# Factor Structure and Measurement Invariance of the Spanish Burnout Inventory Among Professionals Across 17 Countries and Regions

Pedro R Gil-Monte, Begoña Espejo, Irene Checa, Pedro Gil-Laorden, Kezia Angeline J, Mary Sandra Carlotto, Daniela Converso, Ángel Deroncele-Acosta, Hugo Figueiredo-Ferraz, Laura Galarza, et al.

<figure><img src="image_1.png" /></figure>

## ► To cite this version:

Pedro R Gil-Monte, Begoña Espejo, Irene Checa, Pedro Gil-Laorden, Kezia Angeline J, et al., Factor Structure and Measurement Invariance of the Spanish Burnout Inventory Among Professionals Across 17 Countries and Regions. Applied Research in Quality of Life, 2023, 18 (2), 10.1007/s11482-022-10108-1. doi: 10.1007/s11482-022-10108-1

HAL Id: hal-05417215

https://hal.science/hal-05417215v1

Submitted on 15 Dec 2025

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_2.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
<figure><img src="image_3.png" /></figure>

Check for
updates

# Factor Structure and Measurement Invariance of the Spanish Burnout Inventory Among Professionals Across 17 Countries and Regions

Pedro R. Gil-Monte¹,² ID · Begoña Espejo² ID · Irene Checa² ID ·

Pedro Gil-LaOrden¹,² ID · Kezia Angeline J³ ID · Mary Sandra Carlotto⁴ ID ·

Daniela Converso⁵ ID · Ángel Deroncele-Acosta⁶ ID ·

Hugo Figueiredo-Ferraz¹,⁷ ID · Laura Galarza⁸ ID · Viviola Gómez-Ortiz⁹ ID ·

Ester Grau-Alberola¹,¹⁰ ID · Javier Labarthe¹¹ ID · Marta Llorca-Pellicer¹ ID ·

Christy Mekala V¹² ID · Alejandra Misiolek-Marín¹³ ID · Eldis Román-Cao¹⁴ ID ·

Edwin Salas-Blas¹⁵ ID · Sandrine Schoenenberger¹⁶,¹⁷ ID · Sara Unda-Rojas¹⁸ ID ·

Sara Viotti⁵ ID

Received: 18 July 2022 / Accepted: 19 September 2022 / Published online: 3 November 2022
© The Author(s) 2022

## Abstract

Studies on the prevalence of burnout in professionals in service organizations who work in direct contact with the clients or users of the organization have concluded that burnout is a serious health disorder that has increased due to the COVID-19 pandemic. A significant advantage of the Spanish Burnout Inventory (SBI) over other instruments is that it provides a broader conceptualization of burnout by including feelings of guilt as a dimension of burnout to explain its development. However, the measurement invariance of the SBI across countries has not been investigated. The purpose of this study was to test the measurement invariance of the SBI among professionals across 17 countries and regions in Europe, Latin America, and Asia, and in different languages. All the countries showed a good fit to the four-factor model, except the Indian sample, which was excluded from the measurement invariance study. Using the alignment method, it was possible to verify the scalar measurement invariance of the four SBI factors across 15 countries and one Spanish region (16 samples). The comparison of estimated latent means indicates that France is the country with the lowest scores on the Enthusiasm factor and the highest scores on the negative factors (Exhaustion, Indolence, and Guilt). In contrast, the Andean countries, Colombia, Peru, and Ecuador, show the highest latent means on the Enthusiasm factor and the lowest means on the negative factors.

From the fifth author on, authors are listed alphabetically because they contributed equally to this project.
(GROUP)

Extended author information available on the last page of the article
These results support the validity of the SBI in the countries and regions in Europe and Latin America included in this study.

**Keywords** Spanish Burnout Inventory · confirmatory factor analysis · construct validity · measurement invariance · alignment method · cross-cultural validity

The ICD-11 (World Health Organization, 2019) defines burnout syndrome as a psychological response to chronic work-related stress that has not been successfully managed. It is a nonpsychiatric health disorder characterized by three dimensions: (1) feelings of exhaustion; (2) increased mental distance from one's job; and (3) a sense of ineffectiveness. Burnout refers specifically to phenomena in the occupational context and should not be used to describe experiences in other areas of life. It has been classified (QD85) as a problem associated with employment or unemployment.

Burnout can be considered a significant worldwide occupational health problem that appears mainly in professionals in service organizations who work in direct contact with the clients of the organization (Gil-Monte, 2005), and it can be expressed as psychological symptoms related to depressive mood (Figueiredo-Ferraz et al., 2021; Gil-Monte, 2012; Parker & Tavella, 2021). Interest in the study of burnout and the health problems associated with its development has grown in the past decade due to social and occupational changes that have led to an increase in work stress along with social demands to increase the quality of working life and the prevention of psychosocial risks at work (Eurofound, 2016; 2018). The 2021 Work and Well-being Survey by the APA found that 40% of adults who perform job activities related to customer interaction, entertainment, sales, or other services-oriented work felt high levels of emotional exhaustion (American Psychological Association, 2022). In the 2022 trends report, the APA includes burnout as one of the emerging trends, reporting that burnout and stress are everywhere. Both are at all-time highs across professions, and among health care workers they are exacerbated by the unrelenting stressors associated with the COVID-19 pandemic (Abramson, 2022).

According to the literature review carried out by Eurofound (2018), there are variations in burnout levels, and severe forms of burnout are infrequent, with less than 10% reporting symptoms of depression, incapacity to work, or psychosomatic disorders. More moderate forms of burnout were reported by between 15% and 25% of respondents in the different studies. Systematic literature reviews and meta-analyses carried out in healthcare professionals have concluded that burnout prevalence: (a) among physicians practicing in Europe has been estimated at 7.7% (Hiver et al., 2022); (b) among nurses worldwide was 10%, with the highest prevalence in Intensive and Critical Care nurses (14.4%) (Woo et al., 2020); (c) ranges from 3 to 66% in healthcare professionals providing palliative care, with most studies reporting a burnout prevalence of 18% or more (Dijxhoorn et al., 2021); and (d) was 47.3% in postgraduate medical trainees (Naji et al., 2021). In Menscape's 2021 National Physician Burnout & Suicide Survey, 42% of the physicians reported burnout, out of a sample of more than 12,000 American physicians, with the highest prevalence found in Critical Care physicians (51%) (Medscape, 2021).

<figure><img src="image_4.png" /></figure>
Studies of burnout prevalence in educators have concluded that burnout in teachers is a serious problem that has increased during the COVID-19 pandemic (Fernández-Suárez et al., 2021; Weißenfels et al., 2022) due to concern about unsafe school conditions and the pressure of virtual work (Pressley et al., 2021). Working conditions in education (Gil-Monte et al., 2011; Kim et al., 2021) have turned teaching into a profession with a high risk of developing this syndrome. Teaching requires high levels of commitment to the job, and most schools do not consider the work-life balance of their teachers and have unrealistic expectations of them. Instead, school leaders relentlessly focus on improvement and external inspections, accountability, and a blame culture, which discourages teaching staff from revealing how they feel and increases stress at work. Results from systematic reviews showed that the rate of overall burnout was close to 24% in physical education teachers (Alsalhe et al., 2021) and 37% in university teachers (Fernández-Suárez et al., 2021). In addition, Carlotto & Câmara (2019) estimated the prevalence of critical burnout in Brazilian teachers from public elementary schools at 25.8%, and Li et al., (2020) estimated it at 53.20% in Chinese preschool teachers. However, the prevalence was lower in Finnish primary and secondary school teachers, ranging from 3.7 to 5.6% (Pyhältö et al., 2021).

The symptoms of burnout syndrome according the definition of the World Health Organization (2019) can be assessed using the Maslach Burnout Inventory (MBI; Maslach & Jackson 1981; Maslach et al., 1996), because those symptoms are the three dimensions or subscales of this questionnaire -i.e., Emotional exhaustion or Exhaustion, Depersonalization or Cynicism and Personal accomplishment or Professional Efficacy (Maslach & Leiter, 2017; Maslach et al., 1996), but a review of the literature indicates that researchers have been troubled by some of the limitations of the MBI (Kristensen et al., 2005), for example: (a) researchers have proposed different and not always matching solutions for the factor structure (e.g., two-factor model instead of the original three-factor model) (Loera et al., 2014), (b) the MBI dimensions were not theoretically deduced before construction of the questionnaire; instead, they were labeled after the factor analysis (Schaufeli & Van Dierendonck, 1993), (c) it is necessary to more broadly capture the nature of burnout (Gil-Monte et al., 2013) because the definition of burnout according the MBI is based on a limited concept of burnout (Halbesleben & Demerouti, 2005), (d) the MBI was developed as a research tool, not as a diagnostic one (Doulougeri et al., 2016).

Most of the studies on the prevalence of burnout have been carried out with psychometric instruments that do not differentiate between burnout profiles. However, a literature review allows us to conclude that burnout may progress in different ways, and there is empirical evidence for differentiating between types or profiles of burnout (Gillet et al., 2020; Guidetti et al., 2018b; Leiter & Maslach, 2016; Pyhältö et al., 2021; Salmela-Aro & Upadyaya, 2020) related to predictors (Llorca-Pellicer et al., 2021) and consequences (Misiolek-Marín et al., 2020). These profiles could be explained by the fact that some professionals develop higher clinical patterns of burnout, personal distress, and diminished performance as an end state of burnout, whereas others remain in the organization for years without developing relevant personal problems due to work-related stress, but they have attitudes and behaviors of indolence and distance from their job (Figueiredo-Ferraz et al., 2021; Gil-Monte, 2012; Rabasa et al., 2016). Therefore, studies in the field of burnout should
evaluate burnout by taking into consideration different types of burnout and their consequences.

## Overview of the Spanish Burnout Inventory

The Spanish Burnout Inventory (SBI) (Gil-Monte, 2019) is one of the most extensively applied questionnaires to evaluate burnout in Latin America (Díaz & Gómez, 2016) and it has been applied in different countries of Europe to assess job burnout: Czech Republic (Alföldy & Gil-Monte, 2010), Germany (Bosle & Gil-Monte, 2010), Italy (Guidetti et al., 2018b), Poland (Misiolek et al., 2017), Portugal (Figueiredo-Ferraz et al., 2013) and United Kingdom (Cramer et al., 2020). It was developed to address the problems associated with the MBI and other questionnaires assessing burnout.

