Impact of fatalistic approaches in the health crisis: COVID-19 example

Abstract

BACKGROUND:

When faced with a health crisis, most people tend to seek solutions through modern or traditional medicine. However, there is a group of people who tend to take a fatalistic approach to health crises and tend not to do what is necessary. When such approaches are exhibited in disadvantaged groups, there may be a chain reaction resulting in much more important problems.

OBJECTIVE:

The aim of this study is to determine whether the fatalistic approach is realized during a health crisis period in people 65 years and older, pregnant women, people with chronic diseases, and healthcare workers who are considered disadvantaged. Researchers also examined whether there were sociodemographic characteristics that made a difference in these attitudes among those who were determined to have a fatalistic approach.

METHODS:

The research was conducted with a quantitative method. The sample calculation was performed and it was decided to reach 196 people. A self-report scale was used for fatalism.

RESULTS:

It was determined that the participants had a moderate level of fatalism. It was seen that there was a difference between the groups in terms of fatalistic behavior. It was observed that women, divorced people, people without alcohol and smoking habits, people who live alone, and people who think their health is poor tend to behave more fatalistic.

CONCLUSION:

The tendency to show fatalistic behavior was found to be higher in the group of 65 years and older. Sociodemographic characteristics are associated with fatalistic behavior.

Keywords

Fatalistic tendency health crisis health protection risk groups

1 Background

As the COVID-19 pandemic unfolds, it brings about an extraordinary period worldwide, giving rise to various approaches between individuals, societies, and cultures. Within this framework, a fatalistic perspective emerges as a notable approach adopted by individuals and communities in navigating through this challenging period. Fatalism is the conviction that an individual’s actions wield minimal or negligible influence on crucial outcomes [1]. Individuals with a high level of fatalism typically avoid engaging in future-oriented planning, invest minimal effort in pursuing desirable goals, and generally adopt a resigned attitude towards fate. In essence, they are open to relinquish control to external forces. While the inclination toward fatalism is considered an individual difference, there are also situational circumstances that can foster fatalistic thinking, regardless of variations in personality [2]. In this context, understanding the impact of global interactions caused by COVID-19 on individuals’ perceptions of destiny becomes crucial, offering a research avenue to explore the cultural dynamics underlying various strategies and behavioural patterns adopted in the fight against the pandemic. Studies on fatalism in the literature show that people who have a high perception of fatalism are more likely to take risks and not take safety precautions [3]. In a study conducted by Jimenez et al., participation in COVID-19 was associated with death, and it was reported that the perception of fatalism negatively affected social distancing and handwashing behaviors [4]. Papageorge et al. also found that public servants exhibit fatalistic attitudes and do not comply with preventive measures [5]. In a study conducted in rural areas of Iran, it was recommended to reduce fatalistic beliefs in the pandemic process [6]. In the research of Bogolyubova et al., fatalism is explained as a determining factor in experiencing post-traumatic stress disorder [7]. In the study by Bachem et al., the fear experienced by the Swiss sample group was associated with fatalism [8]. Lifshin et al. are also in line with this, and they mention that a fatalistic approach to not taking action or reducing anxiety can be an option [9]. As can be seen, although there is a tendency to behave fatalistically in many situations, it is especially important to reveal this tendency in events that create a health crisis.

1 Introduction

The latest global health crisis is the COVID-19 pandemic. In this crisis, most countries around the world initially aimed to protect people or professions at risk. However, many countries did not react quickly enough [10]. As is known, there have been a significant number of deaths due to this pandemic, and freedoms have been restricted, leading to a decrease in economic well-being. Despite all this, people have been expected to adhere to warnings to avoid exacerbating potential issues, and in many cases, compliance with directives has been mandatory. However, it has been observed that people are not very willing or cooperative in their efforts to comply with the directives [11]. The slow or inadequate response in many European countries has made it challenging to combat the effects of the pandemic. In this recent health emergency, both individuals and governments behaved differently. The nearly worldwide health crisis has shown that risk perception is perceived differently by both individuals and governments [12].

