16 Work and mental health of teachers in the interior of Ceará during the COVID-19 pandemic: conclusive technical research report

Authors

Affiliations

David Sousa Rodrigues

Universidade Federal do Ceará

Esthela Sá Cunha

Faculdade 05 de Julho

Centro Universitário INTA

Francisco Pablo Huascar Aragão Pinheiro

Universidade Federal do Ceará

Quitéria Alves Melo

Universidade Federal do Ceará

Iratan Bezerra de Sabóia

Universidade Federal do Ceará

16.1 Introduction

Show the code

# Setup
## Packages

library(tidyverse)
library(janitor)
library(careless)
library(gt)
library(scales)
library(sysfonts)
library(extrafont)
library(showtext)
library(ggthemes)
library(scales)
library(cowplot)
library(patchwork)
loadfonts(device = "win")
theme_set(theme_cowplot())

## Data collection

sample <- read_csv("./data/dados_municipios.csv")

df <- read_csv("./data/dados_municipios.csv")

nomes_eactdr <- read_csv("./data/nomes_eactdr.csv")

## Creation of an identification variable
df <- df |> 
  mutate(
    id = 1:nrow(df), .before = 1
  )

Show the code

# Data screening
## Evaluation of the limits of quantitative variables

data_screening <- df |> 
  summarise(
    across(c(where(is.double)), list(
      min = \(x) min(x, na.rm = T),
      max = \(x) max(x, na.rm = T)
    ))
  ) |> 
  pivot_longer(
    cols = everything(),
    names_to = c("measure", "stat"),
    names_sep = "_(?=[^_]+$)"
  ) |> 
  pivot_wider(
    id_cols = measure,
    names_from = stat,
    values_from = value
  )

## DASS-21

data_screening |> 
  filter(str_detect(measure, "dass")) |> 
  print(n = Inf)

## EACTDR

data_screening |> 
  filter(str_detect(measure, "ct")) |> 
  print(n = Inf)

## Age

data_screening |> 
  filter(str_detect(measure, "\\bidade\\b"))

## Income

data_screening |> 
  filter(str_detect(measure, "renda"))

df |> 
  ggplot(aes(renda_mensal_familiar)) +
  geom_boxplot() +
  coord_flip()

df |> 
  ggplot(aes(renda_mensal_familiar)) +
  geom_histogram()

df |> 
  filter(renda_mensal_familiar >= 20000) |> 
  select(id, renda_mensal_familiar, 
         dependentes_da_renda) |> 
  arrange(desc(renda_mensal_familiar))
  
df |> 
  filter(renda_mensal_familiar > 40000) 

## Didactic activities

data_screening |> 
  filter(str_detect(measure, "sinc"))

## Careless responses

df <- df |> 
  rowwise() |> 
  mutate(
    dass_media = mean(c_across(dass1:dass21)),
    eactdr_media = mean(c_across(ct1:ct40))
  ) |> 
  ungroup()

df |> 
  filter(dass_media %in% c(0:3)) |> 
  select(id, dass_media)

df |> 
  filter(eactdr_media %in% c(1:5)) |> 
  select(id, eactdr_media)

df |> 
  filter(dass_media %in% c(0:3), 
         eactdr_media %in% c(1:5))


### Longstring Index

careless_long_dass  <- df |> 
  select(starts_with("dass")) |> 
  longstring(avg = T) |> 
  tibble()


long_dass <- careless_long_dass |> 
  select(longstr) |> 
  pull()

careless_long_eactdr  <- df |> 
  select(starts_with("ct")) |> 
  longstring(avg = T) |> 
  tibble()

long_eactdr <- careless_long_eactdr |> 
  select(longstr) |> 
  pull()

findoutlier <- function(x) {
  return(x < quantile(x, .25) - 1.5*IQR(x) | x > quantile(x, .75) + 1.5*IQR(x))
}

df <- df |> 
  mutate(
    long_dass = long_dass,
    long_eactdr = long_eactdr,
    out_long_dass = case_when(findoutlier(long_dass) ~ id, 
                              .default = NA),
    out_long_eactdr = case_when(findoutlier(long_eactdr) ~ id, 
                                .default = NA)
  ) |> 
  relocate(long_dass, out_long_dass, 
           long_eactdr, out_long_eactdr,
           .after = id)

df |> 
  ggplot(aes(long_dass)) +
  geom_boxplot(outlier.colour = "#D55E00") +
  geom_jitter(aes(x = long_dass, y = 0,
                  color = "#D55E00")) +
  coord_flip() +
  theme(legend.position = "none")

df |> 
  ggplot(aes(long_eactdr)) +
  geom_boxplot(outlier.colour = "#D55E00") +
  geom_jitter(aes(x = long_dass, y = 0,
                  color = "#D55E00")) +
  coord_flip() +
  theme(legend.position = "none")


df |> 
  filter(!is.na(out_long_dass) & !is.na(out_long_eactdr)) |> 
  select(id) |> 
  pull() |> 
  str_c(collapse = ",")

Show the code

#### 20 cases considered as careless responses regarding the DASS-21 and EACDR were removed

ids_descuidados <- c(116,157,177,275,297,
                     305,319,343,367,385,
                     397,398,403,413,439,
                     488,679,719,724,726)


df <- df |> 
  filter(!(id %in% ids_descuidados))

Show the code

# Data manipulation
## Creation of per capita income variable

df <- df |> 
  mutate(
    rper = renda_mensal_familiar/dependentes_da_renda,
    .after = dependentes_da_renda
  )

## Creation of mental health variables

df <- df |> 
  rowwise() |> 
  mutate(
    depressao = sum(dass16, dass17,dass10,
                    dass13,dass21,dass3,
                    dass5),
    ansiedade = sum(dass20, dass9, dass19,
                    dass2, dass15, dass7,
                    dass4),
    estresse = sum(dass18, dass6, dass8,
                   dass12,dass11,dass1,
                   dass14)
  ) |> 
  ungroup()

## Creation of qualitative mental health variables

labels <- c("Normal","Mild","Moderate","Severe","Extremely severe")

df <- df |> 
  mutate(depre_2 = depressao*2,
         ansie_2 = ansiedade*2,
         estre_2 = estresse*2) |>
  mutate(depre_corte = cut(depre_2,
                           breaks = c(-Inf,9,13

,20,27,+Inf),
                           labels = labels),
         ansie_corte = cut(ansie_2,
                           breaks = c(-Inf,7,9,14,19,+Inf),
                           labels = labels),
         estre_corte = cut(estre_2,
                           breaks = c(-Inf,14,18,25,33,+Inf),
                           labels = labels))

The COVID-19 pandemic brought challenges to various areas of society. In the field of education, it was necessary to start emergency teaching that kept students and teachers in their homes to avoid the spread of the virus. The remote teaching model emerged so that educational activities could continue without offering the risk of contagion (Pereira et al., 2020; I. B. da Silva et al., 2022). Despite the widespread adoption of this strategy by educational institutions, this change had an impact on teachers’ mental health indices (I. B. da Silva et al., 2022).

Due to the abrupt modification of the work context during the pandemic, teachers had to deal with issues that impacted their mental health, such as the need to develop new skills with the use of technologies, high demands for results, and the double workload caused by demands in their home routine (Cândido et al., 2022). In this context, it was identified that receiving demands outside of working hours, losing contact with colleagues, and having difficulty separating private and work life made it difficult to carry out remote activities (Bernardo et al., 2020). It was also noted that the mental illness of this category was associated with news about deaths due to COVID-19, the pressure exerted by educational institutions, the stress load related to personal life, and the fear of death arising from the pandemic (Santos et al., 2021).

Several studies showed high rates of mental disorders among teachers during the remote work period. A study conducted with 125 university teachers from the northeastern hinterland identified prevalence rates of 60% for anxiety and 50.4% for stress (Machado et al., 2022). Additionally, an investigation with teachers from the state public network of Montes Claros, Minas Gerais, found rates of 51.8% and 52.6% for anxiety and depression, respectively (Ruas et al., 2022). In a study conducted with basic education teachers (n = 203) in Peru, teachers showed rates of 87.45% for the presence of anxiety symptoms and 55.8% for the presence of depressive disorders to some degree, while female teachers showed respective rates of 70.3% and 60.4% to some degree (Betancur et al., 2022).

Given this scenario, the Laboratory of Practices and Research in Psychology and Education (LAPPSIE) at the Sobral campus of the Federal University of Ceará partnered with nine municipalities in the state of Ceará to conduct research aimed at evaluating the work context, mental health, and the relationship of teachers with the COVID-19 pandemic during the remote work period. This chapter aims to present the results of this study, characterizing it as a conclusive technical research report.