The theoretical model of burnout developed by Gil-Monte (2005) is based on the concept that burnout is a response to chronic job stress that stems primarily from problematic interpersonal work relationships characterized by four symptoms: (1) cognitive deterioration (i.e., low enthusiasm toward the job), (2) emotional deterioration (i.e., psychological exhaustion), (3) attitudes and behaviors of indifference, indolence, withdrawal, and in some cases, (4) feelings of guilt. The SBI comprises 20 items divided into four subscales: (1) Enthusiasm toward the job: the individual's desire to achieve goals at work because it is a source of personal pleasure. This scale is similar to that of the Personal accomplishment of the MBI. (2) Psychological exhaustion: the appearance of emotional and physical exhaustion due to the fact that he or she must deal daily with people at work who present problems. This scale is similar to that of the Emotional exhaustion of the MBI. (3) Indolence: the appearance of negative attitudes of indifference and cynicism toward the organization's clients. This scale is similar to that of the Depersonalization of the MBI. (4) Guilt: the appearance of feelings of guilt about negative attitudes developed on the job, especially toward the people with whom he or she establishes work relationships. It is a new dimension added to the concept of burnout (Gil-Monte et al., 2013; Maslach & Leiter, 2017) to explain different types of burnout, considering the role of guilt feelings in the relationship between burnout and its consequences.

The model of the SBI identifies two profiles in the development of burnout. In both, attitudes and behaviors of indolence are understood as a coping strategy that arises to deal with emotional and cognitive deterioration. For some professionals, this coping strategy allows them to manage the levels of strain, but other professionals find this way of proceeding to be inadequate and develop feelings of guilt. Profile 1 is characterized by low enthusiasm toward the job, high levels of psychological exhaustion, and indolence. Individuals who fit Profile 1 suffer moderately from work-related stress, and they are able to do their work without experiencing very high feelings of guilt (i.e., critical levels). However, Profile 2 is characterized by more severe manifestations of burnout and the use of indolence as a dysfunctional coping strategy. Individuals who fall into Profile 2 feel they cannot do their jobs properly, which makes them develop greater feelings of guilt and then other health disorders,
such as symptoms of depression, and absenteeism (Figueiredo-Ferraz et al., 2021; Gil-Monte, 2012).

The SBI fits the common language (in terms of measurement) for the study of burnout delimited by the MBI, which is the most widely used instrument to measure burnout in different cultures and languages (Díaz & Gómez, 2016; Lheureux et al., 2017; McCormack et al., 2018; Rotenstein et al., 2018). However, it adds the dimension of Guilt to improve the diagnosis of job burnout by identifying the profiles.

Taking into consideration the SBI factor structure, previous exploratory factorial analysis studies have shown a four-factor structure similar to that of the model, representing Enthusiasm toward the job, Psychological Exhaustion, Indolence, and Guilt (Bosle & Gil-Monte, 2010; Esteves et al., 2020; Olivares & Gil-Monte, 2007). Results have been replicated through confirmatory factor analysis, obtaining empirical support for the four-factor structure model across countries and occupational groups in several Latin American countries: Brazil (Carlotto et al., 2015), Chile (Olivares-Faúndez et al., 2018), Colombia (García et al., 2022), and Mexico (Gil-Monte et al., 2013); and in some European countries: Germany (Bosle & Gil-Monte, 2010), Italy (Viotti et al., 2015), Poland (Misiolek et al., 2017), Portugal (Figueiredo-Ferraz et al., 2013), and Spain (Gil-Monte & Manzano-García, 2015). In addition, systematic reviews and meta-analysis studies have concluded that the SBI possesses adequate psychometric properties for the study of burnout (Serna et al., 2018). However, to date, the measurement invariance of the SBI across countries has not been tested. This study aims to close this research gap because studies of invariance across countries provide greater support for the construct validity in different cultural groups, and they contribute to enhancing the validity and applicability of the SBI internationally.

## The Present Study

The purpose of this study was to test whether the SBI shows measurement invariance among professionals across 17 countries and regions. It aims to fill the gap in the literature by examining the measurement invariance of the SBI across different cultural contexts and languages in Latin America, Europe, and Asia. When a measure is invariant, it can be concluded that the same constructs are evaluated and construed in the same way in the different samples and, therefore, can be compared (Putnick & Bornstein, 2016).

## Method

### Participants and Procedures

The sample included 18,611 participants (M=42.3 years, SD=10.4, Age Range=18–82 years; 73.4% female) from 17 countries and regions. Two samples from Spain are presented because one of them (Valencia) is a specific region of Spain were, in addition to Spanish, another language (Catalan) is spoken. Data from the questionnaire adapted to Catalan were obtained in this region (Llorca-Rubio et al.,
2021). Regarding the working sector, 80.6% of the sample (n=15,000) came from education, 12.5% (n=2,334) from health, 1.5% (n=277) from the disability sector, and 5.4% (n=1,000) from other sectors. Regarding the type of contract, 75% of the sample (n=11,273) were tenured staff, 23.7% (n=3,566) were temporary staff, and 1.2% (n=185) had another type of contract. The mean years of seniority in the profession of all the participants was 13.2 years (SD=10.4), with a range in seniority of between 0.02 and 54 years of seniority. Sociodemographic data of the different countries and regions is shown in Table 1

The data collection procedure was as follows. First, the first author contacted a number of collaborators to invite them to participate in the study. In addition to Spain, fifteen countries were recruited through this process. Second, the first author chose some data that were used in previous published studies to contribute to the international validation of the SBI (Brazil, Chile, Colombia, Italy, Mexico, Poland, Portugal, and Spain). Finally, to further extend the number of countries included in the study, the collaborators send some data that had been collected for unpublished previous studies carried out in collaboration with the first author (Argentina, Costa Rica, India, Puerto Rico, and Uruguay) or specifically for this study (Ecuador, France, and Peru). The first author sent the SBI translation to the collaborators living in countries where the first language was not Spanish, and the questionnaire had not been validated (France and India). In these countries, prior to starting the data collection, the items on the SBI were translated and back translated. The two versions obtained were compared, discussed, and reviewed until complete agreement was reached among the collaborators and the first author.

Some data were collected by paper and pencil at the workplace in different cities in: Argentina, Brazil (Gil-Monte et al., 2010), Colombia (by aggregating different samples of healthcare professionals, e.g., Tejada & Gómez 2012), Costa Rica, Italy (e.g., Guidetti et al., 2018a; Guidetti et al., 2018b), Mexico, Poland (Misiołek et al., 2017), Portugal (Figueiredo-Ferraz et al., 2013), and Spain (both Spanish and Catalan languages) (e.g., Llorca-Pellicer et al., 2021). Other data were collected online around the country through Google Forms: Brazil (Diehl & Carlotto, 2020), Ecuador, France (the study was approved by the Research Ethics Committee of the Universidad Internacional de La Rioja (UNIR), Spain), and Puerto Rico. Data from Chile were collected by paper and pencil during non-working time at the workplace in two organizations in Valparaíso and Santiago (Gil-Monte & Olivares, 2011) and online by a specific application developed for previous studies. Data from India were collected by paper and pencil at the workplace (the researcher was present during the data collection) and online (Google Forms) in and around Tamil Nadu (Southern India). Data from Peru were collected online through Google Forms and Facebook (around 33% of the data) from teachers working in Lima and Cuzco. Some participants were contacted by Whatsapp, and then the researcher used traditional snowball sampling to increase the sample size. Data from Uruguay were collected online by a specific application similar to Survey Monkey developed for the “Instituto Nacional de Evaluación Educativa” (INEEd) (Ministry of Education and Culture, Government of Uruguay) to analyze the quality of working life in teachers across the country (https://www.ineed.edu.uy/nuestro-trabajo/bases-de-datos/488-encuesta-de-salud-ocupacional-docente-2019.html).

<figure><img src="image_5.png" /></figure>
<table><caption>**Table 1** Sociodemographic Characteristics of the 17 Countries and Regions</caption><thead><tr><th>Country</th><th>n</th><th>Language</th><th>Age</th><th>M</th><th>SD</th><th>Range</th><th>% Female</th><th>Type of contract</th><th>% Tenured</th><th>% Temporary</th><th>% Other</th><th>M</th><th>SD</th><th>Range</th><th>Seniority in the profession (years)</th></tr></thead><tbody><tr><td>Argentina</td><td>302</td><td>Spanish</td><td>40.6</td><td>9.1</td><td>21-66</td><td>87.1</td><td>Education</td><td>64.5</td><td>35.5</td><td>-</td><td>13.9</td><td>9.2</td><td>0.08-38</td><td></td></tr><tr><td>Brazil</td><td>1,943</td><td>Portuguese</td><td>42.4</td><td>10.4</td><td>19-73</td><td>75.5</td><td>Education</td><td>68.9</td><td>31.1</td><td>-</td><td>13.8</td><td>9.3</td><td>0.50-48</td><td></td></tr><tr><td>Chile</td><td>741</td><td>Spanish</td><td>35.5</td><td>8.3</td><td>19-69</td><td>71.9</td><td>Disability (37.4%) / Other (62.6%)</td><td>95.9</td><td>4.1</td><td>-</td><td>9.8</td><td>8.1</td><td>0.02-44</td><td></td></tr><tr><td>Colombia</td><td>675</td><td>Spanish</td><td>38.7</td><td>9.4</td><td>18-82</td><td>63.3</td><td>Health</td><td>69.0</td><td>31.0</td><td>-</td><td>13.1</td><td>8.3</td><td>1.00-36</td><td></td></tr><tr><td>Costa Rica</td><td>477</td><td>Spanish</td><td>-</td><td>-</td><td>-</td><td>38.8</td><td>Other</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td></td></tr><tr><td>France</td><td>316</td><td>French</td><td>45.7</td><td>9.7</td><td>25-64</td><td>83.2</td><td>Education</td><td>90.2</td><td>9.8</td><td>-</td><td>18.6</td><td>9.7</td><td>0.06-41</td><td></td></tr><tr><td>Italy</td><td>891</td><td>Italian</td><td>45.9</td><td>9.4</td><td>22-64</td><td>89.1</td><td>Education</td><td>79.5</td><td>20.5</td><td>-</td><td>20.6</td><td>11.3</td><td>0.02-43</td><td></td></tr><tr><td>Spain</td><td>2,750</td><td>Spanish</td><td>45.6</td><td>9.2</td><td>23-67</td><td>74.2</td><td>Education</td><td>99.5</td><td>0.5</td><td>-</td><td>18.1</td><td>9.8</td><td>0.02-47</td><td></td></tr><tr><td>Poland</td><td>534</td><td>Polish</td><td>41.7</td><td>9.2</td><td>24-65</td><td>69.9</td><td>Health</td><td>52.9</td><td>25.3</td><td>21.8</td><td>16.0</td><td>9.1</td><td>1.00-40</td><td></td></tr><tr><td>Puerto Rico</td><td>736</td><td>Spanish</td><td>37.2</td><td>10.4</td><td>22-70</td><td>82.9</td><td>Education (27.6% / Health (72.4%))</td><td>-</td><td>-</td><td>-</td><td>11.4</td><td>9.7</td><td>1.00-44</td><td></td></tr><tr><td>Valencia (Spain)</td><td>1,228</td><td>Catalan</td><td>46.2</td><td>9.1</td><td>20-67</td><td>66.2</td><td>Education</td><td>99.5</td><td>0.5</td><td>-</td><td>17.5</td><td>10.2</td><td>0.02-45</td><td></td></tr><tr><td>Mexico</td><td>1,358</td><td>Spanish</td><td>42.2</td><td>9.9</td><td>21-77</td><td>72.2</td><td>Education</td><td>71.3</td><td>28.7</td><td>-</td><td>16.0</td><td>9.8</td><td>0.02-54</td><td></td></tr><tr><td>Peru</td><td>327</td><td>Spanish</td><td>47.0</td><td>10.6</td><td>22-75</td><td>72.2</td><td>Education</td><td>75.8</td><td>24.2</td><td>-</td><td>20.1</td><td>10.3</td><td>1.00-42</td><td></td></tr><tr><td>Portugal</td><td>684</td><td>Portuguese</td><td>36.7</td><td>9.7</td><td>22-68</td><td>74.5</td><td>Education (30.9%) / Health (69.1%)</td><td>81.1</td><td>16.1</td><td>2.8</td><td>13.5</td><td>9.5</td><td>0.02-37</td><td></td></tr><tr><td>Uruguay</td><td>4,734</td><td>Spanish</td><td>41.1</td><td>10.4</td><td>20-75</td><td>75.1</td><td>Education</td><td>54.0</td><td>44.2</td><td>1.8</td><td>6.4</td><td>7.4</td><td>0.02-42</td><td></td></tr><tr><td>India</td><td>210</td><td>Tamil</td><td>32.2</td><td>10.0</td><td>20-64</td><td>79.8</td><td>Education (15.3%) / Health (55.6%) / Other (29.1%)</td><td>-</td><td>-</td><td>-</td><td>8.7</td><td>9.2</td><td>0.25-42</td><td></td></tr><tr><td>Ecuador</td><td>705</td><td>Spanish</td><td>45.8</td><td>10.1</td><td>22-75</td><td>63.3</td><td>Education</td><td>79.0</td><td>21.0</td><td>-</td><td>16.0</td><td>10.0</td><td>1.00-51</td><td></td></tr></tbody></table>