Defense mechanisms are believed to be related to fatalistic tendencies. Fatalism is the belief that one’s actions have little or no significant impact on important outcomes [13]. The defence mechanisms developed by individuals against internal and external stimuli that cause distress and the tendency to fatalism that distorts reality, preventing individuals from taking responsibility, are similar in that they tend to distort reality to cope with difficult situations [14]. On the other hand, it is considered that the tendency towards fatalism may negatively affect an individual’s judgment related to the ability to organize and perform actions that will enable them to achieve a certain performance [4, 15]. The literature also highlights the uncertainties created by the pandemic and highlights the possibility of a fatalistic attitude [2]. Fatalism is the belief that one’s actions have little or no significant influence on important situations [16]. Individuals with a fatalistic attitude tend not to plan for the future, make little effort to achieve desirable goals, and generally surrender to fate. In other words, they are ready to let external forces take control. The literature emphasizes the importance of individual differences in fatalistic tendencies, but also indicates that the events experienced are influential [2]. It cannot be precisely predicted when the COVID-19 pandemic, which has fluctuated in the past three years, will end. For this reason, the process may open the door to fatalism in the treatment of the disease.

As is known, in pandemics, there are priority groups that need to be protected first [17, 18]. The primary philosophy here is to ensure the well-being of healthcare personnel [18] who will intervene in the event. Then, individuals with undeveloped immune systems, such as the elderly or infants [19, 20], come next. Furthermore, special groups, such as chronic patients [21] and pregnant women [18] who may have their health compromised due to various additional adversities beyond their existing conditions, are also important.

In this study, the approaches of disadvantaged groups to fatalistic behavior were examined. The study participant groups consisted of healthcare professionals, people 65 years and older, people with chronic diseases, and pregnant women. It was examined whether there was a difference in the fatalistic approaches of these groups during the COVID-19 pandemic. Furthermore, it was aimed to reveal the sociodemographic characteristics that are thought to moderate any difference.

2 Methods

Type of study: This research is a quantitative cross-sectional study (data were collected between March 2020 and May 2020).

Population and sample of the study: The study aimed to reach a total of 196 individuals, determined using the G*Power 3.1 method (effect size: 0.25, α= 0.05, Power (1-β err prob) = 0.80, Number of groups = 4). The reason for including these four groups in the study is based on both theoretical knowledge passed down from the past to the present and observations and reports made at the beginning of this pandemic. For example, in a study published by Naveed et al., physiological changes resulting from aging and diseases that can be added to old age made the COVID-19 pandemic challenging for this group [22]. In the same study, it was explained that healthcare workers are the backbone of the healthcare system, emphasizing their heavy workload, and it was reported that they are also a leading group in terms of contracting and spreading the disease [18]. The World Health Organization, on the other hand, has reported that the COVID-19 pandemic affects people of all ages, but people with serious medical complications such as cardiovascular disease, chronic respiratory disease, diabetes, and cancer are at increased risk of disease morbidity and mortality [18, 22]. The reason for including pregnant women in the classification of the high-risk group for this research is their increased susceptibility to viral infections due to physiological immunosuppression that occurs during pregnancy, which can lead to a more severe clinical course [23].