Since they are neither visible nor tangible, occupational diseases associated with mental health are often not identified as problems arising from the work context. Thus, there is difficulty in establishing a causal link between the two (Borsoi, 2007). In addition, legal and social security views on mental health reinforce the idea that such problems do not stem from work but from individual reality (Sato & Bernardo, 2005).

Thus, situations of mental illness are mostly attributed to the negligence and irresponsibility of the worker. As a result, organizations attempt to conceal the pressures and demands regarding production. The idea of blaming the individual for situations of mental suffering, which are largely produced and intensified by the work context, is reinforced (M. P. da Silva et al., 2016).

In contrast, the field of Worker Health, which underpins this study, seeks to confront this approach that restricts the origin of illness to possible agents or risk situations present in the work environment. Such an approach fails to understand the relationships between work organization and the subjectivity of workers (Lacaz, 2007; Mendes & Dias, 1991). Therefore, the referred approach recognizes the individual as a protagonist, capable of transforming their work reality to reduce health hazards beyond work accidents and diseases. It is proposed that workers be seen as social and political actors capable of intervening in their work context, becoming knowledge producers and health care strategists (Gomez, 2011; Lacaz, 2007).

16.2 Method

16.2.1 Participants

Show the code

# Table for demographics
## Categorical demographics - part 1

demo_cat <- df |> 
  select(c(renda_pos_covid,
           escolaridade,
           estado_civil,
           cor_raca,
           sexo)) |> 
  pivot_longer(cols = everything()) 


demo_cat <- demo_cat |>   
  count(name,
        value) |> 
  group_by(name) |>
  mutate(prop = (n/sum(n))*100 |> 
           round(1)) |>
  ungroup() 


demo_cat <- demo_cat |>
  arrange(factor(name, levels = c("sexo","cor_raca",
                                  "estado_civil","escolaridade",
                                  "renda_pos_covid")),
          desc(n)) |>
  select(value, n, prop)


## Categorical demographics - part 2

sexo <- tibble(
  value = "Sex", n = NA, 
  prop = NA
)

raca <- tibble(
  value = "Race", n = NA, 
  prop = NA
)

est_civil <- tibble(
  value = "Marital status", n = NA, 
  prop = NA
  
)

escol <- tibble(
  value = "Education level", n = NA, 
  prop = NA
)

rend_pos <- 
  tibble(
    value = "Income after the start of the pandemic", n = NA, 
    prop = NA
  )

demo_cat <- demo_cat |> 
  add_row(sexo, .before = 1) |> 
  add_row(raca, .before = 4) |> 
  add_row(est_civil, .before = 9) |> 
  add_row(escol, .before = 15 )|> 
  add_row(rend_pos, .before = 22)

## Categorical demographics - part 3

education_levels <- df |>
  select(educacao_infantil:atendimento_educacional_especializado_aee) |> 
  pivot_longer(cols = everything(), cols_vary = "slowest") |> 
  count(name, value) |> 
  group_by(name) |> 
  mutate(prop = ((n/sum(n))*100) |> 
           round(1)) |>
  arrange(desc(n)) |> 
  filter(value == "Sim") |> 
  select(name, n, prop) |>
  mutate(name = case_when(name == "fundamental_ii" ~ "Final years of elementary school",
                          name == "fundamental_i" ~ "Initial years of elementary school",
                          name == "educacao_infantil" ~ "Early childhood education",
                          name == "educacao_de_jovens_e_adultos_eja" ~ "Youth and Adult Education",
                          name == "atendimento_educacional_especializado_aee" ~ "Specialized Educational Assistance")) |> 
  ungroup() |> 
  rename(
    value = name
  )

edu_levels <- 
  tibble(
    value = "Education level", n = NA, 
    prop = NA
  )

education_levels <- education_levels |> 
  add_row(edu_levels, .before = 1)

demo_cat <- demo_cat |> 
  bind_rows(education_levels)

## Dynamic entries table for the text

demo_cat_prop <- demo_cat |> 
  select(value, prop) |> 
  mutate(
    prop = prop/100,
    prop = label_percent(0.1, decimal.mark = ".")(prop)
  )

## Categorical demographics part 4
demo_cat <- demo_cat |> 
  mutate(
    prop = format(round(prop, 1), decimal.mark = ".", 
                  big.mark = ",", nsmall = 1),
    n = as.character(n),
    prop = case_when(prop == "  NA" ~ NA,
                     .default = prop)
  )

## Numerical demographics

demo_num <- df |> 
  summarise(
    across(c(idade, rper), list(
      M = \(x) mean(x, na.rm = T),
      SD = \(x) sd(x, na.rm = T)
    ))
  ) |> 
  pivot_longer(
    cols = everything(),
    names_to = c("value", ".value"),
    names_sep = "_(?!.*_)"
  )

demo_num <- demo_num |> 
  mutate(
    value = case_when(value == "idade" ~ "Age",
                      value == "rper" ~ "Per capita income")
  ) |> 
  rename(
    n = M, 
    prop = SD
  )

demo_num <- demo_num |> 
  mutate(
    across(where(is.double),
           \(x) format(round(x, 1), decimal.mark = ".", 
                       big.mark = ",", nsmall = 1))
  )

num <- tibble(
  value = NA,
  n = "M",
  prop = "SD"
)

demo_num <- demo_num |> 
  add_row(num, .before = 1)

# Demographics table

demo_tbl <- demo_cat |> 
  bind_rows(demo_num) |> 
  rename(
    "Characteristic" = value,
    "%" = prop
  )

## Final table

demo_tbl <- demo_tbl |> 
  mutate(
    Characteristic = case_when(
      Characteristic == "Sexo" ~ "Sex",
      Characteristic == "Feminino" ~ "Female",
      Characteristic == "Masculino" ~ "Male",
      Characteristic == "Raça" ~ "Race",
      Characteristic == "Pardo" ~ "Mixed race",
      Characteristic == "Branco" ~ "White",
      Characteristic == "Preto" ~ "Black",
      Characteristic == "Amarelo" ~ "Asian",
      Characteristic == "Estado civil" ~ "Marital status",
      Characteristic == "Casado(a)" ~ "Married",
      Characteristic == "Solteiro(a)" ~ "Single",
      Characteristic == "União estável" ~ "Stable union",
      Characteristic == "Divorciado(a)" ~ "Divorced",
      Characteristic == "Viúvo(a)" ~ "Widowed",
      Characteristic == "Nível de escolaridade" ~ "Education level",
      Characteristic == "Especialização" ~ "Specialization",
      Characteristic == "Curso superior completo" ~ "Completed higher education",
      Characteristic == "Curso superior incompleto" ~ "Incomplete higher education",
      Characteristic == "Mestrado" ~ "Master's degree",
      Characteristic == "Ensino Médio" ~ "High school",
      Characteristic == "Doutorado" ~ "Doctorate",
      Characteristic == "Renda após o início da pandemia" ~ "Income after the start of the pandemic",
      Characteristic == "Ficou estável" ~ "Stayed stable",
      Characteristic == "Diminuiu" ~ "Decreased",
      Characteristic == "Aumentou" ~ "Increased",
      Characteristic == "Anos finais do ensino fundamental" ~ "Final years of elementary school",
      Characteristic == "Anos iniciais do ensino fundamental" ~ "Initial years of elementary school",
      Characteristic == "Educação infantil" ~ "Early childhood education",
      Characteristic == "Educação de jovens e adultos" ~ "Youth and Adult Education",
      Characteristic == "Atendimento Educacional Especializado" ~ "Specialized Educational Assistance",
      .default =  Characteristic
    )
  )


tabela_demograficos <- demo_tbl |> 
  gt() |> 
  sub_missing(
    missing_text = ""
  ) |>
  tab_options(
    data_row.padding = 1
  ) |> 
  cols_align(align = "center",columns = c(n, "%")
  ) |> 
  tab_style(
    style = cell_text(align = "center"),
    locations = cells_column_labels(columns = "Characteristic")) |> 
  tab_style(
    style = cell_text(indent = px(20)),
    locations = cells_body(columns = 1,
                           rows = c(2:3, 5:8, 10:14, 16:21,
                                    23:25, 27:31))
  ) |> 
  tab_source_note(
    source_note = md("Note. *N* = 838")
  ) |> 
  cols_width(
    "Characteristic" ~ px(200)
  ) |> 
  tab_options(
    table.width = pct(100),
  )

The study included 838 basic education teachers from nine municipalities in the interior of Ceará. 20 cases were removed due to careless responses regarding the scales used. The majority of the sample was composed of women (82.0%) and mixed race individuals (69.2%). The age ranged from 19 to 75 years (M = 39.3; SD = 9.5). Regarding marital status, 49.8% of the participants reported being married. In terms of education, 57.7% declared having completed a specialization. In terms of teaching level, most teachers worked in the final years of elementary school (42.1%).