Note. A hyphen is offered when the data was not retrieved.

<figure><img src="image_6.png" /></figure>
Participants received no reward for responding in any country or region. In all countries, the fundamental principles of the Declaration of Helsinki were respected (World Medical Association, 2013), with particular emphasis on the anonymity and confidentiality of the data collected and non-discrimination of participants. All the data collection procedures had a cross-sectional design, and participants were selected in a non-random manner.

## Instrument

The Spanish Burnout Inventory (Gil-Monte, 2019) comprises 20 items divided into four subscales: (1) Enthusiasm toward the job (5 items, e.g., I see my job as a source of personal accomplishment) (for the total sample in this study, $α=0.87$, 95% CI [0.870, 0.876]); (2) Psychological exhaustion (4 items, e.g., I feel emotionally exhausted) (for the total sample, $α=0.86$, 95% CI [0.856, 0.862]); (3) Indolence (6 items, e.g., I think many students are unbearable) (for the total sample, $α=0.77$, 95% CI [0.763, 0.773]); and (4) Guilt (5 items, e.g., I regret some of my behaviors at work) (for the total sample, $α=0.81$, 95% CI [0.805, 0.814]). Items are answered on a 5-point frequency scale, ranging from 0 (Never) to 4 (Very frequently: Every day). Low scores on Enthusiasm toward the job, together with high scores on Psychological Exhaustion and Indolence, as well as on Guilt, indicate high levels of burnout.

## Data Analysis

The psychometric and cross-cultural invariance study of the SBI was carried out following the guidelines established for this type of study (Asparouhov & Muthén, 2014; Byrne & van de Vijver, 2017). First, a confirmatory factor analysis (CFA) of the SBI was performed in each of the 17 samples studied. Specifically, the four-factor model, the model with the most support in the literature, was tested. Subsequently, in all the groups with an acceptable model fit, a multigroup confirmatory factor analysis (MGCFA) was carried out to test for configural, metric, and scalar invariance by countries or regions. The Robust Maximum Likelihood (MLR) estimator was used due to: (a) relatively small sample sizes in some regions, (b) the five-category response scale (Rhemtulla et al., 2012), and (c) potential deviations from normality (Yuan & Bentler, 2000). To examine model fit, the most common fit indices were used: Chi-Squared, Comparative fit index (CFI), Root-Mean-Square Error of Approximation (RMSEA), and Standardized-Root-Mean Square Residual (SRMR). The following cut-off points indicated an acceptable fit: CFI>0.90, RMSEA<0.06, SRMR<0.08 (Brown, 2015; Hu & Bentler, 1999). Measurement invariance was examined by comparing the fit indices of the configural model and those of the scalar model. When sample size is adequate (total N>300), a change of ≥0.010 in CFI, supplemented by a change of ≥0.015 in RMSEA or a change of ≥0.030 in SRMR, would indicate non-invariance (Chen, 2007).

Given that in most cross-cultural studies with a significant number of groups, scalar invariance is a big challenge (Cieciuch et al., 2019), the MGCFA with the alignment procedure was used to identify the most non-invariant parameters. This method recently received attention from cross-cultural research (Sawicki et al., 2022).
because the alignment method makes it possible to optimize the reliable estimation of the means, despite the presence of a certain degree of measurement non-invariance (Byrne & van de Vijver, 2017). First, the free alignment approach procedure is tested. If scalar invariance is obtained by using this method, the estimation process ends. But if there is no scalar invariance, the fixed alignment approach is used. According to this method, proposed by Asparouhov & Muthén (2014), the country or region that shows the mean value of the factor closest to 0 will be specified as the reference group to test for measurement invariance. In the alignment optimization, three criteria are used to establish the existence of invariance. First, at most, 25% of the parameters must be non-invariant in order to consider the estimates of the means trustworthy (Cieciuch et al., 2019). Second, the contribution of each item (factor load or intercept) to the fit function is analyzed, and the lowest absolute value is considered the most invariant. Finally, the value of $R^2$ is considered, which indicates the variation in these parameters between groups in the configural model that can be explained by the variation in the means of the factors and the variances between groups. According to Asparouhov & Muthén (2014), a value of $R^2$ close to 1 indicates a high degree of invariance, whereas a value close to 0 suggests a low degree of invariance. Sociodemographic data have been obtained with SPSS. The other analyses have been carried out with Mplus 8.8(Muthén & Muthén, 2017).

## Results

The CFA of the four-factor model carried out with the complete sample showed a good fit to the data ($\chi^2$ (164)=7,783.54, *p*<.001, CFI=0.926, RMSEA=0.050, RMSEA 90% CI [0.049, -0.051], SRMR=0.044). However, the CFA performed with the sample from each country or region (see Table 2) offered good results for all of them, except Argentina (label 1) and India (label 16), which showed fit indices below the cut-off point recommended as acceptable.

In the case of the Argentine sample, it can be seen that the RMSEA and SRMR values are adequate, whereas the CFI value does not exceed the cut-off point. However, the RMSEA and CFI values can be inconsistent at times. Although these indices are commonly used to assess model fit, they do not produce comparable qualitative assessments across data sets because they are computed differently. RMSEA is a non-standardized fit index, and so arbitrary cut-off points are used for its interpretation. However, the CFI measures relative improvement in fit (Shi et al., 2019). Some authors point out that, although these two indices sometimes offer contradictory evaluations of the model, it does not mean that the model is poorly specified or that there is a problem with the data because they evaluate the fit of the model from different perspectives (Lai & Green, 2016). Likewise, in simulation studies, it has been observed that the RMSEA almost always rejects the model if the samples are large and the items' response scale has five response categories. In contrast, the SRMR provides more reliable estimates of model fit. Because SRMR is a standardized fit index (compared to RMSEA, which is a non-standardized index), it shows greater power to reject models that show a poor fit to the data when the response scale is ordinal (as in this case), regardless of the number of parameters to be analyzed and the sample
<table><caption>**Table 2** Confirmatory Factor Analysis Results for the Four-Factor Model in Each of the 17 Countries and Regions in the Sample</caption><thead><tr><th>Label</th><th>Country</th><th>χ²(164)</th><th>CFI</th><th>RMSEA</th><th>90% RMSEA CI</th><th>SRMR</th><th>Standardized factor loadings range</th></tr></thead><tbody><tr><td>1</td><td>Argentina</td><td>348.48*</td><td>0.887</td><td>0.061</td><td>[0.052, -0.070]</td><td>0.060</td><td>0.48-0.77</td></tr><tr><td>2</td><td>Brazil</td><td>967.57*</td><td>0.941</td><td>0.050</td><td>[0.047, -0.053]</td><td>0.046</td><td>0.55-0.86</td></tr><tr><td>3</td><td>Chile</td><td>614.20*</td><td>0.918</td><td>0.061</td><td>[0.056, -0.066]</td><td>0.064</td><td>0.46-0.88</td></tr><tr><td>4</td><td>Colombia</td><td>488.97*</td><td>0.913</td><td>0.054</td><td>[0.049, -0.060]</td><td>0.043</td><td>0.47-0.81</td></tr><tr><td>5</td><td>Costa Rica</td><td>370.93*</td><td>0.919</td><td>0.051</td><td>[0.044, -0.058]</td><td>0.052</td><td>0.33-0.83</td></tr><tr><td>6</td><td>France</td><td>329.05*</td><td>0.939</td><td>0.056</td><td>[0.048, -0.065]</td><td>0.057</td><td>0.46-0.88</td></tr><tr><td>7</td><td>Italy</td><td>586.81*</td><td>0.926</td><td>0.053</td><td>[0.048, -0.057]</td><td>0.048</td><td>0.44-0.83</td></tr><tr><td>8</td><td>Spain</td><td>1,136.04*</td><td>0.934</td><td>0.046</td><td>[0.044, -0.049]</td><td>0.043</td><td>0.43-0.80</td></tr><tr><td>9</td><td>Poland</td><td>407.89*</td><td>0.928</td><td>0.053</td><td>[0.046, -0.059]</td><td>0.052</td><td>0.50-0.82</td></tr><tr><td>10</td><td>Puerto Rico</td><td>577.34*</td><td>0.927</td><td>0.059</td><td>[0.053, -0.064]</td><td>0.064</td><td>0.53-0.88</td></tr><tr><td>11</td><td>Valencia (Spain)</td><td>635.26*</td><td>0.923</td><td>0.048</td><td>[0.044, -0.052]</td><td>0.044</td><td>0.43-0.82</td></tr><tr><td>12</td><td>Mexico</td><td>805.36*</td><td>0.921</td><td>0.054</td><td>[0.050, -0.057]</td><td>0.045</td><td>0.46-0.85</td></tr><tr><td>13</td><td>Peru</td><td>326.22*</td><td>0.918</td><td>0.055</td><td>[0.046, -0.064]</td><td>0.055</td><td>0.47-0.88</td></tr><tr><td>14</td><td>Portugal</td><td>460.34*</td><td>0.935</td><td>0.046</td><td>[0.041, -0.052]</td><td>0.043</td><td>0.54-0.82</td></tr><tr><td>15</td><td>Uruguay</td><td>1,989.69*</td><td>0.940</td><td>0.048</td><td>[0.047, -0.050]</td><td>0.047</td><td>0.50-0.85</td></tr><tr><td>16</td><td>India</td><td>415.85*</td><td>0.815</td><td>0.086</td><td>[0.075, -0.096]</td><td>0.084</td><td>0.44-0.85</td></tr><tr><td>17</td><td>Ecuador</td><td>466.99*</td><td>0.926</td><td>0.051</td><td>[0.046, -0.057]</td><td>0.055</td><td>0.45-0.84</td></tr></tbody></table>

