3.9±3.9 t = 2.284

married 3.9±3.0 p = 0.007^* 3.5±3.0 p = 0.001^* 3.1±3.0 p = 0.329 4.2±4.1 p = 0.009^* 3.0±2.9 p = 0.026^*

Status of having children

Yes 5.1±4.1 t = 1.431 5.1±4.8 t = –3.367 3.4±3.7 t = –0.847 5.2±4.4 t = –2.377 3.9±2.9 t = –2.406

No 3.7±2.7 p = 0.138 3.4±3.0 p = 0.000^* 3.1±3.0 p = 0.447 4.1±3.3 p = 0.016^* 2.9±2.0 p = 0.020^*

Variables	Anxiety disorder	Hostility	Phobic anxiety	Paranoid thought	Psychoticism
Age group
20–29	5.2±5.2		5.1±4.8		3.8±3.5		5.2±4.5		4.0±4.0
30–39	4.4±3.4	F = 4.234	3.1±3.0	F = 5.362	3.2±2.5	F = 2.169	3.8±3.4	F = 3.965	3.1±2.7	F = 4.125
40–49	3.1±2.8	p = 0.006^*	3.4±2.9	p = 0.001^*	3.1±2.4	p = 0.090	3.2±3.0	p = 0.008^*	2.8±2.1	p = 0.006^*
>50 years	5.2±4.9		4.3±4.3		4.2±4.8		5.6±4.5		4.3±4.1
Educational status
Primary education	4.5±4.5		4.3±4.1		4.3±4.1		4.5±4.1		3.9±3.5
High school	4.7±4.3	F = 1.947	5.2±4.7	F = 0.402	3.6±3.1	F = 1.579	4.4±4.3	F = 3.512	3.7±3.5	F = 0.655
University degree or higher	5.2±5.2	p = 0.143	4.9±4.6	p = 0.669	3.8±3.4	p = 0.207	5.3±4.4	p = 0.030^*	3.9±3.9	p = 0.520
Marital status
single	5.1±5.1	t = –2.582	5.1±4.8	t = 3.074	3.5±3.0	t = –1.072	5.2±4.4	t = 2.587	3.9±3.9	t = 2.284
married	3.9±3.0	p = 0.007^*	3.5±3.0	p = 0.001^*	3.1±3.0	p = 0.329	4.2±4.1	p = 0.009^*	3.0±2.9	p = 0.026^*
Status of having children
Yes	5.1±4.1	t = 1.431	5.1±4.8	t = –3.367	3.4±3.7	t = –0.847	5.2±4.4	t = –2.377	3.9±2.9	t = –2.406
No	3.7±2.7	p = 0.138	3.4±3.0	p = 0.000^*	3.1±3.0	p = 0.447	4.1±3.3	p = 0.016^*	2.9±2.0	p = 0.020^*

^*p < 0.05; t - t statistic of t test; F - F statistic of analysis of variance.

The educational status (F = 0.402, p = 0.669) and Hostility scale mean scores did not differ among participants, while the age group (F = 5.362, p = 0.001), marital status (t = 3.074, p = 0.001), having children (t = –3.367, p = 0.000) and Hostility scale mean scores (p < 0.05) differed among the participants. The Hostility scale scores of the participants in the 20–29 age group, those who were single and those who had children were higher (Table 3).

The age group (F = 2.169, p = 0.090), educational status (F = 1.579, p = 0.207), marital status (t = –1.072, p = 0.329), having children (t = –0.847, p = 0.447) and PA scale mean scores did not differ among others (Table 3).

A statistically significant difference was found among the participants’ age group (F = 3.965, p = 0.008), educational status (F = 3.512, p = 0.030), marital status (t = 2.587, p = 0.009), having children (t = –2.377, p = 0.016) and the mean PT scale scores (p < 0.05). Participants aged > 50 years, those with university and higher education, those who were single, and those who had children had higher PT scale scores (Table 3).