Show the code

tabela_demograficos

Table 16.1: Sociodemographic characteristics of participants

Characteristic	n	%
Sex
Female	671	82.0
Male	147	18.0
Race
Mixed race	566	69.2
White	177	21.6
Black	64	7.8
Asian	11	1.3
Marital status
Married	407	49.8
Single	284	34.7
Stable union	86	10.5
Divorced	29	3.5
Widowed	12	1.5
Education level
Specialization	472	57.7
Completed higher education	278	34.0
Incomplete higher education	29	3.5
Master's degree	22	2.7
High school	13	1.6
Doctorate	4	0.5
Income after the start of the pandemic
Stayed stable	424	51.8
Decreased	372	45.5
Increased	22	2.7
Education level
Final years of elementary school	344	42.1
Initial years of elementary school	313	38.3
Early childhood education	290	35.5
Youth and Adult Education	27	3.3
Specialized Educational Assistance	17	2.1
	M	SD
Age	39.3	9.5
Per capita income	1,445.9	1,966.7
Note. N = 838

16.2.2 Instruments

The questionnaire used in the survey included the following instruments: Depression, Anxiety, and Stress Scale (DASS-21, Vignola & Tucci, 2014) and the Teacher Remote Work Context Assessment Scale (EACTDR, Cunha, 2023). Additionally, the questionnaire was composed of sociodemographic questions (e.g., gender, race, education level, etc.) and questions related to experiences during the COVID-19 pandemic (e.g., “Did you lose friends or family members due to the COVID-19 pandemic?”).

16.2.2.1 Depression, Anxiety, and Stress Scale

The DASS-21 is an instrument composed of 21 questions that assess anxiety (e.g., “I felt I was close to panic”), depression (e.g., “I had no initiative to do things”), and stress (e.g., “I felt that I was always nervous”). Respondents are instructed to indicate how much the questions applied to them during the past week. The statements range from 0 – “Did not apply at all” – to 3 – “Applied very much, or most of the time.” Thus, the higher the participant’s score, the higher the levels of anxiety, depression, and stress.

The scale also allows for qualitative interpretation. In this case, the scores are summed and multiplied by two, and the parameters are divided into normal (depression: 0 to 9 points; anxiety: 0 to 7 points; stress: 0 to 14 points), mild (depression: 10 to 13 points; anxiety: 8 to 9 points; stress: 15 to 18 points), moderate (depression: 14 to 20 points; anxiety: 10 to 14 points; stress: 19 to 25 points), severe (depression: 21 to 27 points; anxiety: 15 to 19 points; stress: 26 to 33 points), and extremely severe (depression: 28 points or more; anxiety: 20 points or more; stress: 34 points or more).

16.2.2.2 Teacher Remote Work Context Assessment Scale

The EACTDR contains 33 items that assess three dimensions of the remote work context of teachers: working conditions, work organization, and socioprofessional relationships (Ferreira & Mendes, 2008). The first dimension refers to the work infrastructure, work environment, and institutional support, covering elements such as furniture, resources, tools, and organizational support (e.g., “During my work, my internet connection is poor – it frequently drops, is slow, takes time to load videos, etc.”). The second dimension concerns work management practices that guide the activity and manifest in factors such as division of labor, expected productivity, rules, times, and rhythms (e.g., “The deadlines for tasks demanded by the coordination or school management are short”). The last dimension expresses the interactions established at work, such as hierarchical, collective, and external interactions (e.g., “There are communication difficulties between teachers and principals.”). The instrument uses a frequency scale ranging from one (never) to five (always). The items express negative statements, so higher scores imply worse perceptions of the work context (Cunha, 2023).

16.2.3 Procedures

16.2.3.1 Data Collection

Data collection was conducted between February and June 2021. During the research period, teachers were carrying out their activities remotely, mediated by the internet. Only teachers who stated they were working in this modality were included in the study.

16.2.3.2 Data Analysis

The RStudio software (version 2023.12.0+369) was used for data analysis. Descriptive statistics (mean, standard deviation, and frequencies) were performed to verify the prevalence of the targeted variables.

16.2.4 Ethical Considerations

This research was approved by the Research Ethics Committee of the Vale do Acaraú State University (UVA) under CAAE number 51056621.2.0000.5053. This approval aligns with the guidelines defined in Resolutions 466/12 and 510/2016 of the National Health Council.

16.3 Results and Discussion

16.3.1 Experiences Related to the COVID-19 Pandemic

Regarding experiences related to the COVID-19 pandemic, it was observed that most teachers adopted preventive behaviors against coronavirus infection, for example, frequent and reduced access to places and services considered non-essential, such as gyms, bars, restaurants, and shopping centers. Additionally, safety measures such as the use of masks and hand hygiene were frequent practices among teachers. In this context, it is important that education departments reinforce that such precautions positively impact teachers’ physical and mental health, as maintaining healthy habits can reflect in better work performance and quality of life. Thus, lectures and meetings can be promoted so that teachers have accurate information about coronavirus prevention and are encouraged to become disseminators of this knowledge.

Show the code

# Table on behaviors related to the pandemic
academia_c <- df |> 
  count(academia) |> 
  mutate(
    prop = n/sum(n)
  ) 

academia_c <- academia_c |> 
  pivot_wider(
    names_from = academia, 
    values_from = c(n, prop),
    names_vary = "slowest",
  ) |> 
  mutate(
    var = "Gym", 
    .before = 1
  )

bares_c <- df |> 
  count(bares_restaurantes) |> 
  mutate(
    prop = n/sum(n)
  )

bares_c <- bares_c |> 
  pivot_wider(
    names_from = bares_restaurantes,
    values_from = c(n, prop),
    names_vary = "slowest"
  ) |> 
  mutate(
    var = "Bars and restaurants",
    .before = 1
  )

shopping_c <- df |> 
  count(shopping) |> 
  mutate(
    prop = n/sum(n)
  )

shopping_c <- shopping_c |> 
  pivot_wider(
    names_from = shopping,
    values_from = c(n, prop),
    names_vary = "slowest"
  ) |> 
  mutate(
    var = "Shopping",
    .before = 1
  )

mascara_c <- df |> 
  count(mascara) |> 
  mutate(
    prop = n/sum(n)
  )

mascara_c <- mascara_c |> 
  pivot_wider(
    names_from = mascara,
    values_from = c(n, prop),
    names_vary = "slowest"
  ) |> 
  mutate(
    var = "Use of masks",
    .before = 1
  )

higiene_c <- df |> 
  count(higiene_das_maos) |> 
  mutate(
    prop = n/sum(n)
  )

higiene_c <- higiene_c |> 
  pivot_wider(
    names_from = higiene_das_maos,
    values_from = c(n, prop),
    names_vary = "slowest"
  ) |> 
  mutate(
    var = "Hand hygiene",
    .before = 1
  )

comportamentos_tbl <- academia_c |> 
  add_row(bares_c) |> 
  add_row(shopping_c) |> 
  add_row(mascara_c) |> 
  add_row(higiene_c)


comportamentos_tbl <- comportamentos_tbl |>   
  gt() |> 
  cols_align(align = "center", columns = c(!var)) |> 
  tab_style(
    style = cell_text(align = "center"),
    locations = cells_column_labels(columns = "var")
  ) |> 
  tab_spanner(
    label = "Never",
    columns = c(n_0, prop_0)
  ) |> 


  tab_spanner(
    label = "Rarely",
    columns = c(n_1, prop_1)
  ) |> 
  tab_spanner(
    label = "Sometimes",
    columns = c(n_2, prop_2)
  ) |> 
  tab_spanner(
    label = "Often",
    columns = c(n_3, prop_3)
  ) |>
  tab_spanner(
    label = "Almost always",
    columns = c(n_4, prop_4)
  ) |>
  tab_spanner(
    label = "Always",
    columns = c(n_5, prop_5)
  ) |>
  fmt_percent(
    columns = where(is.double),
    decimals = 1, dec_mark = ".",
    sep_mark = ","
  ) |> 
  cols_label( 
    var = "Variable",
    n_0 = md("*n*"), prop_0 = md("*%*"),
    n_1 = md("*n*"), prop_1 = md("*%*"),
    n_2 = md("*n*"), prop_2 = md("*%*"),
    n_3 = md("*n*"), prop_3 = md("*%*"),
    n_4 = md("*n*"), prop_4 = md("*%*"),
    n_5 = md("*n*"), prop_5 = md("*%*")
  )