Note. CFI=comparative fit index; RMSEA=root-mean-square error of approximation; CI=confidence interval; SRMR=standardized root-mean-squared residual

*p*<.001

<table><caption>**Table 3** Results of the Measurement Invariance Models</caption><thead><tr><th></th><th>χ²</th><th>df</th><th>Δχ²</th><th>Δdf</th><th>CFI</th><th>RMSEA</th><th>SRMR</th><th>ΔCFI</th><th>ΔRMSEA</th><th>ΔSRMR</th></tr></thead><tbody><tr><td>Config-ural</td><td>10,416.29*</td><td>2624</td><td></td><td></td><td>0.932</td><td>0.051</td><td>0.048</td><td></td><td></td><td></td></tr><tr><td>Metric</td><td>11,517.22*</td><td>2864</td><td>1,093.32*</td><td>240</td><td>0.924</td><td>0.051</td><td>0.055</td><td>-0.008</td><td>0.000</td><td>0.007</td></tr><tr><td>Scalar</td><td>20,817.26*</td><td>3104</td><td>10,187.18*</td><td>240</td><td>0.845</td><td>0.070</td><td>0.076</td><td>-0.079</td><td>0.019</td><td>0.021</td></tr></tbody></table>

Note. Δχ² = Chi-Square change; Δdf=degrees of freedom change; CFI=comparative fit index; RMSEA=root-mean-square error of approximation; SRMR=standardized root-mean-squared residual; ΔCFI=CFI change; ΔRMSEA=RMSEA change; ΔSRMR=SRMR change

*p*<.001

size (Shi et al., 2020). Based on these arguments, the fit of the estimated model in the Argentine sample could be evaluated by taking the SRMR values into consideration, which show a good fit to the data. Therefore, only the Indian sample was excluded from the measurement invariance study. Next, the estimation of the MGCFA was carried out with the 16 samples that offered good fit indices for the four-factor model. The results showed the existence of metric invariance (see Table 3), but they did not confirm the scalar invariance (CFI=0.845, RMSEA=0.070, SRMR=0.076, ΔCFI=-0.079, ΔRMSEA=0.019, ΔSRMR=0.021).
Given that no scalar invariance was found, the alignment procedure was per-
formed. After obtaining an unsatisfactory result with the free alignment approach,
the fixed approach was tested. When observing the latent means of the 16 groups, it
was concluded that the country with a combination closest to 0 in the four factors was
Ecuador (Table 4, label 17). Therefore, the configural model was specified as a fixed
alignment analysis with four factor means for Ecuador restricted to 0 and the factor
means for the remaining 14 freely estimated countries. Table 4 shows the results of
the alignment measurement for the 16 countries and regions finally considered for the
analysis, the fit function contribution of both the factor loading and intercept for each
item on the SBI, and R².

As Table 4 reveals, regarding factor loadings, items 14, 15, 19, and 20 were invari-
ant across the 16 countries and regions, whereas item 7 showed less invariance and
was non-invariant in the regions of Chile (3), Spain (8), Valencia (11), and Uruguay
(15). Regarding the percentage of non-invariance, the four factors obtained an aver-
age between the factor loadings and intercepts below 25%, the maximum criterion
for non-invariance. The Indolence factor was shown to have the highest non-invari-
ance across countries or regions (21.4%), whereas the Guilt factor showed the lowest
percentage of non-invariance (17.5%). Regarding the fit functions, item 20 contrib-
utes the least to the fit function (-40.53). This result implies that this item is the most
invariant across all the samples (it exhibits the least amount of non-invariance). In
addition, the R² values showed values close to 1 on the most invariant items, but
some inconsistencies were found, for example, on item 15 (R²=0.77) or item 20
(R²=0.74), where R² shows a lower value than other less non-invariant items (items
5, 10, 12, 17, or 18). One possible explanation for this discrepancy would be that
the alignment method estimates the simplest model with the largest amount of non-
invariance, but if the approximate measurement invariance assumption does not hold,
the simplest and most invariant model may not be the true model (Asparouhov &
Muthén, 2014). Finally, Table 5 shows the comparison of the latent means across
countries or regions for the four factors of the SBI.

As the table reveals, the French sample (6) has significantly higher estimated
latent means in the Psychological Exhaustion, Indolence, and Guilt factors, and the
lowest in the Enthusiasm toward the job factor, compared to the rest of the countries
and regions. Specifically, in the Guilt factor, the estimated latent mean shows values
significantly higher than those of the other countries and regions, except Italy (7) and
Brazil (2), where the difference is not statistically significant.

Another interesting result is the one observed for the Andean countries. In addi-
tion to the previously mentioned result, in the Guilt factor, the countries with lower
estimated latent means are Ecuador (17), Uruguay (15), and Chile (3), with no sig-
nificant differences between them. Regarding the Enthusiasm toward the job factor,
Colombia (4), Peru (13), and Ecuador (17) show the highest estimated latent means.
Furthermore, Colombia shows a significantly higher estimated latent mean than those
of the other countries or regions, except Ecuador, with which there are no statisti-
cally significant differences. In addition, these three countries (Colombia, Peru, and
Ecuador) show the lowest estimated latent means in the Psychological Exhaustion
and Indolence factors, as does Argentina in the Indolence factor, with Ecuador always
being the country with a significantly lower estimated latent mean in these factors.
Discussion

The purpose of this study was to test the measurement invariance of the SBI (Gil-
Monte, 2019) among professionals across 17 countries and regions. The SBI has
shown a four-factor structure that remains constant in the different studies that have
analyzed its psychometric properties in Latin America (e.g., Carlotto et al., 2015;
García et al., 2022; Gil-Monte et al., 2013; Olivares-Faúndez et al., 2018) and in
Europe (e.g., Bosle & Gil-Monte 2010; Figueiredo-Ferraz et al., 2013; Gil-Monte
& Manzano-García, 2015; Misiolek et al., 2017; Viotti et al., 2015). These results
indicate that the SBI offers good evidence of construct validity. However, the cul-
tural context has an important effect on the way people from different countries and
regions interpret a psychological construct. Therefore, in order to compare the mea-
surements obtained with the SBI across countries, it is necessary to check the exist-
ence of measurement invariance. For this reason, the objective of this study was to
study the cross-cultural invariance of the SBI. However, when samples from many
countries are used, it is very difficult to obtain evidence of scalar measure invariance.
For this reason, a different psychometric approach was used (Asparouhov & Muthén,
2014) that optimizes the estimation of means despite the presence of a certain degree
of measurement non-invariance (Byrne & van de Vijver, 2017) and makes it possible
to identify the most non-invariant parameters.

The results of the analyses show that the four-factor structure fits the entire sam-
ple. Likewise, the CFAs carried out with the sample from each country or region offer
a good fit of the four-factor model to all the samples, except Argentina and India.
The poor fit of the Indian sample to the model is probably due to the limited sample
size. However, although in principle it might seem that the four-factor model does
not offer a good fit in the Argentine sample due to the inconsistency between some
fit indices, the results obtained in some simulation studies (Lai & Green, 2016; Shi
et al., 2019, 2020) allow us to suggest that there is a good fit of the data. Therefore,
only the Indian sample was excluded from the study, with 16 samples remaining: 15
countries and 1 region (Valencia, Spain).

Regarding the measurement invariance, it can be concluded that the SBI shows
scalar measurement invariance, which makes it possible to compare the four factors
of burnout across the sixteen samples finally included in the study. When comparing
latent means by country or region, some interesting results stand out. On the one hand,
the highest levels of perceived burnout are observed in the French sample because the
data show the lowest scores on the Enthusiasm toward the Job factor and the highest
scores on the Psychological Exhaustion, Indolence, and Guilt factors, compared to
the other samples. On the other hand, some Andean countries (Colombia, Ecuador,
and Peru), show the lowest levels of perceived burnout because they show the highest
scores on Enthusiasm toward the Job and the lowest scores on Psychological Exhaus-
tion and Indolence, compared to the other samples, with the exception of Colom-
bia vs. Argentina for Indolence. In addition, in the Indolence dimension, a trend is
observed, with the European countries (France, Portugal, Poland, and Italy), except
Spain, showing higher levels than the Latin American countries, except Chile. Data
from France, Ecuador, and Peru were collected in 2022, and so the time of data collec-
tion and the influence of job conditions as a result of the COVID-19 pandemic do not
<table><caption>**Table 4** Results of the Alignment Measurement Invariance (non-invariance) of the Spanish Burnout Inventory over 16 Countries and Regions, Fit Function Contribution of both the Factor Loading and Intercept for each Item, and R²</caption><thead><tr><th rowspan="2"></th><th colspan="2">Factor loadings</th><th colspan="4">Intercepts</th><th colspan="2">All%</th></tr><tr><th>Countries and regions</th><th>%</th><th>Fit function contribution</th><th>R<sup>2</sup></th><th>Countries and regions</th><th>%</th><th>Fit function contribution</th><th>R<sup>2</sup></th></tr></thead><tbody><tr><td>Factor Enthusiasm toward the Job</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Item 1</td><td>12 (3) 4 5 6 7 (8) 9 10 11 12 13 14 (15)<br>17</td><td>8.8</td><td>-46.55</td><td>0.71</td><td>1 (2) 3 (4) (5) (6) (7) (8) 9 10 (11) 12 13<br>(14) (15) 17</td><td>30.0</td><td>-51.37</td><td>0.84</td></tr><tr><td>Item 5</td><td>12 (3) 4 5 6 7 8 9 10 11 12 13 14 15 17</td><td></td><td>-42.35</td><td>0.90</td><td>12 3 4 5 6 (7) (8) 9 10 (11) 12 13 14 (15) 17</td><td></td><td>-45.93</td><td>0.95</td></tr><tr><td>Item 10</td><td>12 (3) 4 5 6 7 8 9 (10) 11 (12) 13 14<br>15 17</td><td></td><td>-43.59</td><td>0.84</td><td>12 3 4 5 6 7 8 9 10 11 12 13 14 15 17</td><td></td><td>-41.97</td><td>0.95</td></tr><tr><td>Item 15</td><td>12 3 4 5 6 7 8 9 10 11 12 13 14 15 17</td><td></td><td>-42.11</td><td>0.77</td><td>1 (3) 4 5 (6) 7 8 9 (10) 11 12 13 14 (15) 17</td><td></td><td>-48.95</td><td>0.89</td></tr><tr><td>Item 19</td><td>12 3 4 5 6 7 8 9 10 11 12 13 14 15 17</td><td></td><td>-42.03</td><td>0.87</td><td>(1) 2 (3) 4 5 6 (7) (8) 9 (10) (11) 12 13 (14)<br>15 17</td><td></td><td>-55.88</td><td>0.87</td></tr><tr><td>Factor<br>Psychological<br>Exhaustion</td><td></td><td>9.4</td><td></td><td></td><td></td><td>32.8</td><td></td><td>21.1</td></tr><tr><td>Item 8</td><td>12 3 4 5 (6) 7 8 9 10 11 (12) 13 14 15<br>17</td><td></td><td>-43.98</td><td>0.48</td><td>1 (2) (3) 4 5 (6) (7) 8 (9) 10 11 12 13 (14)<br>(15) 17</td><td></td><td>-71.76</td><td>0.51</td></tr><tr><td>Item 12</td><td>12 3 4 5 6 7 8 9 10 11 12 13 14 (15) 17</td><td></td><td>-40.82</td><td>0.88</td><td>12 (3) 4 5 6 (7) 8 9 10 11 12 13 (14) (15) 17</td><td></td><td>-52.92</td><td>0.73</td></tr><tr><td>Item 17</td><td>12 3 4 5 6 7 8 9 10 11 12 13 14 (15) 17</td><td></td><td>-43.16</td><td>0.85</td><td>12 (3) 4 5 6 (7) 8 9 10 (11) 12 (13) (14)<br>(15) (17)</td><td></td><td>-56.84</td><td>0.87</td></tr><tr><td>Item 18</td><td>1 (2) 3 4 5 (6) 7 8 9 10 11 12 13 14 15<br>17</td><td></td><td>-41.57</td><td>0.86</td><td>12 3 4 5 6 (7) (8) 9 10 11 12 13 14 15 (17)</td><td></td><td>-42.95</td><td>0.96</td></tr><tr><td>Factor<br>Indolence</td><td></td><td>11.5</td><td></td><td></td><td></td><td>31.5</td><td></td><td>21.5</td></tr><tr><td>Item 2</td><td>12 3 4 5 6 7 8 (9) (10) 11 12 13 14 15<br>17</td><td></td><td>-48.82</td><td>0.54</td><td>(1) (2) 3 4 5 6 (7) (8) 9 10 (11) 12 13 14<br>(15) 17</td><td></td><td>-72.75</td><td>0.64</td></tr><tr><td>Item 3</td><td>12 3 4 5 6 7 8 (9) 10 11 12 13 14 15 17</td><td></td><td>-49.05</td><td>0.63</td><td>1 (2) 3 4 5 6 7 8 9 10 11 (12) 13 14 (15) 17</td><td></td><td>-51.63</td><td>0.91</td></tr></tbody></table>