The educational status and psychoticism scale mean scores did not differ among participants (F = 0.655, p = 0.520). However, the age group differed among the participants (F = 4.125, p = 0.006). A statistically significant difference was found when the participants’ age group were compared with marital status (t = 2.284, p = 0.026), having children (t = –2.406, p = 0.020) and psychoticism scale mean scores (p < 0.05). Participants aged > 50, those who were single, and those who had children had higher psychoticism scale scores (Table 3).

3.4 Distribution of scores for fear of COVID-19 scale, BSI subscales and correlation among the mean scores (N = 490)

The total average scores of the fear of COVID-19 scale was 17.7±5.9 (7–35), obsessive-compulsive disorder was 6.2±5.5 (0–24), somatization was 5.0±4.3 (0–28), IS was 3.7±2.7 (0–16), depression was 6.2±5.9 (0–24), AD was 5.0±4.1 (0–24), hostility was 4.8±4.7 (0–20), PA was 3.4±2.8 (0–20), PT 5.0±4.4 (0–20), and psychoticism was 3.8±2.9 (0–20) were observed respectively (Table 4).

Table 4
Distribution of Scores for fear of COVID-19 Scale, BSI subscales and correlation among the

Mean±SD Min–Max 1 r p 2 r p 3 r p 4 r p 5 r p 6 r p 7 r p 8 r p 9 r p 10 r p

Fear of COVID-19 Scale(1) 17.7±5.9 7–35 –

Obsessive-compulsive disorder (2) 6.2±5.5 0–24 0.433 0.000^* –

Somatization (3) 5.0±4.3 0–28 0.470 0.001^* 0.731 0.000^* –

Interpersonal sensitivity (4) 3.7±2.7 0–16 0.319 0.001^* 0.760 0.000^* 0.711 0.000^* –

Depression (5) 6.2±5.9 0–24 0.073 0.184 0.838 0.000^* 0.702 0.000^* 0.847 0.000^* –

Anxiety disorder (6) 5.0±4.1 0–24 0.491 0.000^* 0.815 0.000^* 0.791 0.000^* 0.819 0.000^* 0.856 0.000^* –

Hostility (7) 4.8±4.7 0–20 0.064 0.084 0.766 0.000^* 0.683 0.000^* 0.745 0.000^* 0.771 0.000^* 0.815 0.000^* –

Phobic anxiety (8) 3.4±2.8 0–20 0.415 0.000^* 0.701 0.000^* 0.788 0.000^* 0.703 0.000^* 0.696 0.000^* 0.778 0.000^* 0.699 0.000^* –

Paranoid thought (9) 5.0±4.4 0–20 0.508 0.001^* 0.788 0.000^* 0.698 0.000^* 0.789 0.000^* 0.813 0.000^* 0.796 0.000^* 0.802 0.000^* 0.708 0.000^* –

Psychoticism (10) 3.8±2.9 0–20 0.067 0.073 0.775 0.000^* 0.784 0.000^* 0.810 0.000^* 0.825 0.000^* 0.839 0.000^* 0.795 0.000^* 0.793 0.000^* 0.807 0.000^* –

	Mean±SD	Min–Max	1 r p	2 r p	3 r p	4 r p	5 r p	6 r p	7 r p	8 r p	9 r p	10 r p
Fear of COVID-19 Scale(1)	17.7±5.9	7–35	–
Obsessive-compulsive disorder (2)	6.2±5.5	0–24	0.433 0.000^*	–
Somatization (3)	5.0±4.3	0–28	0.470 0.001^*	0.731 0.000^*	–
Interpersonal sensitivity (4)	3.7±2.7	0–16	0.319 0.001^*	0.760 0.000^*	0.711 0.000^*	–
Depression (5)	6.2±5.9	0–24	0.073 0.184	0.838 0.000^*	0.702 0.000^*	0.847 0.000^*	–
Anxiety disorder (6)	5.0±4.1	0–24	0.491 0.000^*	0.815 0.000^*	0.791 0.000^*	0.819 0.000^*	0.856 0.000^*	–
Hostility (7)	4.8±4.7	0–20	0.064 0.084	0.766 0.000^*	0.683 0.000^*	0.745 0.000^*	0.771 0.000^*	0.815 0.000^*	–
Phobic anxiety (8)	3.4±2.8	0–20	0.415 0.000^*	0.701 0.000^*	0.788 0.000^*	0.703 0.000^*	0.696 0.000^*	0.778 0.000^*	0.699 0.000^*	–
Paranoid thought (9)	5.0±4.4	0–20	0.508 0.001^*	0.788 0.000^*	0.698 0.000^*	0.789 0.000^*	0.813 0.000^*	0.796 0.000^*	0.802 0.000^*	0.708 0.000^*	–
Psychoticism (10)	3.8±2.9	0–20	0.067 0.073	0.775 0.000^*	0.784 0.000^*	0.810 0.000^*	0.825 0.000^*	0.839 0.000^*	0.795 0.000^*	0.793 0.000^*	0.807 0.000^*	–

