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. 2022 Nov 22;17(11):e0267530. doi: 10.1371/journal.pone.0267530

High prevalence of burnout syndrome among medical and nonmedical residents during the COVID-19 pandemic

Rebeca da Nóbrega Lucena Pinho 1,*, Thais Ferreira Costa 2, Nayane Miranda Silva 1, Adriana Ferreira Barros-Areal 3, André de Matos Salles 4, Andrea Pedrosa Ribeiro Alves Oliveira 5, Carlos Henrique Reis Esselin Rassi 6, Ciro Martins Gomes 7, Dayde Lane Mendonça da Silva 8, Fernando Araújo Rodrigues de Oliveira 9, Isadora Jochims 10, Ivan Henrique Ranulfo Vaz Filho 11, Lucas Alves de Brito Oliveira 12, Marta Alves Rosal 13, Marta Pinheiro Lima 14, Mayra Veloso Ayrimoraes Soares 10, Patricia Shu Kurizky 15, Viviane Cristina Uliana Peterle 16, Ana Paula Monteiro Gomides 17, Licia Maria Henrique da Mota 18, Cleandro Pires de Albuquerque 19, Cezar Kozak Simaan 20, Veronica Moreira Amado 5
Editor: Mohammad Hossein Ebrahimi21
PMCID: PMC9681108  PMID: 36413548

Abstract

Background

Since the beginning of the COVID-19 pandemic, health professionals have been working under extreme conditions, increasing the risk of physical and mental illness. We evaluated the prevalence of burnout and its associated factors among postgraduate student residents in health professions during the global health crisis.

Methods

Healthcare residents were recruited from all across Brazil between July and September 2020 through digital forms containing instruments for assessing burnout (Oldenburg Burnout Inventory (OLBI)), resilience (brief resilient coping scale (BRCS)) and anxiety, stress and depression (depression, anxiety and stress scale (DASS-21) and Patient Health Questionnaire (PHQ-9)). Additionally, the relationships between burnout and chronic diseases, autonomy and educational adequacy in the residency programme, personal protective equipment (PPE), workload and care for patients with COVID-19 were evaluated. The chi-square test, Student’s t test, Pearson’s correlation test and logistic regression were performed.

Results

A total of 1,313 participants were included: mean (standard deviation) age, 27.8 (4.4) years; female gender, 78.1%; white race, 59.3%; and physicians, 51.3%. The overall prevalence of burnout was 33.4%. The odds (odds ratio [95% confidence interval]) of burnout were higher in the presence of pre-existing diseases (1.76 [1.26–2.47]) and weekly work > 60 h (1.36 [1.03–1.79]) and were lower in the presence of high resilience (0.84 [0.81–0.88]), autonomy (0.87 [0.81–0.93]), and educational structure (0.77 [0.73–0.82]), adequate availability of PPE (0.72 [0.63–0.83]) and non-white race (0.63 [0.47–0.83]). Burnout was correlated with anxiety (r = 0.47; p < 0.05), stress (r: 0.58; p < 0.05) and depression (r: 0.65; p < 0.05).

Conclusions

We observed a high prevalence of burnout among residents during the COVID-19 pandemic. Individual characteristics and conditions related to the work environment were associated with a higher or lower occurrence of the syndrome.

Introduction

In early 2020, COVID-19, caused by a new coronavirus (SARS-CoV-2), spread rapidly throughout the world and reached pandemic status, requiring the rapid and extensive reorganization of health services [1–3]. There were many challenges to which health professionals were imposed, such as uncertainties regarding the magnitude, duration and global effects of the health crisis, the level of preparation of individuals and of the institutions to cope with the crisis, and the risk of infection, which could be life threatening. In this context of insecurity, anxiety and work overload, the risk of physical and mental illness among this population is a concern [4].

Brazil was the first South American country to report a confirmed case of COVID-19 (02/26/2020) [5]. The epidemiological scenario soon became dramatic, with uncontrolled growth in the number of confirmed cases and deaths, tending to the collapse of national health systems [6]. On 10/07/2021, the country surpassed 600,000 deaths due to the disease, becoming the nation with the second highest number of deaths in the world, behind only the United States of America [7].

Medical and nonmedical health residencies involve extensive programme content and a high weekly workload. In the context of the pandemic, the prolonged and uncomfortable use of personal protective equipment (PPE), irregular hydration and feeding and sleep deprivation increased fatigue and the risk of burnout [3]. Burnout is defined as a multifaceted construct characterized by emotional exhaustion, depersonalization and a low sense of personal accomplishment [8].