Show the code

comportamentos_tbl

Table 16.2: Behaviors related to the COVID-19 pandemic

Variable	Never		Rarely		Sometimes		Often		Almost always		Always
Variable	n	%	n	%	n	%	n	%	n	%	n	%
Gym	732	89.5%	17	2.1%	19	2.3%	19	2.3%	7	0.9%	24	2.9%
Bars and restaurants	683	83.5%	77	9.4%	37	4.5%	12	1.5%	5	0.6%	4	0.5%
Shopping	598	73.1%	116	14.2%	51	6.2%	34	4.2%	14	1.7%	5	0.6%
Use of masks	4	0.5%	2	0.2%	4	0.5%	9	1.1%	12	1.5%	787	96.2%
Hand hygiene	3	0.4%	6	0.7%	5	0.6%	22	2.7%	66	8.1%	716	87.5%

Show the code

# Dynamic data for the text: impacts of COVID-19
grupo_covid <- df |> 
  count(grupo_de_risco_dic) |> 
  mutate(
    prop = n/sum(n),
    var = "grupo_de_risco_dic"
  ) |> 
  select(!n) |> 
  pivot_wider(values_from = "prop", 
              names_from = grupo_de_risco_dic)


diag_covid <- df |> 
  count(diagnostico_de_covid) |> 
  mutate(
    prop = n/sum(n),
    var = "diagnostico_de_covid"
  ) |> 
  select(!n) |> 
  pivot_wider(values_from = "prop", 
              names_from = diagnostico_de_covid) 

perda_covid <- df |> 
  count(perdeu_amigo_familiar_covid) |> 
  mutate(
    prop = n/sum(n),
    var = "perdeu_amigo_familiar_covid"
  ) |> 
  select(!n) |> 
  pivot_wider(values_from = "prop", 
              names_from = perdeu_amigo_familiar_covid)

covid <- grupo_covid |> 
  bind_rows(diag_covid) |> 
  bind_rows(perda_covid) 

covid <- covid |> 
  select(!Não) |> 
  mutate(
    Sim = label_percent(0.1, decimal.mark = ".")(Sim)
  )

It was also found that 24.2% of the teachers belonged to the risk group for COVID-19, 24.4% were infected with the coronavirus. Furthermore, 62.5% lost friends or family members due to the pandemic.

Show the code

p_diagnostico <- df |> 
  ggplot(aes(diagnostico_de_covid,
             y = after_stat(count/sum(count)),
             fill = diagnostico_de_covid)) +
  geom_bar() +
  labs(
    x = NULL, 
    y = NULL,
    title = "Diagnosis"
  ) +
  geom_text(aes(label = percent(after_stat(count/sum(count)), 
                                0.1, decimal.mark = ".")),
            stat = "count",
            nudge_y = 0.03)

p_diagnostico <- p_diagnostico +
  scale_y_continuous(labels = percent) +
  scale_fill_manual(values = c("#0072B2", "#D55E00")) +
  theme(
    legend.position = "none",
    text = element_text(family = "Source Sans Pro"),
    plot.title = element_text(hjust = 0.5)
  )

p_g_de_risco <- df |> 
  ggplot(aes(grupo_de_risco_dic,
             y = after_stat(count/sum(count)),
             fill = grupo_de_risco_dic)) +
  geom_bar() +
  labs(
    x = NULL, 
    y = NULL,
    title = "Risk Group"
  ) +
  geom_text(aes(label = percent(after_stat(count/sum(count)), 
                                0.1, decimal.mark = ".")),
            stat = "count",
            nudge_y = 0.03)

p_g_de_risco <- p_g_de_risco + 
  scale_y_continuous(labels = percent) +
  scale_fill_manual(values = c("#0072B2", "#D55E00")) +
  theme(
    legend.position = "none",
    text = element_text(family = "Source Sans Pro"),
    plot.title = element_text(hjust = 0.5)
  )

p_perdas <- df |> 
  ggplot(aes(perdeu_amigo_familiar_covid,
             y = after_stat(count/sum(count)),
             fill = perdeu_amigo_familiar_covid)) +
  geom_bar() +
  labs(
    x = NULL, 
    y = NULL,
    title = "Losses"
  ) +
  geom_text(aes(label = percent(after_stat(count/sum(count)), 
                                0.1, decimal.mark = ".")),
            stat = "count",
            nudge_y = 0.03)

p_perdas <- p_perdas +  
  scale_y_continuous(labels = percent) +
  scale_fill_manual(values = c("#0072B2", "#D55E00")) +
  theme(
    legend.position = "none",
    text = element_text(family = "Source Sans Pro"),
    plot.title = element_text(hjust = 0.5)
  )

p_g_de_risco + p_diagnostico + p_perdas

The image displays three bar charts representing statistical data related to the impact of COVID-19 on a group of teachers. In the first chart, titled 'Risk Group', two bars show that 76% of teachers did not belong to the risk group for COVID-19 (blue bar), while 24% did (orange bar). The second chart, 'Diagnosis', follows a similar pattern, with 76% of teachers not infected with the coronavirus (blue bar) and 24% infected (orange bar). The third chart, 'Losses', shows an inversion in colors, indicating that 38% of teachers did not lose friends or family members due to the pandemic (blue bar), compared to 62% who did (orange bar). Each bar is accompanied by the respective percentage value. The charts are simple, with a white background and clearly marked categories, making it easy to interpret the visual data presented. — Figure 16.1: Impacts of COVID-19 on teachers

Belonging to the group of people vulnerable to the virus can mean an unfavorable experience, as these individuals may manifest feelings of fear due to the potential complications inherent to the disease (Lindemann et al., 2021). Being diagnosed with COVID-19 can bring adverse consequences for teachers’ health and routine, as, besides the physical implications of the disease and the risk of death, infected people may experience greater social isolation and fear of transmitting the virus to their families (Mazza et al., 2020). Regarding losses due to the coronavirus, containment measures for SARS-CoV-2 made the grieving experience difficult, as many people could not say goodbye to their loved ones through funeral rituals (Crepaldi et al., 2020).

In this sense, it is essential that managers disseminate information about the importance of testing in the face of symptoms indicative of COVID-19, the need for proper medical follow-up, and the use of available vaccines. It is also understood that teachers can be affected in various ways by the consequences of the pandemic, and it is up to the administrations to promote environments that make issues such as grief less harmful.

16.3.2 Teaching During Remote Work

Regarding the teaching modality, it was found that teachers carried out asynchronous activities more frequently. It was also found that student participation in these activities was higher compared to the synchronous modality. Other studies have shown that a significant portion of students did not have quality equipment and internet to follow remote classes, which may partially explain the lower participation in real-time activities (Vazquez & Pesce, 2022).

Furthermore, other investigations revealed that teachers also had difficulties with the use of technological resources for online work (Stang-Rabrig et al., 2022), as well as insufficient training for using digital tools. Thus, it is necessary to implement training courses that facilitate the use of technological resources by teachers and guide them on using specific didactic strategies for this context.

Moreover, in an emergency situation, it is essential that teachers have support for acquiring adequate equipment and access to quality internet. In the case of students, the departments should provide the necessary equipment and conditions for the students to follow the classes properly.

Show the code

atividades_sincronas_c <- df |> 
  count(atividades_sincronas) |> 
  mutate(
    prop = n/sum(n)
  )

atividades_sincronas_c <- atividades_sincronas_c |> 
  pivot_wider(
    names_from = atividades_sincronas, 
    values_from = c(n, prop),
    names_vary = "slowest",
  ) |> 
  mutate(
    var = "Conducting synchronous activities", 
    .before = 1
  )

alunos_participam_sincronas_c <- df |> 
  count(alunos_participam_sincronas) |> 
  mutate(
    prop = n/sum(n)
  )

alunos_participam_sincronas_c <- alunos_participam_sincronas_c |> 
  pivot_wider(
    names_from = alunos_participam_sincronas,
    values_from = c(n, prop),
    names_vary = "slowest"
  ) |> 
  mutate(
    var = "Student participation in synchronous activities",
    .before = 1
  )

atividades_assincronas_c <- df |> 
  count(atividades_assincronas) |> 
  mutate(
    prop = n/sum(n)
  )

atividades_assincronas_c <- atividades_assincronas_c |> 
  pivot_wider(
    names_from = atividades_assincronas,
    values_from = c(n, prop),
    names_vary = "slowest"
  ) |> 
  mutate(
    var = "Conducting asynchronous activities",
    .before = 1
  )

alunos_participam_assincronas_c <- df |> 
  count(alunos_participam_assincronas) |> 
  mutate(
    prop = n/sum(n)
  )  

alunos_participam_assincronas_c <- alunos_participam_assincronas_c |> 
  pivot_wider(
    names_from = alunos_participam_assincronas,
    values_from = c(n, prop),
    names_vary = "slowest"
  ) |> 
  mutate(
    var = "Student participation in asynchronous activities",
    .before = 1
  )

ensino_tbl <- atividades_sincronas_c |> 
  add_row(alunos_participam_sincronas_c) |> 
  add_row(atividades_assincronas_c) |> 
  add_row(alunos_participam_assincronas_c)