Data were collected from approximately 10% more than the calculated sample, totaling 208 participants (the number of participants in each group is 52 people). The data collection method employed was convenience sampling, chosen for its expediency in the digital environment based on the Internet (Twitter (X), Facebook, Instagram, Whatsapp). Since the government of the Republic of Turkey advised the public to minimize face-to-face interaction and isolate themselves at home, the participants were invited to the study electronically. Participants completed the questionnaires through the online survey platform. To exclude overlap between the study groups, precautions have been taken. The purpose of this measure is to ensure that participants possess only the targeted characteristic. For this reason, people who are healthcare workers were instructed to have only the characteristic of being a healthcare worker, and it was specified that having a chronic illness, being pregnant, or being over 65 years old would affect the objective of the study. Therefore, healthcare workers with such conditions were instructed not to answer the survey questions. Similar explanations were also provided for other groups. It was indicated that people with chronic diseases should not be pregnant, elderly, or healthcare workers. Pregnant participants were instructed not to be healthcare workers and not have chronic diseases. Elderly participants over 65 years of age were asked not to participate in the study if they had chronic illnesses or if they were still working as healthcare workers. After collecting all data, it was checked whether there was any overlap in this regard. The average age of the participants is 37.42±11.54 (min: 18.00 –max: 58.00) among those with chronic diseases, 71.71±5.47 (min: 65.00 –max: 93.00) among those over 65 years of age, 27.25±6.13 (min: 17.00 –max: 54.00) among pregnant women, and 28.30±7.58 (min: 20.00 –max: 51.00) among healthcare workers.

Data Collection Tools: The data collection instrument was a Personal Information Form and the Health Fatalism Scale.

Personal Information Form: This form was used to gather specific characteristics of the participants, including age, gender, educational status, employment, habits, presence of chronic diseases, presence of people in the household belonging to other risk groups (diagnosed/suspected personal status), feelings of safety and opinions on the sufficiency of the measures taken, among other factors.

Health Fatalism Scale (HFS): HFS was used to assess fatalistic thinking attitudes related to health problems. The original version of this scale was developed by Franklin, Schlundt, and Wallston in 2008 [24], and its Turkish adaptation was conducted by Bobov and Canturk in 2018 [16]. The scale is designed to investigate the correlation between health fatalism and health-related behaviors. In addition, it seeks to mitigate cognitive obstacles to adopting positive health behaviors, utilizing healthcare services, and embracing a healthy lifestyle. Higher scale scores are indicative of elevated levels of fatalism [25]. The scale is unifactorial, consisting of 17 items, and employs a 5-point Likert scale (1 = Strongly disagree, 2 = disagree, 3 = undecided, 4 = agree, 5 = strongly agree). The scale yields a minimum score of 17 and a maximum score of 85. The Cronbach’s α value in the validation study was reported as 0.91, while in this study it was found to be 0.94.

Ethics: Before starting the research, written consent was obtained from Gumushane University’s Scientific Research (21452481 –100 –E.12363) and the Publication Ethics Committee, as well as the Ministry of Health Scientific Research Platform (2020 –05 –01T01 _ 41 _ 44). Permission to use the HFS in the study was secured via email from the authors responsible for the Turkish validation study of the scale. Data were collected from “volunteer participants who reported not having any psychiatric illness diagnosed by a physician,” according to the criteria of the Helsinki Declaration, as outlined in the information provided at the beginning of the research form.

Statistical analysis: The data obtained from the research were analyzed using SPSS-22 software. Before the normality analysis, lost data and extreme value analyses were performed. Subsequently, histograms were generated to assess adherence to a normal distribution, and skewness and kurtosis values were examined along with the Kolmogorov-Smirnov tests. Chi-square analysis was performed to investigate the relationship between certain characteristics and health-protective behaviors. Independent sample-T tests, one-way ANOVA tests and factor ANOVA tests were conducted to explore associations between independent variables and HFS. In cases where significant variance analysis results were obtained, Tukey’s HSD test was carried out to identify the specific differences between means causing this effect. Logistic regression analysis was performed to identify characteristics that influence “anxiety related to changes in health status” due to the COVID-19 pandemic, with a statistical significance level accepted at p < 0.05.