<figure><img src="image_7.png" /></figure>
<table><caption>Table 4 (continued)</caption><thead><tr><th></th><th>Factor loadings</th><th>Intercepts</th><th>All%</th></tr></thead><tbody><tr><td>Item 6</td><td>1.2 (3) 4 (5) 6.7 (8) 9 10 11 12 13 14<br>15 17</td><td>0.00<br>1 (2) 3 4 (5) 6 (7) (8) (9) (10) 11 12 13 14<br>15 17</td><td>0.39</td></tr><tr><td>Item 7</td><td>1.2 (3) 4.5 6.7 (8) 9 10 (11) 12 13 14<br>(15) 17</td><td>-50.65<br>0.58<br>(1) (2) 3 4.5 6.7 8 (9) 10 11 (12) (13) 14 15<br>(17)</td><td>-91.72<br>-57.91<br>0.64</td></tr><tr><td>Item 11</td><td>1.2 3 4.5 6.7 8 9 10 11 12 13 14 (15) 17</td><td>-48.96<br>0.70<br>(1) 2.3 (4) 5.6 7 (8) 9 10 (11) (12) 13 14<br>15 17</td><td>-73.88<br>-64</td></tr><tr><td>Item 14</td><td>1.2 3 4.5 6.7 8 9 10 11 12 13 14 15 17</td><td>-42.23<br>0.87<br>1.2 3 4 (5) 6 7 8 9 10 11 (12) (13) 14 (15) 17</td><td>-66.53<br>0.62</td></tr><tr><td>Factor Guilt</td><td>6.3</td><td>28.8</td><td>17.5</td></tr><tr><td>Item 4</td><td>1.2 3 4.5 6.7 8 9 10 11 12 (13) 14 15 17</td><td>-46.86<br>0.38<br>1 (2) (3) 4 (5) 6 (7) 8 (9) 10 11 (12) 13 14<br>15 (17)</td><td>-61.79<br>0.56</td></tr><tr><td>Item 9</td><td>1.2 3 4.5 6.7 8 9 10 11 12 13 (14) 15 17</td><td>-43.42<br>0.71<br>1.2 3 4.5 6.7 8 9 10 11 12 13 (14) 15 (17)</td><td>-47.72<br>0.85</td></tr><tr><td>Item 13</td><td>1.2 3 4.5 6.7 8 9 10 11 12 13 14 (15) 17</td><td>-44.35<br>0.77<br>1.2 3 4.5 (6) 7 8 (9) 10 11 (12) 13 14 (15) 17</td><td>-47.10<br>0.80</td></tr><tr><td>Item 16</td><td>1.2 3 4.5 (6) 7 8 9 10 11 12 13 14 (15)<br>17</td><td>-48.19<br>0.00<br>1.2 3 (4) (5) (6) 7 (8) 9 10 (11) 12 (13) (14)<br>(15) (17)</td><td>-62.20<br>0.00</td></tr><tr><td>Item 20</td><td>1.2 3 4.5 6.7 8 9 10 11 12 13 14 15 17</td><td>-40.53<br>0.74<br>1.2 3 4.5 6.7 8 9 10 11 12 13 14 (15) 17</td><td>-42.57<br>0.83</td></tr></tbody></table>

*Note.* Non-invariant parameters are bolded and parenthesized. Although one sample was excluded in the alignment analysis, the region and country labels have been kept for clarity.

Country and regions labels: 1=Argentina; 2=Brazil; 3=Chile; 4=Colombia; 5=Costa Rica; 6=France; 7=Italy; 8=Spain; 9=Poland; 10=Puerto Rico; 11=Valencia (Spain); 12=Mexico; 13=Peru; 14=Portugal; 15=Uruguay; 17=Ecuador

% = percent non-invariance; All% = average percent non-invariance

<figure><img src="image_8.png" /></figure>
<table><caption>**Table 5** Comparison of the Latent Means between Countries or Regions for the Four Factors of the Spanish Burnout Inventory</caption><thead><tr><th>Country</th><th>Mean Value</th><th>Countries with significantly smaller factor mean</th><th>Country</th><th>Mean Value</th><th>Countries with significantly smaller factor mean</th></tr></thead><tbody><tr><td>Factor Enthusiasm toward the Job</td><td></td><td></td><td>Factor Psychological Exhaustion</td><td></td><td></td></tr><tr><td>4 Colombia</td><td>0.098</td><td>13 11 8 5 7 12 1 2 15 14 3 9 10 6</td><td>6 France</td><td>1.313</td><td>2 3 7 12 14 9 15 8 1 11 5 13 4 17</td></tr><tr><td>17 Ecuador</td><td>0.000</td><td>11 8 5 7 12 2 15 14 3 9 10 6</td><td>10 Puerto Rico</td><td>1.298</td><td>2 3 7 12 14 9 15 8 1 11 5 13 4 17</td></tr><tr><td>13 Peru</td><td>-0.117</td><td>7 12 1 2 15 14 3 9 10 6</td><td>2 Brazil</td><td>0.823</td><td>3 7 12 14 9 15 8 1 11 5 13 4 17</td></tr><tr><td>11 Valencia (Spain)</td><td>-0.190</td><td>12 1 2 15 14 3 9 10 6</td><td>3 Chile</td><td>0.698</td><td>15 8 1 11 5 13 4 17</td></tr><tr><td>8 Spain</td><td>-0.201</td><td>12 1 2 15 14 3 9 10 6</td><td>7 Italy</td><td>0.678</td><td>15 8 1 11 5 13 4 17</td></tr><tr><td>5 Costa Rica</td><td>-0.229</td><td>12 2 15 14 3 9 10 6</td><td>12 Mexico</td><td>0.600</td><td>15 8 11 5 13 4 17</td></tr><tr><td>7 Italy</td><td>-0.281</td><td>12 2 15 14 3 9 10 6</td><td>14 Portugal</td><td>0.580</td><td>8 11 5 13 4 17</td></tr><tr><td>12 Mexico</td><td>-0.441</td><td>2 15 14 3 9 10 6</td><td>9 Poland</td><td>0.565</td><td>11 5 13 4 17</td></tr><tr><td>1 Argentina</td><td>-0.442</td><td>2 15 14 3 9 10 6</td><td>15 Uruguay</td><td>0.485</td><td>5 13 4 17</td></tr><tr><td>2 Brazil</td><td>-0.653</td><td>3 9 10 6</td><td>8 Spain</td><td>0.473</td><td>5 13 4 17</td></tr><tr><td>15 Uruguay</td><td>-0.722</td><td>3 9 10 6</td><td>1 Argentina</td><td>0.470</td><td>13 4 17</td></tr><tr><td>14 Portugal</td><td>-0.725</td><td>3 9 10 6</td><td>11 Valencia (Spain)</td><td>0.447</td><td>13 4 17</td></tr><tr><td>3 Chile</td><td>-1.190</td><td>6</td><td>5 Costa Rica</td><td>0.343</td><td>4 17</td></tr><tr><td>9 Poland</td><td>-1.306</td><td>6</td><td>13 Peru</td><td>0.266</td><td>17</td></tr><tr><td>10 Puerto Rico</td><td>-1.308</td><td>6</td><td>4 Colombia</td><td>0.208</td><td>17</td></tr><tr><td>6 France</td><td>-1.669</td><td></td><td>17 Ecuador</td><td>0.000</td><td></td></tr><tr><td>Factor Indolence</td><td></td><td></td><td>Factor Guilt</td><td></td><td></td></tr><tr><td>6 France</td><td>2.382</td><td>14 9 3 7 12 10 2 11 15 5 8 4 1 13 17</td><td>6 France</td><td>0.770</td><td>14 9 13 12 1 10 5 11 8 4 17 15 3</td></tr><tr><td>14 Portugal</td><td>1.519</td><td>10 2 11 15 5 8 4 1 13 17</td><td>7 Italy</td><td>0.708</td><td>2 14 9 13 12 1 10 5 11 8 4 17 15 3</td></tr><tr><td>9 Poland</td><td>1.422</td><td>10 2 11 15 5 8 4 1 13 17</td><td>2 Brazil</td><td>0.562</td><td>9 13 12 1 10 5 11 8 4 17 15 3</td></tr><tr><td>3 Chile</td><td>1.377</td><td>10 2 11 15 5 8 4 1 13 17</td><td>14 Portugal</td><td>0.484</td><td>13 12 1 10 5 11 8 4 17 15 3</td></tr><tr><td>7 Italy</td><td>1.326</td><td>10 2 11 15 5 8 4 1 13 17</td><td>9 Poland</td><td>0.402</td><td>5 11 8 4 17 15 3</td></tr><tr><td>12 Mexico</td><td>1.280</td><td>2 11 15 5 8 4 1 13 17</td><td>13 Peru</td><td>0.304</td><td>11 8 4 17 15 3</td></tr><tr><td>10 Puerto Rico</td><td>1.121</td><td>8 4 1 13 17</td><td>12 Mexico</td><td>0.282</td><td>11 8 4 17 15 3</td></tr><tr><td>2 Brazil</td><td>1.079</td><td>8 4 1 13 17</td><td>1 Argentina</td><td>0.255</td><td>4 17 15 3</td></tr></tbody></table>