ensino_tbl <- ensino_tbl |> 
  gt() |> 
  cols_align(align = "center", columns = c(!var)) |> 
  tab_style(
    style = cell_text(align = "center"),
    locations = cells_column_labels(columns = "var")
  ) |> 
  tab_spanner(
    label = "Never",
    columns = c(n_1, prop_1)
  ) |> 
  tab_spanner(
    label = "Rarely",
    columns = c(n_2, prop_2)
  ) |> 
  tab_spanner(
    label = "Sometimes",
    columns = c(n_3, prop_3)
  ) |> 
  tab_spanner(
    label = "Often",
    columns = c(n_4, prop_4)
  ) |>
  tab_spanner(
    label = "Almost always",
    columns = c(n_5, prop_5)
  ) |> 
  tab_spanner(
    label = "Always",
    columns = c(n_6, prop_6))


ensino_tbl <- ensino_tbl |>
  fmt_percent(
    columns = where(is.double),
    decimals = 1, dec_mark = ".",
    sep_mark = ","
  ) |> 
  cols_label( 
    var = "Variable",
    n_1 = md("*n*"), prop_1 = md("*%*"),
    n_2 = md("*n*"), prop_2 = md("*%*"),
    n_3 = md("*n*"), prop_3 = md("*%*"),
    n_4 = md("*n*"), prop_4 = md("*%*"),
    n_5 = md("*n*"), prop_5 = md("*%*"),
    n_6 = md("*n*"), prop_6 = md("*%*")
  )

Show the code

ensino_tbl

Table 16.3: Teaching During Remote Work

Variable	Never		Rarely		Sometimes		Often		Almost always		Always
Variable	n	%	n	%	n	%	n	%	n	%	n	%
Conducting synchronous activities	142	17.4%	70	8.6%	97	11.9%	125	15.3%	96	11.7%	288	35.2%
Student participation in synchronous activities	148	18.1%	105	12.8%	150	18.3%	155	18.9%	113	13.8%	147	18.0%
Conducting asynchronous activities	13	1.6%	6	0.7%	26	3.2%	54	6.6%	95	11.6%	624	76.3%
Student participation in asynchronous activities	8	1.0%	14	1.7%	51	6.2%	144	17.6%	200	24.4%	401	49.0%

16.3.3 Work Context

The evaluation of teachers’ work context showed that, in terms of work organization, the time for preparing remote activities, the number of students to accompany, and the invasion of work into rest hours were the elements worst rated by teachers. These results can be seen in Table 16.4.

Show the code

organizacao_do_trabalho <- df |> 
  summarise(
    across(c(ct1, ct4, ct7,
             ct8, ct12, ct13,
             ct14, ct17, ct19,
             ct21, ct22, ct23,
             ct24, ct30, ct37), list(
               M = \(x) mean(x, na.rm = T),
               SD = \(x) sd(x, na.rm = T)
             ))
  ) |> 
  pivot_longer(
    cols = everything(),
    names_to = c("var", ".value"),
    names_sep = "_"
  ) |> 
  arrange(desc(M))

itens_organizacao <- organizacao_do_trabalho |> 
  select(var) |> 
  pull() 


nomes_organizacao <- nomes_eactdr |> 
  select(any_of(itens_organizacao)) |> 
  pivot_longer(
    cols = everything(), 
  ) |> 
  select(Item = value)

organizacao_do_trabalho <- nomes_organizacao |> 
  bind_cols(organizacao_do_trabalho) |> 
  select(-var)

Show the code

organizacao_do_trabalho <- organizacao_do_trabalho |> 
  mutate(
    Item = case_when(
      Item == "As atividades de trabalho realizadas pela internet demandam muito tempo de preparação." ~ "The work activities performed over the internet require a lot of preparation time.",
      Item == "Tenho que acompanhar uma grande quantidade de alunos durante as atividades remotas." ~ "I have to monitor a large number of students during remote activities.",
      Item == "O horário de descanso e/ou finais de semana são usados para trabalho." ~ "Rest hours and/or weekends are used for work.",
      Item == "Falta cooperação dos pais ou responsáveis no processo de ensino e aprendizagem dos alunos." ~ "There is a lack of cooperation from parents or guardians in the teaching and learning process.",
      Item == "As atividades remotas dificultam o acompanhamento do desempenho dos alunos." ~ "Remote activities make it difficult to monitor student performance.",
      Item == "Preciso responder e-mails, ligações ou mensagens de trabalho que chegam fora do horário de expediente." ~ "I need to respond to emails, calls, or work messages that arrive outside of working hours.",
      Item == "As atividades remotas dificultam a interação com os alunos durante as aulas." ~ "Remote activities hinder interaction with students during classes.",
      Item == "Há uma grande quantidade de formulários ou questionários online para preencher." ~ "There is a large number of online forms or questionnaires to fill out.",
      Item == "Falta tempo para realizar pausas de descanso no trabalho." ~ "There is no time to take breaks during work.",
      Item == "Há cobrança por parte dos alunos para responder rapidamente dúvidas e questões enviadas através de redes sociais (p. ex.: WhatsApp, Instagram, Messenger etc.)." ~ "Students demand quick responses to questions sent through social networks (e.g., WhatsApp, Instagram, Messenger, etc.).",
      Item == "Os resultados esperados estão além do que é possível realizar." ~ "The expected results are beyond what can be realistically achieved.",
      Item == "A formação dada aos professores para usar aplicativos ou programas é insuficiente." ~ "The training provided to teachers to use apps or programs is insufficient.",
      Item == "O grande número de alunos não permite acompanhá-los adequadamente." ~ "The large number of students does not allow for adequate monitoring.",
      Item == "Os prazos para a realização das tarefas demandadas pela coordenação ou direção escolar são curtos." ~ "The deadlines for completing tasks demanded by the coordination or school management are tight.",
      Item == "Há um número excessivo de reuniões online." ~ "There is an excessive number of online meetings.",
      .default = Item
    )
  )

Show the code

organizacao_tbl <- organizacao_do_trabalho |> 
  gt() |> 
  cols_align(align = "center", columns = c(!Item)) |> 
  tab_style(
    style = cell_text(align = "center"),
    locations = cells_column_labels(columns = "Item")
  ) |> 
  fmt_number(columns = c(M, SD),
             decimals = 2, sep_mark = ",", 
             dec_mark = "."
  )

organizacao_tbl

Table 16.4: Evaluation of Work Organization

Item	M	SD
The work activities performed over the internet require a lot of preparation time.	3.91	1.13
I have to monitor a large number of students during remote activities.	3.69	1.34
Rest hours and/or weekends are used for work.	3.62	1.10
There is a lack of cooperation from parents or guardians in the teaching and learning process.	3.55	1.01
Remote activities make it difficult to monitor student performance.	3.32	1.12
I need to respond to emails, calls, or work messages that arrive outside of working hours.	3.24	1.24
Remote activities hinder interaction with students during classes.	3.17	1.11
There is a large number of online forms or questionnaires to fill out.	3.09	1.23
There is no time to take breaks during work.	2.89	1.20
Students demand quick responses to questions sent through social networks (e.g., WhatsApp, Instagram, Messenger, etc.).	2.70	1.20
The expected results are beyond what can be realistically achieved.	2.67	1.01
The training provided to teachers to use apps or programs is insufficient.	2.52	1.23
The large number of students does not allow for adequate monitoring.	2.50	1.24
The deadlines for completing tasks demanded by the coordination or school management are tight.	2.43	1.05
There is an excessive number of online meetings.	2.41	1.17

Several studies conducted during the pandemic found that there was an increase in teachers’ working hours during this period (Messineo & Tosto, 2023; Oliveira & Pereira Junior, 2021; Petrakova et al., 2021). The global health crisis of SARS-CoV-2 accentuated and introduced new forms of intensification and precariousness of teachers’ work (Pessanha & Trindade, 2022).

Before the pandemic, Brazilian educational reforms implemented new models of management of teaching work that had repercussions, among other aspects, on the increase in workload, curricular changes, and pressure for results (Garcia & Anadon, 2009). These changes occurred in the quest for efficiency and productivity that entered schools, but without the necessary structural counterpoint (Pessanha & Trindade, 2022).

During the pandemic, remote work required a sudden adaptation to the distance learning modality. Teachers had to learn to use digital, pedagogical, and evaluative resources compatible with the new reality, which was a challenge. Moreover, the adaptation process also impacted the balance between work and personal life, as remote activities could demand more flexible schedules and result in the invasion of rest and leisure time (Petrakova et al., 2021).

Thus, it is essential that the departments ensure teachers’ planning time and guide them so that the flexibility allowed by remote teaching does not impact an increase in workload. In addition, the demands presented by the institutions should be accompanied by resources that enable the teachers to carry out their work.

