BIOPSYCHOSOCIAL DATA

School teachers (n = 75) from the Paraná State Education Network took part in the study, most of whom were women (76%, Table 1). Table 1 shows the gender frequencies of the participants analyzed in the study. The table is made up of: ‘Gender’, ‘Counts’, ‘Total %’ and ‘Cumulative %’. The "Gender" column indicates the two genders considered in the analysis, which are "Female" and "Male". The "Counts" column represents the number of participants in each gender category; in this case, 57 female participants and 18 male participants. The "Total %" column shows the percentage that each gender category represents in relation to the total number of participants. For the female gender, the percentage is 76%, indicating that women represent the majority of participants, while, for the male gender, the percentage is 24%, indicating a smaller proportion in relation to the total. The "Cumulative %" column indicates the cumulative percentage of participants up to the category in question. In the case of females, the cumulative percentage is also 76%, as this is the only category listed. For males, the cumulative percentage is 100%, as it represents all the remaining participants after females. This table provides information on the gender frequencies of the study participants, allowing an understanding of the distribution of these categories in the sample analyzed.

The teachers were generally over 41 years of age and, when broken down by gender, the data shows that 60% were women over 41, characterizing the sample. Most of the teachers who took part were permanent teachers (74.7%) and only a third of the teachers had a temporary employment contract (24.3%). Most teachers work between 20 and 40 hours a week, representing 94.7% of the sample (Table 2).

Still with regard to Table 1, more detailed and relevant observations could be made based on the data provided: the 40-hour workload and the permanent employment contract. This combination has the highest number of participants, with 41 individuals, representing 54.7% of the total number of participants. This shows that most of the teachers analyzed have a contract and work 40 hours a week. As for the 20-hour workload: both temporary and permanent teachers have participants in this workload, with six and 12 individuals, respectively. These figures represent 8 and 16% of the total number of participants. This indicates that a significant proportion of teachers work 20 hours a week, regardless of the type of contract. Regarding the 60-hour workload: this workload has the lowest number of participants, with only one individual working on a temporary basis (1.3% of the total) and three working on a permanent basis (4% of the total). This suggests that a minority of the teachers work a 60-hour week. In the last column, the "Cumulative %" column indicates the cumulative percentage of participants in each category. As the table progresses, the cumulative percentage increases, reaching 100% for the 60-hour workload and the permanent contract. This means that all participants are accounted for up to that point.

Regarding the data from the biopsychosocial questionnaire, most of the teachers do not engage in any other professional activities and are fully dedicated to their work.

This analysis of Table 2, in short, allows us to identify the working hour distribution and the participating teachers’ employment relationships. The results indicate a predominance of 40-hour workloads and contracts, as well as the presence of a considerable number of teachers with 20-hour workloads. This information may be relevant to understanding the characteristics of the group studied and helping to draw up policies and interventions aimed at the teachers’ working environment and quality of life.

Most of the teachers surveyed have teaching experience, with 54.7% having been in the classroom for more than 15 years (Table 3). This table provides information on the distribution of the study participants’ teaching experience. It can be noted that most participants have more than 20 years of teaching, representing 34.7% of the total. In addition, the 11-to-15 years and 16-to-20 years brackets also have a significant representation, with 22.7 and 20% of the total, respectively. The zero to five and 6-to-10-year groups, on the other hand, have a lower number of participants, corresponding to 16 and 6.7% of the total, respectively. The data in Table 3 allow us to understand the distribution of the participants’ length of time teaching and are useful for identifying different levels of experience within the group studied.

Table 4 shows the frequencies of the study participants’ self-reported illnesses. The column "Do you report an illness?" indicates whether the participants reported having any illnesses. There are two categories in this column: "No" and "Yes". The column "Do you have any illnesses?" specifies the type of illness reported by the participants who declare having any illnesses. There are two categories in this column: "Physical" and "Mental". The "Counts" column shows the number of participants in each category. For example, 31 participants do not report having any illnesses. The "Total %" column indicates the percentage that each category represents in relation to the total number of participants. For example, 43.1% of participants do not report having any illnesses. The "Cumulative %" column indicates the cumulative percentage of participants up to the category in question. For example, the cumulative percentage for the "Yes — Physical" category is 65.3%, while the cumulative percentage for the "Yes — Mental" category is 100%.

Thus, when teachers were asked about a diagnosis of illness, 56.9% of them claimed to have some kind of illness, 34.7% of which were mental, which may be associated with work-related stress (Table 4). The reasons for absence due to illness amounted to 55.80%. Most diagnoses are focused on mental and emotional health. The illnesses diagnosed and being treated include anxiety, depression, hypertension, and diabetes, and most teachers are taking medication. During the COVID-19 pandemic, the greatest difficulties were related to adapting to educational context changes (69.7%) and social confinement (47.4%).