3 Results

The research found that the mean age of all participants was 42.12±20.66 (range: 17–93), with 79.3% of the participants being women. Regarding educational background, 31.3% had primary or secondary education, 20.7% had completed high school, and the remainder had a university or higher degree. Of all participants, 64.4% were married, 20.2% were single, and 15.4% were divorced or lived separately. In terms of perceived income, 64.9% stated that their income covered their expenses, 23.6% had a low income, and 11.5% had a high income. Among all participants, 66.3% reported not having habits, 17.8% had the habit of smoking alone, 9.6% had some habits in the past but none in the last five years, 4.8% had various types of habits, and 1.4% had the habit of consuming alcohol. The percentage of those living alone was 9.6%, while 83.7% lived with their family. Of all participants, 5.3% lived with friends and 1.4% with relatives. In terms of place of residence, 53.4% lived in provinces, 35.6% in districts and 11.1% in villages. Additionally, 15.4% of all participants reported experiencing issues related to access to medications or health checkups related to their existing chronic disease. When asked what they would do first if they experienced a change in their health status, 43.8% said that they would go to the hospital, 26.9% would call health hotlines and follow their instructions, 17.3% would visit the local healthcare center, 5.3% would seek advice from people who had experienced a similar situation, 4.8% would do nothing and wait for the issue to resolve, 1.4% would try to gather information from the Internet and 0.5% would go to a pharmacy. Furthermore, 77.7% of the participants claimed to take adequate protective health measures to prevent changes in their health status. Regarding how they coped with the pandemic, 45.1% mentioned using coping methods such as listening to music, reading books, meditating, playing sports, and participating in worship, 28.6% did not follow the news, and 26.2% did not require any coping methods. The descriptive characteristics of the participants according to the groups to which they belong are shown in Table 1. The distribution of responses to certain variables for all participants is shown in Table 2. In this study, health care workers reported that 5.8% lived alone, 50% had someone at home who was vulnerable to COVID-19 (such as elderly or pregnant individuals), 78.8% experienced concerns about changes in their health, 71.2% believed their mental health had been affected by the pandemic, 51.9% stated that their physical health had changed due to the COVID-19 pandemic, 53.8% mentioned a change in their sleep patterns, 48.1% reported a change in their eating habits, 61.5% observed a change in their exercise routine, and 84.6% indicated a change in their interpersonal interactions.

Table 1
Participant some characteristics (N = 208)

Variable Characteristics Participants Participants Pregnant Healthcare

with Chronic aged 65 and Participants Worker Participants

Illness n (%) older n (%) n (%) n (%)

Gender Female 42 (80.8) 42 (80.8) 33 (63.5) 40 (76.9)

Male 10 (19.2) 10 (19.2) 19 (36.5) 12 (23.1)

Education Level Primary school 18 (34.6) 18 (34.6) 39 (75.0) –

High school 8 (15.4) 8 (15.4) 4 (7.7) 19 (36.5)

University 26 (50.0) 26 (50.0) 9 (17.3) 33 (63.5)

Marital Status Married 29 (55.8) 29 (55.8) 31 (59.6) 24 (46.2)

Single 12 (23.1) 12 (23.1) 2 (3.8) 27 (51.9)

Widowed-divorced-separated 11 (21.2) 11 (21.2) 19 (36.5) 1 (1.9)

Perceived income level Low 14 (26.9) 14 (26.9) 16 (30.8) 13 (25.0)

Moderate 32 (61.5) 32 (61.5) 30 (57.7) 30 (57.7)

High 6 (11.5) 6 (11.5) 6 (11.5) 9 (17.3)

Variable	Characteristics	Participants	Participants	Pregnant	Healthcare
Gender	Female	42 (80.8)	42 (80.8)	33 (63.5)	40 (76.9)
	Male	10 (19.2)	10 (19.2)	19 (36.5)	12 (23.1)
Education Level	Primary school	18 (34.6)	18 (34.6)	39 (75.0)	–
	High school	8 (15.4)	8 (15.4)	4 (7.7)	19 (36.5)
	University	26 (50.0)	26 (50.0)	9 (17.3)	33 (63.5)
Marital Status	Married	29 (55.8)	29 (55.8)	31 (59.6)	24 (46.2)
	Single	12 (23.1)	12 (23.1)	2 (3.8)	27 (51.9)
	Widowed-divorced-separated	11 (21.2)	11 (21.2)	19 (36.5)	1 (1.9)
Perceived income level	Low	14 (26.9)	14 (26.9)	16 (30.8)	13 (25.0)
	Moderate	32 (61.5)	32 (61.5)	30 (57.7)	30 (57.7)
	High	6 (11.5)	6 (11.5)	6 (11.5)	9 (17.3)