<figure><img src="image_9.png" /></figure>
<table><caption>Table 5 (continued)</caption><thead><tr><th>Country</th><th>Mean Value</th><th>Countries with significantly smaller factor mean</th><th>Country</th><th>Mean Value</th><th>Countries with significantly smaller factor mean</th></tr></thead><tbody><tr><td>11 Valencia (Spain)</td><td>1.030</td><td>8 4 13 17</td><td>10 Puerto Rico</td><td>0.243</td><td>8 4 17 15 3</td></tr><tr><td>15 Uruguay</td><td>1.005</td><td>4 1 13 17</td><td>5 Costa Rica</td><td>0.207</td><td>4 17 15 3</td></tr><tr><td>5 Costa Rica</td><td>1.003</td><td>4 1 13 17</td><td>11 Valencia (Spain)</td><td>0.151</td><td>4 17 15 3</td></tr><tr><td>8 Spain</td><td>0.906</td><td>4 1 13 17</td><td>8 Spain</td><td>0.117</td><td>17 15 3</td></tr><tr><td>4 Colombia</td><td>0.762</td><td>1 13 17</td><td>4 Colombia</td><td>0.039</td><td>3</td></tr><tr><td>1 Argentina</td><td>0.412</td><td>17</td><td>17 Ecuador</td><td>0.000</td><td></td></tr><tr><td>13 Peru</td><td>0.261</td><td>17</td><td>15 Uruguay</td><td>-0.007</td><td></td></tr><tr><td>17 Ecuador</td><td>0.000</td><td></td><td>3 Chile</td><td>-0.081</td><td></td></tr></tbody></table>

<figure><img src="image_10.png" /></figure>
seem to explain these differences. In addition, all the participants in the samples from France, Italy, Argentina, Peru, and Ecuador were teachers. These differences could be explained by taking into consideration the different social and cultural values in Europe and Latin America. Affective values are transmitted more in Latin America, whereas moral, social, and intellectual values predominate in European education (Manzano-García & Tomé-Fernández, 2017). Moreover, studies based on Hofstede's Cultural Dimensions Theory have concluded that individualist traits characterize people from Western Europe, whereas people from South America are described as having collectivist characteristics (Green et al., 2005), for example, France or Italy vs. Ecuador or Peru (https://www.hofstede-insights.com/country-comparison/).

Some differences observed with the samples from Chile, Colombia, Costa Rica and Poland may also be influenced by the professional characteristics of these samples, since the data from those countries were not collected in teachers (see Table 1 and Participants and Procedures section). An interesting result was the significant difference in Indolence between the Spanish sample that answered the questionnaire in Spanish and the one that answered in Catalan, given that the two samples consist of teachers working in the same geographical area (Valencian Community). The difference could be explained by the fact that the highest percentage (74.3%) of teachers who answered in Catalan were teaching in secondary school (12 to 18 years old), and the lowest percentage (25.7%) were teaching in kindergarten (3 to 6 years old) and primary school (6 to 12 years old), whereas the majority of those who responded in Spanish were teaching in kindergarten and primary school (58.8%), and the lowest percentage were teaching in secondary school (41.2%). High school students may be more demanding and show more negative and disruptive behaviors, and so teachers in secondary schools might have more discipline problems (Shaheen & Mahmood, 2016). In addition, societal respect for teachers in secondary schools seems to have declined, contributing to the development of higher levels of indolence.

## Limitations and Future Directions

In interpreting our findings, several limitations should be noted. First, sample sizes vary across countries, ranging from 210 (India), 302 (Argentina), and 316 (France) to 4,734 (Uruguay). When studying the factorial invariance of a measure between groups, these groups usually have different sizes, and when the sizes of the samples are quite unbalanced, invariance violations might not be detected because the fit function in the factorial analysis of multiple groups includes a weighting based on the sample size of the group (Yoon & Lai, 2018). However, the alignment approach we used to test for approximate measurement invariance provides an automated procedure that can overcome important limitations of traditional CFA procedures when comparing many groups. We still do not know to what extent very different sample sizes between groups can affect this method of measuring invariance.

Second, we were not able to obtain samples that are representative of their respective countries but very similar in features that might influence burnout syndrome (e.g., occupational sector, different cultures in each country). As previously mentioned, some results could be explained by the differences in the occupational sector of the participants from the different countries. Third, the data collection procedures
and recruitment strategies differed by country (pencil and paper vs. online), which
can produce differences across countries (see Spector et al., 2015).

As future research directions, it might be beneficial to examine measurement invariance across other demographic and occupational groups (e.g., women vs. men, health vs. education participants) in future research.

Conclusion and Practical Implications

Our findings highlight the recommendation of using invariance testing in psycho-
logical research to better assess whether psychometric instruments are appropriate
for group comparisons. Likewise, the usefulness of the alignment method to study
the invariance among multiple groups has been demonstrated. The results support
the validity of the SBI in the countries and regions of Europe and Latin America
included in this study. We have advanced international research on burnout by intro-
ducing a reliable measure to assess it in six languages (Catalan, French, Italian, Pol-
ish, Portuguese, and Spanish) and in 16 countries and regions (excluding India) and
demonstrating its psychometric qualities. We found evidence (i.e., factor validity,
homogeneity within countries, and measurement invariance) that the Spanish Burn-
out Inventory (Gil-Monte, 2019) is a reliable and valid measure that can be used
for international research projects and surveys that are concerned with research on
burnout, job stress consequences, and quality of working life. This evidence is a
prerequisite for identifying links between burnout levels and specific country char-
acteristics that might help to better understand the relationship between a country’s
culture and burnout.

Currently, burnout in teachers (Fernández-Suárez et al., 2021; Pressley et al., 2021; Weißenfels et al., 2022) and health professionals (Kane, 2022; Stokowski et al., 2020) is a serious problem that has increased during the COVID-19 pandemic because of the deterioration of their working conditions. Therefore, it is necessary to carry out studies to identify the evolution of this health problem and to compare the prevalence between countries and regions. To conduct these studies, psychometric instruments with sufficient measurement invariance must be applied, avoiding comparative biases due to the measurement instrument. The results of this study support the validity of the SBI to carry out these studies in the countries and regions in Europe and Latin America included in the present study.

**Acknowledgements** The authors would like to thank the participants of this study for their voluntary participation in the study.

**Authors' Contributions** PGM: conceptualisation, data curation, project administration, writing—original draft, writing—review and editing. BE and IC: methodology, formal analysis, writing—original draft, writing—review and editing. PGL: writing—original draft, writing—review and editing. GROUP: data curation. All authors read and approved the final manuscript.

**Funding** The authors did not receive support from any organization for the submitted work. Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature.
**Data Availability** The datasets generated during and/or analysed during the current study are not publicly available due to their proprietary nature but are available from the corresponding author on reasonable request.

**Declarations**

**Financial interests** The authors have no relevant financial or non-financial interests to disclose.

**Competing Interests** The authors declare that they have no competing interests.

**Ethics Approval and Consent to Participate** The study was approved by the Ethics Committee for Research on Human Beings of the universities participating in the study. All participants are of legal age and gave their informed consent.

**Consent for Publication** Not applicable.

**Open Access** This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

**References**

Abramson, A. (2022). Burnout and stress are everywhere. *Monitor on Psychology*, 53(1), 72–73

Alföldy, S., & Gil-Monte, P. R. (2010). *Syndrom vyhoření při práci v manažerských pracovních pozicích*. *Srovnání českých manažerů pracujících v České republice a v zahraničí* [The burnout syndrome associated with working at managerial positions. A comparison of Czech managers working in the Czech Republic and abroad]. En *Psychologická řada*, PSY-029 (pp. 1–20). Fakulta sociálních věd UK. Pražské sociálně vědní studie

Alsalhe, T. A., Chalghaf, N., Guelmami, N., Azaiez, F., & Bragazzi, N. L. (2021). Occupational burnout prevalence and its determinants among physical education teachers: A systematic review and meta-analysis. *Frontiers in Human Neuroscience*, 15, 553230. https://doi.org/10.3389/fnhum.2021.553230

American Psychological Association (2022). Work and well-being survey reports. https://www.apa.org/pubs/reports/work-well-being/compounding-pressure-2021

Asparouhov, T., & Muthén, B. (2014). Multiple-group factor analysis alignment. *Structural Equation Modeling*, 21(4), 495–508. https://doi.org/10.1080/10705511.2014.919210

Bosle, A., & Gil-Monte, P. R. (2010). Psychometric properties of the Spanish Burnout Inventory in German professionals: Preliminary results. *Ansiedad y Estrés*, 6(2–3), 283–291

Brown, T. A. (2015). *Confirmatory factor analysis for applied research*. Guilford publications

Byrne, B. M., & van de Vijver, F. J. R. (2017). The maximum likelihood alignment approach to testing for approximate measurement invariance: A paradigmatic cross-cultural application. *Psicothema*, 29(4), 539–551. https://doi.org/10.7334/psicothema2017.178

Carlotto, M. S., & Câmara, S. G. (2019). Prevalence and predictors of burnout syndrome among public elementary school teachers. *Análise Psicológica*, 37(2), 135–146. https://doi.org/10.14417/ap.1471

Carlotto, M. S., Gil-Monte, P. R., & Figueiredo-Ferraz, H. (2015). Factor analysis of the Spanish Burnout Inventory among public administration employees. *Japanese Psychological Research*, 57(2), 155–165. https://doi.org/10.1111/jpr.12071

Chen, F. F. (2007). Sensitivity of goodness of fit indexes to lack of measurement invariance. *Structural Equation Modeling*, 14(3), 464–504. https://doi.org/10.1080/10705510701301834
Cieciuch, J., Davidov, E., Schmidt, P., & Algesheimer, R. (2019). How to obtain comparable measures for cross-national comparisons. *Kolner Zeitschrift Fur Soziologie Und Sozialpsychologie, 71*, 157–186.
https://doi.org/10.1007/s11577-019-00598-7

Cramer, R. J., Ireland, J. L., Hartley, V., Long, M. M., Ireland, C. A., & Wilkins, T. (2020). Coping, mental health, and subjective well-being among mental health staff working in secure forensic psychiatric settings: Results from a workplace health assessment. *Psychological Services, 17*(2), 160–169.
https://doi.org/10.1037/ser0000354