Table 4 provides information on the self-declaration of illnesses by the study participants. It can be observed that only 43.1% of the participants did not declare to have any illnesses. On the other hand, 22.2% of participants reported having physical illnesses, and 34.7% reported having mental illnesses. It is important to note that the results are based on participants’ self-reporting and have not been verified by health professionals. Nevertheless, this information provides relevant insights into teachers’ health and the need to implement support policies and strategies for teachers.

The data presented in the survey (Tables 1 to 4) shows that most Paraná school teachers are women over 41 years old, with teaching experience, working on a permanent basis for 20 to 40 hours a week. The majority do not have any other professional activities, and more than half of the teachers reported having some kind of illness, most of which are mental health conditions.

Studies on teacher absence due to illness can be caused by a wide range of conditions, including physical, mental, and emotional. These studies tend to point to the impact of teacher sick leave on the quality of education, the teachers’ health and well-being, and the effectiveness of the education system (^{Coledam et al., 2020}; ^{Salvagioni et al., 2022}; ^{Medeiros et al., 2023}).

Studies on teacher absenteeism due to physical illness usually assess the prevalence of conditions such as chronic diseases, injuries, and accidents at work. On the other hand, studies on teacher absenteeism due to mental and emotional illness usually investigate the incidence of problems such as anxiety, depression, stress, burnout, and more serious mental disorders.

A study published by ^{McIntyre, McIntyre, and Francis (2017)} evaluated the relationship between stress and medical leave among teachers in the United States. The results showed that teachers who reported higher levels of stress were more likely to have health-related absences. Another study published by Cheng, Huang and Chan (2019) investigated the prevalence of burnout among high school teachers in China. The results showed that more than a third of teachers reported symptoms of burnout and that the presence of burnout was associated with a higher risk of health leave.

Taken together, these data point to factors that can directly interfere with teachers’ stress, since their job involves different factors. In addition, most teachers work on a full-time basis, which can lead to greater emotional strain. The presence of illnesses, especially of a mental nature, can aggravate stress, as these conditions can be associated with symptoms such as anxiety and depression. Thus, the data presented corroborates studies in this area and justifies the target audience chosen for the research and the impacts of one's mentality in an environment known for its stress factors.

To check the mindset of the teachers (n = 75), the SMM was applied. When the scale was applied, a score was obtained which made it possible to find two groups relating to stress mentality. The data showed that 85.3% of the teachers surveyed have a debilitating mentality, which views stress as something negative, and 14.7% have an enhancing mentality (Figure 1). There is a significant difference between the groups (p < 0.05), and the Shapiro-Wilk normality test indicates no violation of the normality assumption. This supports the distinction between the groups and suggests that changing one's mentality is possible.

Source: Elaborated by the authors.

Figure 1 Mentality of public-school teachers in the state of Paraná, divided into two groups: debilitating and enhancing mentality. The data was submitted to the Shapiro-Wilk normality test and then analyzed using an ANOVA with a significance level set at *p < 0.05. 

Figure 1 represents the response distribution in relation to the enhancing or debilitating mindset, with the X axis representing these mindsets and the Y axis showing the response counts. Of the 75 participants included in the study, more than 60% showed a debilitating mindset, while the rest showed an enhancing mindset. To assess the normality of the data, the Shapiro-Wilk normality test was performed. This test is commonly used to check whether a sample follows a normal distribution. After confirming the normality of the data, an ANOVA was carried out. A statistically significant difference was found between the groups (Figure 1).

The view that stress is exclusively negative, disseminated by the media and common sense, is characterized by a debilitating mindset. ^{Crum et al. (2017)} argue that this debilitating mindset can have a negative impact on health, increasing the likelihood of chronic diseases such as diabetes, cardiovascular disease, and cancer. On the other hand, an enhancing mindset can improve health and resilience by promoting a healthy lifestyle and increasing the ability to cope with stress and challenges. In addition, it has also been suggested that mindset can influence academic and professional performance. A debilitating mindset can limit the capacity for learning and development while an enhancing mindset can stimulate creativity and innovation.