Regarding working conditions, it was observed that the worst-rated elements were the availability and quality of technological resources, such as computers and cell phones, and the internet connection for carrying out activities. These findings are in Table 16.5.

Show the code

condicoes_de_trabalho <- df |> 
  summarise(
    across(c(ct9, ct16, ct18,
             ct20, ct26, ct33,
             ct36, ct38), list(
               "M" = mean, 
               "SD" = sd)
    )
  ) |> 
  pivot_longer(
    cols = everything(),
    names_to = c("var", ".value"),
    names_sep = "_"
  ) |> 
  arrange(desc(M))

itens_condicoes <- condicoes_de_trabalho |> 
  select(var) |> 
  pull() 


nomes_condicoes <- nomes_eactdr |> 
  select(any_of(itens_condicoes)) |> 
  pivot_longer(
    cols = everything(), 
  ) |> 
  select(Item = value)

condicoes_de_trabalho <- nomes_condicoes |> 
  bind_cols(condicoes_de_trabalho) |> 
  select(-var)

Show the code

condicoes_de_trabalho <- condicoes_de_trabalho |> 
  mutate(
    Item = case_when(
      Item == "O computador ou celular fica lento ou trava durante a realização do meu trabalho." ~ "The computer or cell phone is slow or crashes during my work.",
      Item == "O celular é o único equipamento disponível para realizar meu trabalho." ~ "The cell phone is the only device available for my work.",
      Item == "Ao realizar reuniões online, ocorrem falhas no áudio e/ou no vídeo que prejudicam a comunicação." ~ "During online meetings, there are audio and/or video failures that hinder communication.",
      Item == "Durante a realização do meu trabalho, a minha conexão com a internet é ruim (cai com frequência, é lenta, demora para carregar vídeos etc.)." ~ "During my work, my internet connection is poor (frequently drops, is slow, takes time to load videos, etc.).",
      Item == "O espaço utilizado para trabalhar é desconfortável (mesa, cadeira, teclado, mouse, monitor, iluminação, climatização, ventilação etc)." ~ "The space used for work is uncomfortable (desk, chair, keyboard, mouse, monitor, lighting, air conditioning, ventilation, etc.).",
      Item == "Os aplicativos/programas que uso para ministrar aulas/atividades pela internet são difíceis de utilizar." ~ "The apps/programs I use to teach classes/activities over the internet are difficult to use.",
      Item == "Os equipamentos (celular, computador, tablet etc.) utilizados para o trabalho são compartilhados com outras pessoas." ~ "The devices (cell phone, computer, tablet, etc.) used for work are shared with other people.",
      Item == "O espaço para a realização do trabalho é barulhento e/ou movimentado." ~ "The workspace is noisy and/or busy.",
      .default =  Item
    )
  )

Show the code

condicoes_de_trabalho_tbl <- condicoes_de_trabalho |> 
  gt() |> 
  cols_align(align = "center", columns = c(!Item)) |> 
  tab_style(
    style = cell_text(align = "center"),
    locations = cells_column_labels(columns = "Item")
  ) |> 
  fmt_number(columns = c(M, SD),
             decimals = 2,sep_mark = ",", 
             dec_mark = "."
  )

condicoes_de_trabalho_tbl

Table 16.5: Evaluation of working conditions

Item	M	SD
The computer or cell phone is slow or crashes during my work.	3.14	1.03
The cell phone is the only device available for my work.	2.97	1.50
During online meetings, there are audio and/or video failures that hinder communication.	2.95	0.96
Durante a realização do meu trabalho, a minha conexão com a internet é ruim (cai com frequência, é lenta, demora para carregar vídeos etc.)	2.85	0.98
The space used for work is uncomfortable (desk, chair, keyboard, mouse, monitor, lighting, air conditioning, ventilation, etc.).	2.63	1.26
The apps/programs I use to teach classes/activities over the internet are difficult to use.	2.46	0.98
The devices (cell phone, computer, tablet, etc.) used for work are shared with other people.	2.23	1.35
The workspace is noisy and/or busy.	2.20	1.07

Before the COVID-19 pandemic, teachers already faced adverse working conditions, and during the pandemic context

, the situation was intensified as teachers had to adapt their homes to transform them into a work environment.

Only a small part of the teachers received help from institutions regarding the maintenance and acquisition of essential technological equipment for conducting classes (Santos et al., 2021). In this sense, many of them had to deal with the lack of a computer or the need to share the equipment with other family members, which became one of the main causes of dissatisfaction with remote work, as this situation represented a disadvantage in planning and carrying out activities (R. R. V. Silva et al., 2021).

As presented above, emergency situations, such as teaching during the pandemic, require subsidies for teachers to acquire equipment and have access to quality internet. This measure is important for conducting classes, planning, and evaluating students.

Regarding socioprofessional relationships, the worst-rated items pointed to the lack of teacher participation in planning and decision-making about the school. There was also the perception that communication between teachers was inadequate, as shown in Table 16.6.

Show the code

relacoes_de_trabalho <- df |> 
  summarise(
    across(c(ct5, ct6, ct10,
             ct11, ct15, ct27,
             ct29, ct32, ct35,
             ct39), list(
               "M" = mean, 
               "SD" = sd)
    )
  ) |> 
  pivot_longer(
    cols = everything(),
    names_to = c("var", ".value"),
    names_sep = "_"
  ) |> 
  arrange(desc(M))

itens_relacoes <- relacoes_de_trabalho |> 
  select(var) |> 
  pull() 


nomes_relacoes <- nomes_eactdr |> 
  select(any_of(itens_relacoes)) |> 
  pivot_longer(
    cols = everything(), 
  ) |> 
  select(Item = value)

relacoes_de_trabalho <- nomes_relacoes |> 
  bind_cols(relacoes_de_trabalho) |> 
  select(-var)

Show the code

relacoes_de_trabalho <- relacoes_de_trabalho |> 
  mutate(
    Item = case_when(
      Item == "Os professores não participam das decisões sobre a escola." ~ "Teachers do not participate in decisions about the school.",
      Item == "Os gestores (coordenadores e diretor) realizam planejamentos e tomam decisões sobre a escola sem consultar os professores." ~ "Managers (coordinators and principal) make plans and decisions about the school without consulting the teachers.",
      Item == "A comunicação entre os professores é insatisfatória." ~ "Communication among teachers is unsatisfactory.",
      Item == "Existem disputas profissionais entre os professores." ~ "There are professional disputes among teachers.",
      Item == "Existem dificuldades na comunicação entre docentes e coordenadores." ~ "There are communication difficulties between teachers and coordinators.",
      Item == "Não há autonomia para decidir sobre as metodologias de ensino aplicadas nas aulas." ~ "There is no autonomy to decide on the teaching methodologies applied in classes.",
      Item == "Falta apoio dos gestores escolares (coordenadores e diretor) para o meu desenvolvimento profissional." ~ "There is a lack of support from school managers (coordinators and principal) for my professional development.",
      Item == "Falta autonomia para decidir sobre os conteúdos das aulas." ~ "There is a lack of autonomy to decide on class content.",
      Item == "Falta apoio dos coordenadores escolares para realizar meu trabalho." ~ "There is a lack of support from school coordinators to do my work.",
      Item == "Há dificuldades na comunicação entre docentes e diretores." ~ "There are communication difficulties between teachers and principals.",
      TRUE ~ Item
    )
  )

Show the code

relacoes_de_trabalho_tbl <- relacoes_de_trabalho |> 
  gt() |> 
  cols_align(align = "center", columns = c(!Item)) |> 
  tab_style(
    style = cell_text(align = "center"),
    locations = cells_column_labels(columns = "Item")
  ) |> 
  fmt_number(columns = c(M, SD),
             decimals = 2, sep_mark = ",", 
             dec_mark = "."
  )

relacoes_de_trabalho_tbl

Table 16.6: Evaluation of socioprofessional relationships

Item	M	SD
Teachers do not participate in decisions about the school.	2.04	1.20
Managers (coordinators and principal) make plans and decisions about the school without consulting the teachers.	1.89	1.22
Communication among teachers is unsatisfactory.	1.88	0.99
There are professional disputes among teachers.	1.66	0.98
There are communication difficulties between teachers and coordinators.	1.64	0.89
There is no autonomy to decide on the teaching methodologies applied in classes.	1.63	1.04
There is a lack of support from school managers (coordinators and principal) for my professional development.	1.62	0.92
There is a lack of autonomy to decide on class content.	1.60	0.93
There is a lack of support from school coordinators to do my work.	1.56	0.89
There are communication difficulties between teachers and principals.	1.54	0.85

In this sense, it is possible to assume that the social isolation imposed to reduce the spread of the virus also reflected in interpersonal relationships with colleagues, as teachers were prevented from attending the same space and maintaining the routine contact (Anastácio et al., 2022). Additionally, regarding the lower participation in decision-making and school activity planning, it is possible to assume that there was a centralization of decisions, considering the emergency nature of the situation. However, it is essential to bet on participatory management models, even in severe situations such as that faced during the period of social isolation.