^*p < 0.05; r = Pearson correlation coefficient.

Overall, we found a significantly positive relationship between the fear of COVID-19 scale score and obsessive-compulsive disorder, somatization, IS, AD, PA, and Paranoid Thought scale score (Table 4). As the participants’ fear level increased, their obsessive-compulsive disorder, somatization, IS, AD, PA, PT levels also increased (Table 4).

4 Discussion

The sudden changes in our lifestyle, loss of beloved ones, quarantine, isolation, and social distancing due to COVID-19 pandemic has equally procured risks in terms of our physical health, mental and social health [26]. Thus, the symptoms of psychological diseases increase alongside the spread of the disease worldwide during the pandemic, and women are much more vulnerable to the stress caused by the pandemic [2, 27]. Previous studies on the pandemic have focused on the fear-related symptoms in our country. However, few studies demonstrated the effects of this fear on the mental states of female employees. Thus, we conducted a study to assess the relationship between fear of COVID-19 and the psychological distress in female workers during the COVID-19 pandemic. We aimed alerting medical professionals with our findings and aid in the creation of efficient mental interventions during and after the pandemic.

Panic, fear of contagion, paranoia and social isolation are among the typical psychological reactions after a biological attack [28]. In this study, we found a significantly positive relationship between fear of COVID-19 and obsessive-compulsive disorder, S, IS, AD, PA, and PT scale score. Beyond the potential threat of the infection, the pandemic procures a high degree of uncertainty that triggers feelings of arousal and hypervigilance. Moreover, fear of the unknown triggers anxiety and affects the mental health status of a healthy individual negatively [29]. The uncertainties, the duration of measures to be taken, the stress caused by the risk of tapinophobia, the individual’s concerns about himself and his family health, the effects of the disease, and the fear reactions of individuals have caused some emotional disorders such as anxiety, depression, stress, IS, PA and psychoticism, high rate of mental illnesses, and mortality [30 –32]. Fear about the pandemic have increased stress, panic, and IS, and have created temporary paranoia about being infected by and transmitting the disease [27]. Furthermore, some obsessive-compulsive symptoms such as forgetting, worrying about the cleanliness of clothes and rules, washing hands repeatedly, and tending to have an exaggerated perception of threat also occurred during the pandemic [32]. PA symptoms can be thought as the fear of getting on the tram, bus, subway, train, being in open areas or on the street, avoiding activities, and feeling restless in crowds such as shopping and cinema [32]. Additionally, somatization has increased during the pandemic, and was triggered by the fear in the pandemic associated with the persistence of disease symptoms and negative treatment perceptions [33]. Thus, the level of fear and threat perception related to the pandemic is associated with anxiety, depression, somatization, obsessive-compulsive symptoms, and PA [29 , 33]. The scores of BSI sub-dimensions (negative self, depression, anxiety, hostility, and somatization) increased positively as the fear experienced due to COVID-19 as in our study. Our findings were similar to those found in the literature on the mental health of working women [29 , 33].