Table 2

All participant some characteristics (N = 208)

Variable		n	%
The presence of other individuals at risk in the same household	Yes	93	44.7
	No	115	55.3
The presence of a COVID-19 diagnosis in individuals within the household, including oneself.	Yes	11	5.3
	No	194	93.3
Suspect	3	1.4
Experiencing a change in the perception of safety as a result of the pandemic.	Yes	154	26.0
	No	54	74.0
Opinion on the adequacy of measures implemented against COVID-19.	Yes	111	53.4
	No	97	46.6
Experiencing anxiety related to any potential change in one’s personal health status.	Yes	160	76.9
	No	48	23.1
The idea that mental health may be impacted by COVID-19.	Yes	143	68.7
	No	65	31.3
The idea that physical health may be affected by COVID-19.	Yes	95	45.7
	No	113	54.3
Alteration of sleep patterns as a result of COVID-19.	Yes	97	53.4
	No	111	46.6
Modification of dietary habits because of COVID-19.	Yes	94	45.2
	No	114	54.8
Alteration of exercise routines or habits due to the impact of COVID-19.	Yes	116	44.2
	No	92	55.8
Modification of interpersonal relationships as a consequence of COVID-19.	Yes	171	82.2
	No	37	17.8

The distribution of the health protective behaviors of all participants based on certain characteristics is presented in Table 3, which indicates that gender, income level and health perception (p < 0.05) had a significant influence. On the contrary, education level, marital status, belonging to a risk group (chronic disease, age over 65 years, pregnancy, healthcare personnel), living arrangements, whether there was a diagnosis of COVID-19 in yourself or in the household, and perception of possible changes in health status changes (p > 0.05) did not show significant differences.

Table 3

Distribution of participants’ health-protective behaviors by selected characteristics (N = 208)

Adopting health-protective measures.				Test and p value^*
Characteristics	No n (%)		Yes n (%)
Gender
Female	31 (19.0)		123 (81.0)		χ² = 4.938
Male	15 (34.9)		28 (65.1)		p = 0.026
Income level		AR (z)^**	AR (z)^**
Low income	19 (38.8)a	–3.5	30 (61.2)a	–3.2	χ² = 14.857
Income matching expenses	27 (20.3)b	2.3	106 (79.7)b	0.9	p = 0.000
High income	0 (0.0)c	3.6	24 (100.0)c	2.8
Health Perception		AR (z)^**	AR (z)^**
Well	15 (13.2)a	–3.5	99 (86.8)a	3.5	χ² = 21.500
Moderate	26 (30.2)b	2.3	60 (69.8)b	–2.3	p = 0.000
Poor	5 (83.3)c	3.6	1 (16.7)c	–3.6

^*: Chi-Square Test ^**Row percentage a, b: Z values were given in binary comparisons as the post hoc test, and Bonferroni correction was made. AR (z): Adjusted Rezidüe.

The overall mean score on the Health Fatalism Scale (HFS) for all study participants was 47.85±13.96 (range: 17–85). The mean HFS scores and standard deviation values for different risk groups were as follows: 53.78±13.20 (min: 31.00 –max: 85.00) for the age group 65 and older, 43.03±14.99 (min: 17.00 –max: 81.00) for chronic patients, 49.48±12.54 (min: 17.00 –max: 78.00) for pregnant women, and 44.90±14.96 (min: 17.00 –max: 83.00) for healthcare professionals. The proportion of participant responses to the HFS scale items is illustrated in Fig. 1.