Thus, it is necessary to decentralize decisions regarding the progress of classes, involving all professionals in both planning and managing activities. The possibility of sharing ideas and dividing tasks can be less harmful by avoiding an overload on managers, as well as contributing to better relationships among school community members.

16.3.4 Mental Health

Show the code

## Dynamic data for the text: mental health

ind_depre <- df |>
  count(depre_corte) |> 
  mutate(
    prop = n/sum(n),
    var = "Depression"
  ) |> 
  select(!n) |> 
  pivot_wider(
    names_from = depre_corte,
    values_from = prop)

ind_ansiedade <- df |> 
  count(ansie_corte) |> 
  mutate(
    prop = n/sum(n),
    var = "Anxiety"
  ) |> 
  select(!n) |> 
  pivot_wider(
    names_from = ansie_corte,
    values_from = prop
  )

ind_estrese <- df |> 
  count(estre_corte) |> 
  mutate(
    prop = n/sum(n),
    var = "Stress"
  ) |> 
  select(!n) |> 
  pivot_wider(
    names_from = estre_corte,
    values_from = prop
  )

saude_mental <- ind_depre |> 
  add_row(ind_ansiedade) |> 
  add_row(ind_estrese) |> 
  rowwise() |> 
  mutate(
    ind = sum(c_across(Mild:"Extremely severe")),
    ind = label_percent(0.1, decimal.mark = ".")(ind)
  ) |> 
  ungroup() |> 
  select(var, ind)

Regarding mental health, 36.3% of the teachers presented some level of depression between moderate and extremely severe. For anxiety and stress, these rates were 47.1% and 42.2%, respectively.

Show the code

p_depre <- df |> 
  ggplot(aes(depre_corte ,
             y = after_stat(count/sum(count)),
             fill = depre_corte )) +
  geom_bar() +
  labs(
    x = NULL, y = NULL,
    title = "Depression"
  ) +
  geom_text(aes(label = percent(after_stat(count/sum(count)))),
            stat = "count", nudge_y = 0.03,
            size = 2, nudge_x = 0.04)

p_depre <- p_depre + 
  scale_y_continuous(labels = percent) +
  scale_fill_colorblind() +
  theme(
    legend.position = "none",
    text = element_text(family = "Source Sans Pro"),
    axis.text.x = element_text(angle = 45, vjust = 1, 
                               hjust = 1, size = 8),
    axis.text.y = element_text(size = 8),
    title = element_text(size = 10),
    plot.title = element_text(hjust = 0.5)
  )

p_ansiedade <- df |> 
  ggplot(aes(ansie_corte,
             y = after_stat(count/sum(count)),
             fill = ansie_corte)) +
  geom_bar() +
  labs(
    x = NULL, y = NULL,
    title = "Anxiety"
  ) +
  geom_text(aes(label = percent(after_stat(count/sum(count)))),
            stat = "count", nudge_y = 0.03,
            size = 2, nudge_x = 0.04) 

p_ansiedade <- p_ansiedade + 
  scale_y_continuous(labels = percent) +
  scale_fill_colorblind() +
  theme(
    legend.position = "none",
    text = element_text(family = "Source Sans Pro"),
    axis.text.x = element_text(angle = 45, vjust = 1, 
                               hjust = 1, size = 8),
    axis.text.y = element_text(size = 8),
    title = element_text(size = 10),
    plot.title = element_text(hjust = 0.5)
  )

p_estresse <- df |> 
  ggplot(aes(estre_corte,
             y = after_stat(count/sum(count)),
             fill = estre_corte)) +
  geom_bar() +
  labs(
    x = NULL,y = NULL,
    title = "Stress"
  ) +
  geom_text(aes(label = percent(after_stat(count/sum(count))),),
            stat = "count",nudge_y = 0.03,
            size = 2, nudge_x = 0.04,
  ) 

p_estresse <- p_estresse + 
  scale_y_continuous(
    labels = percent
  ) +
  scale_fill_colorblind() +
  theme(
    legend.position = "none",
    text = element_text(family = "Source Sans Pro"),
    axis.text.x = element_text(angle = 45, vjust = 1, 
                               hjust = 1, size = 8),
    axis.text.y = element_text(size = 8),
    title = element_text(size = 10),
    plot.title = element_text(hjust = 0.5)
  )

p_depre + p_ansiedade + p_estresse

The image shows three bar charts representing the qualitative evaluation of the DASS-21 scale for depression, anxiety, and stress. In the first chart, titled 'Depression', the bars represent the following categories and percentages: 'Normal' with 64.4%, 'Mild' with 11.2%, 'Moderate' with 13.1%, 'Severe' with 6.3%, and 'Extremely Severe' with 4.9%. The second chart, 'Anxiety', shows the categories: 'Normal' with 53.94%, 'Mild' with 6.56%, 'Moderate' with 15.16%, 'Severe' with 5.85%, and 'Extremely Severe' with 18.50%. In the third chart, 'Stress', the categories are presented with the following percentages: 'Normal' with 58.71%, 'Mild' with 9.90%, 'Moderate' with 12.05%, 'Severe' with 12.89%, and 'Extremely Severe' with 6.44%. Each category is represented by a different colored bar on a scale of 0% to 60%. The charts have a white background with black text and lines and are organized side by side to facilitate visual comparison between the three mental health conditions evaluated by the DASS-21 scale. — Figure 16.2: Evaluation of teachers’ mental health

These results are similar to those found in the aforementioned studies, which revealed high rates of anxiety, stress, and depression (Machado et al., 2022; Ruas et al., 2022). Studies indicate that teaching work carries a share of physical and mental wear due to its specificities, such as the need for constant focus and concentration. Additionally, professionals had to deal with existing vulnerabilities alongside the double workload, pressure for results, and excessive demands from administrations (Machado et al., 2022).

The lack of interaction with students and colleagues, coupled with concerns about the pandemic and the precariousness of teaching work during the period, contributed to worse mental health evaluations (Machado et al., 2022; Ruas et al., 2022). In this sense, it is important to promote mental health care for teachers, reinforcing the importance of therapy, the role of school psychologists, and partnering with teachers to transform the work environment into a health-promoting space.

16.4 Conclusions

This research aimed to profile teachers’ mental health, work context, and their relationship with the COVID-19 pandemic. The findings presented here can serve as a basis for education departments to improve the education system by promoting better working conditions for teachers.

It was found that most teachers were aware and adopted preventive behaviors against COVID-19, such as wearing masks and social isolation. 24% of them were part of the risk group, and 24% of them were infected with the virus. Additionally, 69% of the sample lost friends or family members to COVID-19. Regarding the teaching modality, teachers carried out asynchronous activities more frequently, and students participated more in this configuration compared to synchronous activities.

Regarding the work context, the preparation of remote activities, the number of students to accompany, and the invasion of work into rest hours were the elements worst rated by teachers. In terms of working conditions, the availability and quality of technological resources were the worst-rated items. Concerning socioprofessional relationships during the period, it was found that teachers negatively evaluated the possibilities of participating in planning and decision-making at the school.

Regarding mental health, 35.6% of the teachers presented some level of depression between moderate and extremely severe. Anxiety and stress showed respective rates of 46.06% and 41.29%.

Given the findings in this research, it is important to recognize that the education system needs to make improvements that not only address the shortcomings exposed by the pandemic context but also use resources that contribute to the performance of teaching activities. Regarding teachers’ behavior during the pandemic, it is important to hold lectures and meetings so that teachers have accurate information about coronavirus prevention, reinforcing the attitudes already taken and making them disseminators of good prevention practices. It is also timely to reinforce the importance not only of testing in the face of symptoms indicative of COVID-19 but also of using the available vaccines.

In an emergency situation like this, it is essential that teachers receive support for acquiring equipment and contracting quality internet services, as well as promoting training courses that facilitate the use of these tools and guide them on using the best didactic strategies for this context. For students, the departments should provide the necessary equipment and conditions for students to properly follow classes, preferably synchronously.