Figure 2 shows the self-reported source of stress in the two different groups. The columns represent the different sources of stress, whether work or other reasons. The Y axis shows the number of participants who reported each response. This figure allows us to visualize the distribution of participants in relation to the sources of stress mentioned. We can see how many participants indicated work as a source of stress and how many indicated other reasons as causing stress. This graphic representation helps us to understand the relative importance of these different sources of stress for the study participants. We can analyze the frequency of responses in each category and identify possible patterns or differences between the groups. Thus, after observing the predominant mentality among teachers in public schools, they were asked to name the main stress factors (Figure 2). The results were analyzed and divided into two categories: work-related stress (74%) and other factors (26%). Of the 75 participants, only two said they had no stress at all.

Source: Elaborated by the authors.

Figure 2 Self-reported source of stress in different groups. The columns represent work or other reasons as a source of stress. The Y axis is represented by the count of participants for a given answer. 

Figure 3 illustrates the distribution of stress intensity. The Y-axis represents the number of participants who gave each response in relation to the intensity of stress. The X axis represents the categories of stress intensity, which are "Not perceived", "Moderate stress" and "Extreme stress". The columns represent the distribution of stress intensity by source. The black column represents stress from work, while the gray column represents other sources of stress. This figure allows us to visualize how the intensity of stress is distributed between the different sources (work or other reasons) mentioned by the participants. We can see how many participants reported each level of stress intensity for work and for other sources of stress. Thus, the third question described the amount of stress felt by the teacher, which could be no stress, a moderate amount or an extreme amount. Among the participants, 1.3% reported having no stress at all, 61.3% perceived their stress as moderate, and 37.3% perceived it as extremely high. Figure 3 divides the groups and illustrates that, both for those reporting moderate and for those reporting extreme stress, work is the primary cause.

Source: Elaborated by the authors.

Figure 3 Distribution of stress intensity by source. The graph shows the frequency of participants who reported each level of stress (not perceived, moderate and extreme), categorized by source of stress (work and other sources). The black columns represent the frequency of participants who indicated work as the main source of stress, while the gray columns represent other sources. 

Figure 4 shows the score obtained in the stress test in relation to the participants’ gender. The X axis represents the two gender categories: "Male" and "Female". The Y axis represents the score obtained in the stress test. The circles represent the mean score for each gender category, while the squares represent the median. These measures summarize the central value of the scores for each group. The presence of an asterisk accompanied by the note "*p < 0.01" indicates the level of statistical significance found in the comparative analysis of the groups. This means that there is a statistically significant difference between the stress scores of the male and female participants. This figure allows us to visually compare the average stress scores between the gender groups and identify whether there are statistically significant differences between them. We can see if there is a disparity in the perception of stress between men and women. Finally, the scale scores were correlated with various biopsychosocial aspects such as gender, age, length of time teaching, workload and other health aspects. The significant correlations pointed to stress as being related to the nature of a teacher's work. Gender was the only correlated factor in the mentality scale on stress, with a significant difference between men and women (p < 0.01).

Source: Elaborated by the authors.

Figure 4 Mean and median stress test scores by gender. The scores obtained in the stress test were compared between men and women. The mean and median scores for each gender are represented by circles and squares, respectively. The asterisk (*p < 0.01) indicates a statistically significant difference between the groups, as determined in the comparative analysis. 

Although work-related stress is an individual experience and can be influenced by various factors, scientific evidence indicates that women may be more susceptible to experiencing work-related stress than men in some professions, including teaching.

The data in this article corroborates a study conducted by ^{Medeiros et al. (2023)} which analyzed the stress levels of primary and secondary school teachers in relation to different variables, including gender. The results revealed that women reported higher levels of stress than men in relation to some factors, such as the conflict between work and family and work overload. Another study, carried out by ^{Carlotto et al. (2014}) and ^{Redondo-Flórez et al. (2020)}, explored gender differences among teachers and found that women experienced higher levels of stress than men, particularly due to factors like overwork and work-life conflict, reinforcing the findings of this survey.

Next, one-way ANOVA tests were carried out comparing teachers’ mentality with the main source of stress and the extent to which this stress is perceived. There were correlations between the MMS and work-related stress, which confirmed teacher work (routines and demands) as the main factor (p < 0.05). The scale was also correlated with perceived stress and once again the correlation was significant (p < 0.01), indicating that stress is linked to the teaching profession.

Analysis of the other elements of the biopsychosocial questionnaire, such as age, marital status, education, type of work, professional relationship, workload, and length of time teaching did not reveal any significant correlations, concentrating stress as inherent in the wear and tear of the teaching profession.

Given this situation, it is believed that actions are needed to address workplace stress, as it is usually perceived negatively (^{Mazon and Leite, 2013}). In other words, the approach to stress is generally debilitating because of the very construction of the concept of stress, and it is necessary to develop new forms of more positive and constructive responses to deal with stress.