Fig. 1

Rates of responses of the health fatalism scale.

Sociodemographic characteristics that showed a difference in terms of HFS score, along with the groups from which the differences originated, according to Tukey’s post hoc * test, are as follows:

Participant risk group: F = 6.196, p = 0.001 (*65 years of age and older)

Education level: F = 6.661, p = 0.002 (*university degree)

Marital status: F = 3.394, p = 0.035 (*single)

Unhealthy habits: F = 3.258, p = 0.013 (*alcohol use)

Other members of the household: F = 5.385, p = 0.001 (*living with friends)

Health perception: F = 4.778, p = 0.009 (*good health)

Opinion on the sufficiency of measures taken against COVID-19, t = 4.759, p = 0.001

The study also examined the participants’ scores on the fatalism tendency scale and their anxiety perceptions, as shown in Table 4. It was observed that the risk group of the participant influenced the HFS (p < 0.05), and post hoc analysis with Bonferroni correction revealed that the difference was mainly between the chronic disease group and those over 65 years of age, and participants over 65 years of age had higher levels of anxiety.

Table 4

Participants’ fatalism tendency scale and anxiety perception scores (N = 208)

	Fatalism tendency scale	Anxiety scores
Mean±SD	47.85±13.96	6.24±2.20
% 95 CI	45.78–49.92	5.91–6.56
Median	49.00	7.00
Min-Max	17–85	1–10
Skewness±SE	0.037±0.182	–0.519±0.182
Kurtosis±SE	–0.068±0.362	–0.011±0.362

It has been observed that gender, income level perceptions, general health perceptions and mental health perceptions of healthcare workers do not make a difference in their scale scores of fatalistic tendency.

Factors affecting participant safety feelings during the COVID-19 pandemic were evaluated by logistic regression analysis, as presented in Table 5. The table indicates that the participant’s risk group, the presence or absence of a diagnosis of COVID-19 in themselves or others in the household, the level of education and the anxiety due to the pandemic had an impact on the perception of safety, independently of each other. For each unit increase in the perception of safety, the presence/absence of a diagnosis of COVID-19 in oneself or others in the household had an effect of 12.851 times, anxiety had an effect of 4.805, being in the age group of 65 and older had an effect of 3.487, and having a high school education had an effect of 0.281 (p < 0.05).

Table 5

Factors influencing participants’ sense of safety (N = 208)

Characteristics		β	p	OR	% 95 GA
Age (Numeric)		–0.024	0.122	0.976	0.947–1.006
HFS (Numeric)		0.009	0.470	1.009	0.985–1.034
Gender	Female			1.00
	Male	–0.431	0.345	0.650	0.265–1.591
Group	Chronic		0.250	1.00
	65 years+	1.249	0.049	3.487	1.005–12.094
	Pregnant	–0.111	0.847	0.895	0.290–2.764
	Healthcare	0.174	0.759	1.190	0.392–3.616
Place of residence	Center		0.687	1.00
	District	0.160	0.527	1.174	0.537–2.564
	Village	–0.397		0.673	0.197–2.296
Has anyone in the	Yes		0.001	1.00
household been	No	2.553	0.001	12.851	3.217–51.332
diagnosed with COVID-19?	Suspect	21.848	0.999	307909	0.000–
Perceived income level	Low		0.537	1.00
	Moderate	0.014	0.976	1.014	0.416–2.469
	High	0.681	0.327	1.976	0.507–7.703
Education level	Primary school		0.054	1.00
	High school	–1.271	0.040	0.281	0.083–0.943
	University	–0.310	0.571	0.734	0.252–2.139
Experiencing anxiety	No			1.00
	Yes	1.570	0.001	4.805	2.176–10.610

^*Nagelkerke R Square: 0.248.