It is also essential to ensure teachers’ planning hours and guide them on the impact of increased workload caused by the flexibility of the remote teaching model. It is also important to adapt the demands presented by institutions to the pandemic period and what teachers could achieve with the resources they had at the time. The pandemic period also highlighted the need to decentralize decisions regarding the progress of classes and include professionals in decision-making about the school. Based on the data presented, it is essential that departments promote mental health care actions for professionals through lectures and other activities that provide accurate information on the subject and can reduce the strain often caused by the work context itself. ```

References

Anastácio, Z. F. C., Antão, C., & Cramês, M. L. (2022). Professores/educadores em pandemia covid19: Percepções de saúde, rotinas pessoais e competências profissionais. Revista Contexto & Educação, 37(117), 24–37. https://doi.org/10.21527/2179-1309.2022.117.13000

Bernardo, K. A. da S., Maia, F. L., & Bridi, M. A. (2020). As configurações do trabalho remoto da categoria docente no contexto da pandemia covid-19. Novos Rumos Sociológicos, 8(14), 8–39. https://doi.org/10.15210/norus.v8i14.19908

Betancur, H. N. C., Argollo, K. P., Huanca, E. O. R., Lujan, J. R. C., García, E. L. J., & Mendizabal, B. K. S. (2022). Salud mental y calidad de sueño en los docentes de educación básica regular. Vive Revista de Salud, 5(15), 865–873. https://doi.org/10.33996/revistavive.v5i15.194

Borsoi, I. C. F. (2007). Da relação entre trabalho e saúde à relação entre trabalho e saúde mental. Psicologia & Sociedade, 19(spe), 103–111. https://doi.org/10.1590/S0102-71822007000400014

Cândido, T. S., Bittencourt, R. L. de, & Assunção, V. K. de. (2022). Os impactos da pandemia de covid-19 no trabalho docente universitário. Debates Em Educação, 14(35), 566–585. https://doi.org/10.28998/2175-6600.2022v14n35p566-585

Crepaldi, M. A., Schmidt, B., Noal, D. da S., Bolze, S. D. A., & Gabarra, L. M. (2020). Terminalidade, morte e luto na pandemia de COVID-19: Demandas psicológicas emergentes e implicações práticas. Estudos de Psicologia (Campinas), 37, e200090. https://doi.org/10.1590/1982-0275202037e200090

Cunha, E. S. (2023). Contexto de trabalho docente e assédio moral: Construção e validação de instrumentos [Dissertação de mestrado não publicada]. Universidade Federal do Ceará.

Ferreira, M. C., & Mendes, A. M. (2008). Contexto de trabalho. In M. M. M. Siqueira (Ed.), Medidas do comportamento organizacional: Ferramentas de diagnóstico e gestão (pp. 111–123). Artmed.

Garcia, M. M. A., & Anadon, S. B. (2009). Reforma educacional, intensificação e autointensificação do trabalho docente. Educação & Sociedade, 30(106), 63–85. https://doi.org/10.1590/S0101-73302009000100004

Gomez, C. M. (2011). Introdução - campo da saúde do trabalhador: Trajetória, configuração e transformações. In C. M. Gomez, J. M. H. Machado, & P. G. L. Pena (Eds.), Saúde do trabalhador na sociedade brasileira contemporânea (pp. 23–34). Editora FIOCRUZ. https://doi.org/10.7476/9788575413654.0002

Lacaz, F. A. de C. (2007). O campo saúde do trabalhador: Resgatando conhecimentos e práticas sobre as relações trabalho-saúde. Cadernos de Saúde Pública, 23(4), 757–766. https://doi.org/10.1590/S0102-311X2007000400003

Lindemann, I. L., Simonetti, A. B., Amaral, C. P. D., Riffel, R. T., Simon, T. T., Stobbe, J. C., & Acrani, G. O. (2021). Percepção do medo de ser contaminado pelo novo coronavírus. Jornal Brasileiro de Psiquiatria, 70, 3–11. https://doi.org/10.1590/0047-2085000000306

Machado, R. R., Carvalho, M. D. F. A. A., Lacerda Pedrosa, S. C. B. de, Moura, L. T. R. de, & Souza, D. M. O. R. de. (2022). A qualidade de vida, saúde física e mental de professores universitários em contexto da pandemia de covid-19. Revista de Educação Da Universidade Federal Do Vale Do São Francisco, 12(29), 149–179. https://periodicos.univasf.edu.br/index.php/revasf/article/view/1686

Mazza, C., Ricci, E., Biondi, S., Colasanti, M., Ferracuti, S., Napoli, C., & Roma, P. (2020). A nationwide survey of psychological distress among italian people during the COVID-19 pandemic: Immediate psychological responses and associated factors. International Journal of Environmental Research and Public Health, 17(9), 3165. https://doi.org/10.3390/ijerph17093165

Mendes, R., & Dias, E. C. (1991). Da medicina do trabalho à saúde do trabalhador. Revista de Saúde Pública, 25, 341–349. https://doi.org/10.1590/S0034-89101991000500003

Messineo, L., & Tosto, C. (2023). Perceived stress and affective experience in italian teachers during the COVID-19 pandemic: Correlation with coping and emotion regulation strategies. European Journal of Psychology of Education, 38, 1271–1293. https://doi.org/10.1007/s10212-022-00661-6

Oliveira, D. A., & Pereira Junior, E. A. (2021). Trabalho docente em tempos de pandemia: Mais um retrato da desigualdade educacional brasileira. Retratos Da Escola, 14(30), 719–734. https://doi.org/10.22420/rde.v14i30.1212

Pereira, H. P., Santos, F. V., & Manenti, M. A. (2020). Saúde mental de docentes em tempos de pandemia: Os impactos das atividades remotas. Boletim de Conjuntura (BOCA), 3(9), 26–32. https://doi.org/10.5281/zenodo.3986851

Pessanha, F. N. de L., & Trindade, R. A. C. (2022). La pandemia de covid-19 y el precario desarrollo del trabajo docente en brasil. Actualidades Investigativas En Educación, 22(2), 1–28. https://doi.org/10.15517/aie.v22i2.48916

Petrakova, A., Kanonire, T., Kulikova, A., & Orel, E. (2021). Characteristics of teacher stress during distance learning imposed by the COVID‑19 pandemic. Voprosy Obrazovaniya / Educational Studies Moscow, 1, 93–114. https://doi.org/10.17323/1814-9545-2021-1-93-114

Ruas, C. F. A., Oliveira, W. N., Silva, L. L. F., Mello, R. S. D. M. V., & Soares, W. D. (2022). Prevalência de depressão e ansiedade em professores da rede pública na era COVID-19. Cadernos UniFOA, 17(49), 165–171. https://doi.org/10.47385/cadunifoa.v17.n49.3691

Santos, G. M. R. F. dos, Silva, M. E. da, & Belmonte, B. do R. (2021). COVID-19: Emergency remote teaching and university professors’ mental health. Revista Brasileira de Saúde Materno Infantil, 21, 237–243. https://doi.org/10.1590/1806-9304202100S100013

Sato, L., & Bernardo, M. H. (2005). Saúde mental e trabalho: Os problemas que persistem. Ciência & Saúde Coletiva, 10(4), 869–878. https://doi.org/10.1590/S1413-81232005000400011

Silva, I. B. da, Pimenta, A. A., Nascimento, M. do, Lima, T. M. G. de, Aguiar, T. S., Ribeiro, R. M., & Albuquerque, A. T. de. (2022). Teaching work and impact the pandemic in mental health. Research, Society and Development, 11(10), e556111033200. https://doi.org/10.33448/rsd-v11i10.33200

Silva, M. P. da, Bernardo, M. H., & Souza, H. A. (2016). Relação entre saúde mental e trabalho: A concepção de sindicalistas e possíveis formas de enfrentamento. Revista Brasileira de Saúde Ocupacional, 41. https://doi.org/10.1590/2317-6369000003416

Silva, R. R. V., Barbosa, R. E. C., Silva, N. S. S. e., Pinho, L. de, Ferreira, T. B., Moreira, B. B., Brito, M. F. S. F., & Haikal, D. S. (2021). Pandemia da COVID-19: Insatisfação com o trabalho entre professores(as) do estado de minas gerais, brasil. Ciência & Saúde Coletiva, 26(12), 6117–6128. https://doi.org/10.1590/1413-812320212612.10622021

Stang-Rabrig, J., Brüggemann, T., Lorenz, R., & McElvany, N. (2022). Teachers’ occupational well-being during the COVID-19 pandemic: The role of resources and demands. Teaching and Teacher Education, 117, 103803. https://doi.org/10.1016/j.tate.2022.103803

Vazquez, D. A., & Pesce, L. (2022). A experiência de ensino remoto durante a pandemia de covid-19: Determinantes da avaliação discente nos cursos de humanas da unifesp. Avaliação: Revista Da Avaliação Da Educação Superior (Campinas), 27(1), 183–204. https://doi.org/10.1590/S1414-40772022000100010

Vignola, R. C. B., & Tucci, A. M. (2014). Adaptation and validation of the depression, anxiety and stress scale (DASS) to brazilian portuguese. Journal of Affective Disorders, 155, 104–109. https://doi.org/10.1016/j.jad.2013.10.031