Although there was no significant link between age and fear in the study, it was observed that the levels of obsessive-compulsive disorder, IS, depression, AD, hostility, PD, and psychoticism have increased in young adults and older women. During the pandemic, the elderly individuals are more likely to be affected psychologically as they have more passive life, chronic diseases, limited social opportunities and high mortality rates; thereby increasing the susceptibility to depression and psychological distress [2 , 34]. Young adults tend to receive information that can easily trigger stress, and can watch and listen to negative news that intensifies their feelings of anxiety and depression in times of crisis [28, 35]. Our results were similar to those in other studies. However, the pandemic caused fear in all individuals, young adults and elderly individuals.

The level of education does not affect fear of COVID-19, and there was a significant difference between the scores of obsessive-compulsive disorder and PD. Although there are some studies about the risk increases in case [32], the pandemic causes more mental effects in these individuals due to the high level of awareness of highly educated people on health issues [2, 28]. Therefore, it is thought that advanced education level causes an increase in obsessive and paranoid thoughts because of the necessity of fully comply with quarantine warnings and rules.

In this study, the obsessive-compulsive disorder, IS, depression, hostility, paranoid ideation and psychoticism scores of single participants were higher than those of married participants. Tian et al. claimed that divorced or widowed individuals experience more obsessive-compulsive symptoms, IS, PA, and psychotic symptoms during the epidemics in China [32]. As seen in previous studies, the presence of a spouse/partner is a protective factor on the mental health of married people and that support has an essential role in the mental health status of such females [36 –38].

During the isolation in the COVID-19 pandemic, parents and their children were forced to stay together at home, leading to drastic changes in daily life routine [39]. Those who did not have children had higher obsessive-compulsive disorders, IS, and depression scores than those with children. Parents who work from home, particularly women, found it difficult to juggle childcare responsibilities with keeping an eye on the home education of their kids. In general, the COVID-19 pandemic has been linked to parental burnout as parents experienced an increase in expectations and a decrease in resources [40]. In a study on the mental health of Irish working mothers during the lockdown, Clark et al. stated that providing education, care and socialization of children at home in addition to their normal work routines aggravates the pandemic burden experienced by women [41]. Avery et al. demonstrated that perceived stress levels in with 1,014 women with children under the age of 18 were not different from those women without children. He also showed that anxiety levels of women with children were higher than those of women without children [40]. The findings of this study were similar to the studies in the literature.

4.1 Limitations

This study is not without limitations. This is cross-sectional study that used the simple random probability sampling method. Moreover, there was room for selection bias as it included only volunteering participants. In other words, the participants may have intentionally given misleading answers to questions. Conversely, certain negativities are possible, such as security concerns related to the electronic environment, uncertainty of respondents, problems with access to the questionnaire, misunderstanding the sensitivity of the research, incorrect e-mail addresses and problems with access to the web page. Additionally, the results are applicable only to the surveyed participants and cannot be generalized to other populations.

Conclusion

The COVID-19 pandemic is a stressor or traumatic experience with psychopathological consequences compared to other natural disasters such as earthquakes, tsunamis, or wars. The fear experienced by individuals during the pandemic is associated with depression, anxiety, somatization, obsessive-compulsive disorder, IS, hostility, PA and PTs and has deleterious effects on the mental health of female employees. Women, young people, the elderly and single individuals experienced more psychological disorders during the pandemic. Thus, interventions and psychological evaluations are recommended at an early stage since affects the lives of women, and these interventions must be done considering strategic planning and coordination for risk groups.

Data availability statement

The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy/ethical restrictions.

Ethical approval

The study performed in accord with the Declaration of Helsinki. Prior to the study, approval was obtained from the Non-Interventional Research Ethics Committee of Ege University (decision number 21-12.2T/6.)

Informed consent

All participants provided informed consent prior to data collection based on the principles of confidentiality and volunteerism. To ensure understanding, the following statements were added prior to submission: “Submitting the information form indicates consent to participate” and “Proceed to the survey”.

Conflict of interest

The authors have no conflict of interest to declare.

Footnotes

Acknowledgments

Not applicable.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

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