4 Discussion

Regarding the worldwide economic, social, and psychological impacts of the COVID-19 pandemic [26], understanding the perceptions, attitudes, and behaviors, especially among high-risk groups, is crucial to developing relevant measures and interventions. This is particularly important since the health behaviors exhibited by individuals in response to the threat of pandemic play a significant role in reducing the geographical spread and transmission speed of the pandemic and minimizing potential casualties [27]. This study aimed to assess whether the COVID-19 pandemic differentiates risk groups (those with chronic diseases, people aged 65 and older, pregnant individuals, and healthcare personnel) in terms of health fatalism in Turkey. Additionally, it sought to identify the knowledge, attitudes, and behaviors that contribute to health protection, improvement, and rehabilitation actions with respect to fatalistic tendencies. Turkey is sixth in the European region in terms of the number of confirmed COVID-19 cases [28].

Geographically located at the crossroads of Eastern Europe and Western Asia, Turkey possesses various unique characteristics, including high population density, importance as a travel hub, the acceptance of a large number of immigrants, a notable elderly population (considered a risk group), high fertility rates, a significantly high child population compared to neighboring countries, accessibility to healthcare services, and ease of movement in infected areas [29, 30]. The first case of COVID-19 in Turkey was reported on 11 March 2020, followed by a series of measures. These measures included curfews for people over 65 years of age and under 20 years of age, transitioning all educational activities to remote learning, granting leave of absence for pregnant women working in the public sector, and additional employment opportunities to reduce the workload of healthcare professionals. Furthermore, masks were distributed free of charge to all citizens as a preventive measure.

Individuals may adopt preventive behaviors and avoid actions perceived as risky for their health when they believe that a particular disease can easily spread and pose a threat to their health [31]. Studies demonstrating a positive relationship between religion and health behaviors suggest that people adhere to certain health behaviors due to their spiritual and mental beliefs [25]. In this study, it was observed that women, those with a higher income and those with a positive perception of their health were more likely to report compliance with health protective measures. Gender, income level, and health perception were identified as factors that make a difference in this regard. However, education level, marital status, membership in risk groups (chronic disease, age over 65, pregnant, healthcare personnel), living arrangements, diagnosis of COVID-19 within or within the household, and perception of possible changes in health status did not demonstrate significant differences. Excluding mandatory and unalterable aspects such as work conditions, professions, and socioeconomic status, the failure to adhere to the preventive measures and restrictions of COVID-19, in the absence of any hiccup, requires elucidation. The examination of cultural values that are beyond the control of health services and contribute to unfavorable health indicators is a noteworthy issue. It is crucial to understand the cultural factors that influence health-promoting behaviors [32]. Gast, Peak, and Hund’s research also found that women and individuals with a sense of sufficient income tend to adopt more health-protective behaviors, with religious beliefs playing a significant role [33]. The literature suggests that, especially during stressful events, religion, norms, and beliefs influence social and economic systems related to health and illness. These factors can encourage or hinder healthy behavior while shaping individual attitudes and beliefs [34].

During global health emergencies, panic can arise, leading to border closures, social isolation, cancelation of planned activities or events, and a general atmosphere of fear that can disrupt the perception of safety [35]. In this study, variables such as the participant’s risk group, the presence or absence of a diagnosis of COVID-19 within the home, the level of education and the anxiety due to the pandemic were found to influence whether participants felt safe during the COVID-19 pandemic. Fear of COVID-19 appears to be a significant factor in the adherence to preventive measures against COVID-19. This may suggest that people’s fears related to the coronavirus influence their motivation to comply with health precautions. Therefore, it is important to understand and address these fears for the development and implementation of effective public health strategies. In addition, this finding has led to a focus on the need to consider emotional factors in the design of health policies and awareness campaigns within the community. The literature reveals that compliance with preventive measures was solely determined by the fear of COVID-19 in a reported study [36]. Emphasizing the importance of decreasing fatalistic tendencies and fostering confidence in the efficacy of preventive measures is crucial for promoting protective behavior against COVID-19 [37]. Fatalistic perception involves relying on God or supernatural powers to cope with life’s challenges. Those with a fatalistic tendency may feel that they lack control over their lives and the ability to effect change [38, 39].