Díaz, F., & Gómez, I. C. (2016). La investigación sobre el síndrome de burnout en Latinoamérica entre 2000 y el 2010 [The research on burnout syndrome in Latin America between 2000 and 2010]. *Psicología desde el Caribe, 33*(1), 113–131. https://doi.org/10.14482/psdc.33.1.8065

Diehl, L., & Carlotto, M. S. (2020). Síndrome de Burnout em professores: diferenças entre níveis de ensino. *Research Society and Development, 9*(5), e62952623. https://doi.org/10.33448/rsd-v9i5.2623

Dijxhoorn, A. Q., Brom, L., van der Linden, Y. M., Leget, C., & Raijmakers, N. J. (2021). Prevalence of burnout in healthcare professionals providing palliative care and the effect of interventions to reduce symptoms: A systematic literature review. *Palliative Medicine, 35*(1), 6–26. https://doi.org/10.1177/0269216320956825

Doulougeri, K., Georganta, K., & Montgomery, A. (2016). “Diagnosing” burnout among healthcare professionals: Can we find consensus? *Cogent Medicine, 3*(1), 1. https://doi.org/10.1080/2331205X.2016.1237605

Esteves, G. G., Leão, A. A., & Alves, E. (2020). Evidências de validade do “Cuestionario para la Evaluación del Síndrome de Quemarse por el Trabajo” em profissionais da saúde [Evidences of validity of “Cuestionario para la Evaluación del Síndrome de Quemarse por el Trabajo” in health professionals]. *Psico, 51*(4), https://doi.org/10.15448/1980-8623.2020.4.33335. Article e-33335

Eurofound (2016). *Sixth European Working Conditions Survey - Overview report*. Publications Office of the European Union. https://www.eurofound.europa.eu/sites/default/files/ef_publication/field_e document/ef1634en.pdf

Eurofound (2018). *Burnout in the workplace: A review of data and policy responses in the EU*. Publications Office of the European Union. https://www.eurofound.europa.eu/sites/default/files/ef_publica tion/field_e document/ef18047en.pdf

Fernández-Suárez, I., García-González, M. A., Torrano, F., & García-González, G. (2021). Study of the prevalence of burnout in university professors in the period 2005–2020. *Education Research International, 21*, https://doi.org/10.1155/2021/7810659. Article 7810659

Figueiredo-Ferraz, H., Gil-Monte, P. R., & Grau-Alberola, E. (2013). Psychometric properties of the “Spanish Burnout Inventory” (SBI): Adaptation and validation in a Portuguese-speaking sample. *European Review of Applied Psychology, 63*, 33–40. https://doi.org/10.1016/j.erap.2012.08.003

Figueiredo-Ferraz, H., Gil-Monte, P. R., Grau-Alberola, E., & Ribeiro, B. (2021). The mediator role of feelings of guilt in the process of burnout and psychosomatic disorders: A cross-cultural study. *Frontiers in Psychology, 12*, Article 751211. https://doi.org/10.3389/fpsyg.2021.751211

García, L. M., Aguirre, D. C., Zapata, C. M., & García, H. I. (2022). Validity and reliability of the SBI (Spanish Burnout Inventory) in medical surgical specialists. *Colombian Journal of Anesthesiology, 50*, e1017. https://doi.org/10.5554/22562087.e1017

Gil-Monte, P. R. (2005). *El síndrome de quemarse por el trabajo. Una enfermedad laboral en la sociedad del bienestar* [Burnout: An occupational illness in the society of well-being]. Pirámide

Gil-Monte, P. R. (2012). Influence of guilt on the relationship between burnout and depression. *European Psychologist, 17*(3), 231–236. https://doi.org/10.1027/1016-9040/a000096

Gil-Monte, P. R. (2019). *CESQT. Cuestionario para la Evaluación del Síndrome de Quemarse por el Trabajo* [SBI: The Spanish Burnout Inventory] (2nd ed.). TEA

Gil-Monte, P. R., Carlotto, M. S., & Gonçalves, S. (2010). Validation of the Brazilian version of the “Spanish Burnout Inventory” in teachers. *Revista de Saúde Pública, 44*(1), 140–147. https://doi.org/10.1590/S0034-89102010000100015

Gil-Monte, P. R., Carlotto, M. S., & Gonçalves, S. (2011). Prevalence of burnout in a sample of Brazilian teachers. *European Journal of Psychiatry, 25*(4), 205–212. https://doi.org/10.4321/S0213-61632011000400003

Gil-Monte, P. R., Figueiredo-Ferraz, H., & Valdez Bonilla, H. (2013). Factor analysis of the Spanish Burnout Inventory among Mexican prison employees. *Canadian Journal of Behavioural Science / Revue canadienne des sciences du comportement, 45*(2), 96–104. https://doi.org/10.1037/a0027883
Gil-Monte, P. R., & Manzano-García, G. (2015). Psychometric properties of the Spanish Burnout Inventory among staff nurses. *Journal of Psychiatric and Mental Health Nursing, 22*(10), 756-763. https://doi.org/10.1111/jpm.12255

Gil-Monte, P. R., & Olivares, V. (2011). Psychometric properties of the "Spanish Burnout Inventory" in Chilean professionals working to physical disabled people. *The Spanish Journal of Psychology, 14*(1), 441-451. https://doi.org/10.5209/rev_SJOP.2011.v14.n1.40

Gillet, N., Morin, A. J. S., Colombat, P., Ndiaye, A., & Fouquereau, E. (2020). Burnout profiles: dimensionality, replicability, and associations with predictors and outcomes. *Current Psychology*. Advance online publication. https://doi.org/10.1007/s12144-021-01807-3

Green, E. G. T., Deschamps, J. C., & Páez, D. (2005). Variation of individualism and collectivism within and between 20 countries. A typological analysis. *Journal of Cross-Cultural Psychology, 36*(3), 321-339. https://doi.org/10.1177/0022022104273654

Guidetti, G., Viotti, S., Bruno, A., & Converso, D. (2018a). Teachers' work ability: a study of relationships between collective efficacy and self-efficacy beliefs. *Psychology Research and Behavior Management, 11*, 197-206. https://doi.org/10.2147/PRBM.S157850

Guidetti, G., Viotti, S., Gil-Monte, P. R., & Converso, D. (2018b). Feeling guilty or not guilty. Identifying burnout profiles among Italian teachers. *Current Psychology, 37*, 769-780. https://doi.org/10.1007/s12144-016-9556-6

Halbesleben, J. R., & Demerouti, E. (2005). The construct validity of an alternative measure of burnout: Investigating the English translation of the Oldenburg Burnout Inventory. *Work & Stress, 19*(3), 208-220. https://doi.org/10.1080/02678370500340728

Hiver, C., Villa, A., Bellagamba, G., & Lehucher-Michel, M. P. (2022). Burnout prevalence among European physicians: A systematic review and meta-analysis. *International Archives of Occupational and Environmental Health, 95*, 259-273. https://doi.org/10.1007/s00420-021-01782-z

Hu, L. T., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. *Structural Equation Modeling, 6*(1), 1-55. https://doi.org/10.1080/10705519909540118

Kane, L. (2022, January 21). Physician burnout & depression report 2022: Stress, anxiety, and anger. *Medscape Psychiatry*. https://www.medscape.com/slideshow/2022-lifestyle-burnout-6014664 19

Kim, L. E., Oxley, L., & Asbury, K. (2021). "My brain feels like a browser with 100 tabs open": A longitudinal study of teachers' mental health and wellbeing during the COVID-19 pandemic. *British Journal of Educational Psychology*. Advance online publication, Article e12450. https://doi.org/10.1111/bjp.12450

Kristensen, T. S., Borritz, M., Villadsen, E., & Christensen, K. B. (2005). The Copenhagen Burnout Inventory: A new tool for the assessment of burnout. *Work & Stress, 19*(3), 192-207. https://doi.org/10.1080/02678370500297720

Lai, K., & Green, S. B. (2016). The problem with having two watches: Assessment of fit when RMSEA and CFI disagree. *Multivariate Behavioral Research, 51*(2-3), 220-239. https://doi.org/10.1080/00273171.2015.1134306

Leiter, M. P., & Maslach, C. (2016). Latent burnout profiles: A new approach to understanding the burnout experience. *Burnout Research, 3*, 89-100. https://doi.org/10.1016/j.burn.2016.09.001

Lheureux, F., Truchot, D., Borteyrou, X., & Rascle, N. (2017). The Maslach Burnout Inventory – Human Services Survey (MBI-HSS): factor structure, wording effect and psychometric qualities of known problematic items. *Le Travail Humain, 80*(2), 161-186. https://doi.org/10.3917/th.802.0161

Li, S., Li, Y., Lv, H., Jiang, R., Zhao, P., Zheng, X., Wang, L., Li, J., & Mao, F. (2020). The prevalence and correlates of burnout among Chinese preschool teachers. *Bmc Public Health, 20*, 160. https://doi.org/10.1186/s12889-020-8287-7

Llorca-Pellicer, M., Soto-Rubio, A., & Gil-Monte, P. R. (2021). Development of burnout syndrome in non-university teachers: Influence of demand and resource variables. *Frontiers in Psychology, 12*, 644025. https://doi.org/10.3389/fpsyg.2021.644025

Loera, B., Converso, D., & Viotti, S. (2014). Evaluating the psychometric properties of the Maslach Burnout Inventory-Human Services Survey (MBI-HSS) among Italian nurses: How many factors must a researcher consider? *Plos One, 9*(12), e114987. https://doi.org/10.1371/journal.pone.0114987

Manzano-García, B., & Tomé-Fernández, M. (2017). Intercultural values in the European and Latin American basic education. *Procedia - Social and Behavioral Sciences, 237*, 130-136. https://doi.org/10.1016/j.sbspro.2017.02.053

Maslach, C., & Jackson, S. (1981). *Maslach Burnout Inventory manual*. Consulting Psychologists Press
Maslach, C., Jackson, S. E., & Leiter, M. P. (1996). *Maslach Burnout Inventory manual* (3rd ed.). Consulting Psychologists Press

Maslach, C., & Leiter, M. P. (2017). Understanding burnout: New models. In C. L. Cooper, & J. C. Quick (Eds.), *The handbook of stress and health: A guide to research and practice* (pp. 36–56). Wiley Blackwell

McCormack, H. M., MacIntyre, T. E., O’Shea, D., Herring, M. P., & Campbell, M. J. (2018). The prevalence and cause(s) of burnout among applied psychologists: A systematic review. *Frontiers in Psychology, 9*, 1897. https://doi.org/10.3389/fpsyg.2018.01897

Medscape (2021). Death by 1000 Cuts’: Medscape National Physician Burnout & Suicide Report 2021. https://www.medscape.com/slideshow/2021-lifestyle-burnout-6013456#1

Misiołek, A., Gil-Monte, P. R., & Misiołek, H. (2017). Prevalence of burnout in Polish anesthesiologists and anesthetist nursing professionals: A comparative non-randomized cross-sectional study. *Journal of Health Psychology, 22*(4), 465–474. https://doi.org/10.1177/1359105315604377

Misiołek-Marín, A., Soto-Rubio, A., Misiołek, H., & Gil-Monte, P. R. (2020). Influence of burnout and feelings of guilt on depression and health in anesthesiologists. *International Journal of Environmental Research and Public Health, 17*(24), 9267, 1–17. https://doi.org/10.3390/ijerph17249267

Muthén, L. K., & Muthén, B. (2017). *Mplus User's Guide* (8th ed.). Muthén & Muthén

Naji, L., Singh, B., Shah, A., Naji, F., Dennis, B., Kavanagh, O., Banfield, L., Alyass, A., Razak, F., Samaan, Z., Profetto, J., Thabane, L., & Sohani, Z. N. (2021). Global prevalence of burnout among postgraduate medical trainees: A systematic review and meta-regression. *CMAJ Open, 9*(1), E189–E200. https://doi.org/10.9778/cmajo.20200068

Olivares, V. E., & Gil-Monte, P. R. (2007). Análisis de las propiedades psicométricas del “Cuestionario para la Evaluación del Síndrome de Quemarse por el Trabajo” (CESQT) en profesionales chilenos [An exploratory factor analysis of the Job Burnout Syndrome Assessment Questionnaire (CESQT) in a Chilean sample]. *Ansiedad y Estrés, 13*, 229–240

Olivares-Faúndez, V., Gil-Monte, P. R., Montaño, R., Barrera, R., Fredes, D., & Figueiredo-Ferraz, H. (2018). Validez factorial del cuestionario para la evaluación del síndrome de quemarse por el trabajo (CESQT) en profesionales de servicios [Factorial validity of the Spanish Burnout Inventory (SBI) in a sample of service professionals]. *Interciencia, 43*(6), 417–424

Parker, G., & Tavella, G. (2021). Distinguishing burnout from clinical depression: A theoretical differentiation template. *Journal of Affective Disorders, 281*, 168–173. https://doi.org/10.1016/j.jad.2020.12.022

Pressley, T., Ha, C., & Learn, E. (2021). Teacher stress and anxiety during COVID-19: An empirical study. *School Psychology, 36*(5), 367–376. https://doi.org/10.1037/spq0000468

Putnick, D. L., & Bornstein, M. (2016). Measurement invariance conventions and reporting: The state of the art and future directions for psychological research. *Developmental Review, 41*, 71–90. https://doi.org/10.1016/j.dr.2016.06.004

Pyhältö, K., Pietarinen, J., Haverinen, K., Tikkanen, L., & Soini, T. (2021). Teacher burnout profiles and proactive strategies. *European Journal of Psychology of Education, 36*, 219–242. https://doi.org/10.1007/s10212-020-00465-6

Rabasa, B., Figueiredo-Ferraz, H., Gil-Monte, P. R., & Llorca-Pellicer, M. (2016). The role of guilt in the relationship between teacher’s job burnout syndrome and the inclination toward absenteeism. *Revista de Psicodidáctica, 21*(1), 103–119. https://doi.org/10.1387/RevPsicodidact.13076

Rhemtulla, M., Brosseau-Liard, P., & Savalei, V. (2012). When can categorical variables be treated as continuous? A comparison of robust continuous and categorical SEM estimation methods under sub-optimal conditions. *Psychological Methods, 17*(3), 354–373. https://doi.org/10.1037/a0029315

Rotenstein, L. S., Torre, M., Ramos, M. A., Rosales, R., Guille, C., Sen, S., & Mata, D. A. (2018). Prevalence of burnout among physicians: A systematic review. *Journal Of The American Medical Association, 320*(11), 1131–1150. https://doi.org/10.1001/jama.2018.12777

Salmela-Aro, K., & Upadyaya, K. (2020). School engagement and school burnout profiles during high school - The role of socio-emotional skills. *European Journal of Developmental Psychology, 17*(6), 943–964. https://doi.org/10.1080/17405629.2020.1785860

Sawicki, A. J., Żemojtel-Piotrowska, M., Balcerowska, J. M., Sawicka, M. J., Piotrowski, J., Sedikides, C., Jonason, P. K., Maltby, J., Adamovic, M., Agada, A. M. D., Ahmed, O., Al-Shawaf, L., Appiah, S. C. Y., Ardi, R., Babakr, Z. H., Bălătescu, S., Bonato, M., Cowden, R. G., Chobthamkit, P., & Zand, S. (2022). The Fear of COVID-19 scale: Its structure and measurement invariance across 48 countries. *Psychological Assessment, 34*(3), 294–310. https://doi.org/10.1037/pas0001102
Schaufeli, W. B., & Van Dierendonck, D. (1993). The construct validity of two burnout measures. *Journal of Organizational Behavior, 14*, 631–647. https://doi.org/10.1002/job.4030140703

Serna, H. M., García, B. R., Olguín, J. E., & Vásquez, D. (2018). Spanish Burnout Inventory: An approach based on meta-analysis. *Revista Contaduría y Administración, 63*(2), 1–13. https://doi.org/10.22201/fca.24488410e.2018.1120

Shaheen, F., & Mahmood, N. (2016). Demographic variables as determinants of emotional burnout among public school teachers. *Journal of Research and Reflections in Education, 10*(1), 37–50

Shi, D., Lee, T., & Maydeu-Olivares, A. (2019). Understanding the model size effect on SEM fit indices. *Educational and Psychological Measurement, 79*(2), 310–334. https://doi.org/10.1177/0013164418783530

Shi, D., Maydeu-Olivares, A., & Rosseel, Y. (2020). Assessing fit in ordinal factor analysis models: SRMR vs. RMSEA. *Structural Equation Modeling: A Multidisciplinary Journal, 27*(1), 1–15. https://doi.org/10.1080/10705511.2019.1611434

Spector, P. E., Liu, C., & Sanchez, J. I. (2015). Methodological and substantive issues in conducting multinational and cross-cultural research. *Annual Review of Organizational Psychology and Organizational Behavior, 2*, 101–131. https://doi.org/10.1146/annurev-orgpsych-032414-111310

Stokowski, L. A., Bastida, D., McBride, M., & Berry, E. (2020, December 24). *Medscape nurse career satisfaction report 2020*. https://www.medscape.com/slideshow/2020-nurse-career-satisfaction-6013455

Tejada, P., & Gómez, V. (2012). Prevalencia y factores demográficos y laborales asociados al burnout de psiquiatras en Colombia [Prevalence and both demographic and work factors associated with burnout of psychiatrists in Colombia]. *Universitas Psychologica, 11*(3), 863–873. https://doi.org/10.11144/javeriana.upsy11-3.pfdl

Viotti, S., Gil-Monte, P. R., & Converso, D. (2015). Toward validating the Italian version of the “Spanish Burnout Inventory”: a preliminary study. *Revista da Escola de Enfermagem da USP, 49*(5), 819–825. https://doi.org/10.1590/S0080-623420150000500016

Weißenfels, M., Kropp, E., & Perels, F. (2022). Changes in teacher burnout and self-efficacy during the COVID-19 pandemic: Interrelations and e-learning variables related to change. *Frontiers in Education, 6*, 736992. https://doi.org/10.3389/feduc.2021.736992

Woo, T., Ho, R., Tang, A., & Tama, W. (2020). Global prevalence of burnout symptoms among nurses: A systematic review and meta-analysis. *Journal of Psychiatric Research, 123*, 9–20. https://doi.org/10.1016/j.jpsychires.2019.12.015

World Health Organization (2019). International statistical classification of diseases and related health problems (11th ed.). https://icd.who.int/

World Medical Association. (2013). Declaration of Helsinki: Ethical principles for medical research involving human subjects. *Journal Of The American Medical Association, 310*(20), 2191–2194. https://doi.org/10.1001/jama.2013.281053

Yoon, M., & Lai, M. H. C. (2018). Testing factorial invariance with unbalanced samples. *Structural Equation Modeling: A Multidisciplinary Journal, 25*(2), 201–213. https://doi.org/10.1080/10705511.2017.1387859

Yuan, K. H., & Bentler, P. M. (2000). Three likelihood-based methods for mean and covariance structure analysis with nonnormal missing data. *Sociological Methodology, 30*(1), 165–200. https://doi.org/10.1111/0081-1750.00078

**Publisher's Note** Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

## Authors and Affiliations

Pedro R. Gil-Monte¹,² · Begoña Espejo² · Irene Checa² · Pedro Gil-LaOrden¹,² ·

Kezia Angeline J³ · Mary Sandra Carlotto⁴ · Daniela Converso⁵ ·

Ángel Deroncele-Acosta⁶ · Hugo Figueiredo-Ferraz¹,⁷ · Laura Galarza⁸ ·

Viviola Gómez-Ortiz⁹ · Ester Grau-Alberola¹,¹⁰ · Javier Labarthe¹¹ ·

Marta Llorca-Pellicer¹ · Christy Mekala V¹² · Alejandra Misiolek-Marín¹³ ·

Eldis Román-Cao¹⁴ · Edwin Salas-Blas¹⁵ · Sandrine Schoenenberger¹⁶,¹⁷ ·
Sara Unda-Rojas¹⁸ · Sara Viotti⁵

<figure><img src="image_11.png" /></figure>

Begoña Espejo

bespejo@uv.es

¹ Unidad de Investigación Psicosocial de la Conducta Organizacional (UNIPSICO), Valencia, Spain

² Faculty of Psychology (Dep. Methodology of Behavioral Sciences), University of Valencia, Av. Blasco Ibáñez, 21; 46010, Valencia, Spain

³ St. John's Research Institute, Bangalore, South India, India

⁴ Universidade do Vale do Rio dos Sinos, Cristo Rei, Brazil

⁵ Università degli Studi di Torino, Turin, Italy

⁶ Universidad César Vallejo, Lima, Peru

⁷ Universidad Internacional de Valencia (VIU), Valencia, Spain

⁸ Universidad de Puerto Rico, San Juan, Puerto Rico

⁹ Universidad de Los Andes, Bogotá, Colombia

¹⁰ Universidad Internacional de La Rioja, Logroño, Spain

¹¹ Universidad Católica del Uruguay, Montevideo, Uruguay

¹² Bishops College of Nursing, Dharapuram, Tamil Nadu, India

¹³ Clínica Art Psychology and Psychotherapy, Barcelona, Spain

¹⁴ Universidad Técnica de Manabi, Portoviejo, Ecuador

¹⁵ Universidad Peruana de Ciencias Aplicadas, Lima, Peru

¹⁶ Université Catholique de Lille, Lille, France

¹⁷ Université de Lorraine), Metz, France

¹⁸ Universidad Nacional Autónoma de México, FES Zaragoza, Ciudad de Mexico, Mexico

<figure><img src="image_12.png" /></figure>