[25]. In a factorial ANOVA analysis aimed to determine whether the participant’s risk group and their health status anxiety differentiate fatalistic tendencies, it was observed that health fatalism was more prevalent among individuals aged 65 and older. In the healthcare workers in this study, it has been observed that characteristics such as gender, income level, and perceptions of general health and mental health do not create a difference in the fatalistic approach. Other research indicates that people can experience depression, feelings of imminent doom, and even contemplate suicide as a result of the COVID-19 pandemic [40]. The study by Shehu and Rao found that a significant proportion of participants had a fatalistic outlook on the COVID-19 pandemic, perceiving it as an event determined by a higher power [41]. Bobov and Capik’s research identified factors such as gender, low education, marital status, lack of social health security, low income, unemployment, and chronic diseases as significant contributors to a fatalistic outlook. Franklin et al. noted that women and individuals with poor health perception exhibited a higher tendency towards fatalism [25], while Ayoğdu’s study reported a stronger tendency toward fatalism among those with lower educational attainment [42]. Considering that COVID-19 can have more severe consequences in elderly individuals, strict isolation and protective measures are of utmost importance, particularly for high-risk groups [43]. Pan et al. emphasized the role of beliefs in shaping fatalistic attitudes toward health, noting that fatalism becomes more pronounced as individuals’ perception of their ability to control their lives diminishes [44]. The study by Chen et al. examining the factors that influence the health practices of individuals over 65 years of age within the context of religious beliefs identified several influential factors, including age, gender, marital status, income level, and place of residence [45]. Given that the elderly face a natural decline in their capabilities during “old age,” a higher inclination toward fatalism in this group is understandable. Consequently, due to the high contagion potential of COVID-19, the implementation of strict isolation and protective measures becomes crucial, particularly for individuals at higher risk [46].

4.1 Limitations

This study has several limitations. The primary limitation is that it was a cross-sectional study, which means that the identified effects cannot establish causal relationships. Second, due to global lockdowns, the researchers were unable to conduct fieldwork to collect data. Instead, electronic tools were utilized for data collection via sampling. The final limitation of the study is that it only relied on a questionnaire as a data collection tool, with no direct observations conducted.

5 Conclusions

The participants in this study showed a moderate tendency towards a fatalistic approach. It was evident that a significant proportion of them expressed concerns about possible health changes due to the pandemic, with a notable impact on their mental well-being. The study revealed that women, individuals with higher incomes and those who perceived themselves to be in good health were more likely to adopt health protective measures. Furthermore, participants in high-risk groups tended to exhibit a fatalistic outlook, and specific sociodemographic characteristics influenced their knowledge, attitudes, and behaviors related to health protection and improvement. The research has found that all healthcare workers, elderly people, people with chronic illnesses, and pregnant women included in this study exhibited a remarkably high tendency towards fatalistic behavior. Being over the age of 65, having a university-level education, having negative health behaviors such as alcohol consumption, believing that one’s health is good, and having a strong belief in taking sufficient measures against the pandemic were identified as differentiating factors.

To better manage the pandemic among high-risk groups, it is advisable to prioritize their needs and actively promote positive health behaviors and beliefs. The use of various communication tools and the immediate opportunity to interact with healthcare professionals can be instrumental in this effort.

Ethics statement

This study was approved by the Gumushane University Scientific Research and Publication Ethics Committee (21452481 –100 –E.12363) and the Ministry (2020 –05 –01T01 _ 41 _ 44).

Informed consent

The information text, aligned with the Helsinki criteria, was presented to the participants along with the questionnaire.

Reporting guidelines

In this research carried out with a quantitative method, the STROBE reporting guideline has been used.

Footnotes

Acknowledgments

The authors extend their appreciation to the survey respondents for their valuable time and participation.

Conflict of interest

The authors have declared no conflicts of interest.

Funding

This research did not receive specific funding.

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