The literature on this topic is still scarce despite the importance of understanding the impact of the pandemic on health professionals in training and estimating the prevalence of burnout and its relationship with other mental conditions such as stress, depression and anxiety, thus contributing to the development of alternatives that mitigate this problem.

The objective of the present study was to evaluate the prevalence of burnout syndrome among health professionals in training, medical residencies and other health areas and to identify the factors associated with the occurrence of burnout in this specific population.

Materials and methods

This study served as the baseline evaluation of a longitudinal study still in progress, whose protocol has been published; the study included post-graduate student residents in health professions, aged over 18 years, assigned to the direct provision of care to patients during the COVID-19 pandemic and sought to identify risk factors associated with burnout in this population [9].

Recruitment occurred via e-mail, messages on social networks, posters in hospitals and the university hospital intranet containing QR codes with links to the survey forms. The codes and links were sent to the 7,215 residents of 40 university hospitals affiliated with the Brazilian Hospital Services Company (Empresa Brasileira de Serviços Hospitalares—EBSERH). EBSERH is a public company linked to the Ministry of Education established to manage federal university hospitals. Health professional residents at any healthcare institution in the country could also participate.

Data collection was performed using a structured electronic form (via Microsoft Forms) designed to gather information on the clinical and epidemiological characteristics of the participants; the form also included the assessment instruments used in the study, in accordance with the approved protocol [9]. The following instruments were applied.

1. Oldenburg Burnout Inventory (OLBI): This instrument has been adapted for and validated in Portuguese for the evaluation of burnout and contains eight questions in each of the “disengagement” (OLBI-D) and “exhaustion” (OLBI-E) subscales, totalling 16 questions (OLBI Total); responses are provided using a five-point Likert scale. Disengagement refers to distancing from work and the development of work-related cynical and negative attitudes and behaviours. Exhaustion refers to feelings of physical fatigue, need for rest, and feelings of overload and work-related emptiness [10]. We adopted the method used by Delgadillo et al. [11], who defined the cut-off point (values equal to or greater than the mean + 1 standard deviation) for the classification of the total OLBI score as “high”, thus indicative of burnout [11]. We applied this method using values observed in the Brazilian population [10].

2. Brief resilient coping scale (BRCS): This is a one-dimensional instrument adapted for and validated in Portuguese consisting of four items that assess the ability to adaptively cope with stress [12]. In this study, a score less than 13 was considered “low resilience”.

3. Degree of autonomy to decide behaviours at work: A visual numerical scale was used to evaluate each individual’s perception of his or her degree of autonomy at work. The responses ranged from 0 to 10 (0 “I have no autonomy” and 10 “I have full autonomy”). A value ≤ 4 indicated a low perception of autonomy at work.

4. Adequacy of the educational organization of the residency programme: A visual numerical scale was used to evaluate each individual’s perception of the adequacy of the educational structure of his or her residency programme. The responses ranged from 0 to 10, with 0 being “completely inadequate” and 10 being “completely adequate”. The cut-off point defined for “poor educational adequacy” was ≤ 5.

5. Availability of PPE: A 5-point Likert scale was used to evaluate the perception of residents regarding the adequacy of PPE availability in their professional practice. The following question was asked: “In your professional practice, in patient care, how often do you have sufficient and adequate PPE available?”. The possible responses were as follows: 1—at no time, 2—less than half the time, 3—half the time, 4—more than half the time and 5—all the time. The cut-off point for “inadequate PPE availability” was defined as ≤ 3.

6. External work link: Respondents provided an answer of YES or NO regarding the exercise of professional work outside of the residency programme.

7. Providing direct care to patients with COVID-19: Respondents provided an answer of YES or NO as to whether, in their practice in the residency programme, direct care was mandatory for patients with COVID-19.

8. Depression, anxiety and stress scale (DASS-21): This instrument has been translated into and validated for Portuguese [13] and is composed of three subscales covering the domains of depression (DASS21-D), anxiety (DASS21-A) and stress (DASS21-S), with cut-off points > 9, > 7 and > 14, respectively, for the classification of scores as “high”, thus indicative of the respective mental disorders.

9. Brief Depression Scale (Patient Health Questionnaire/PHQ-9): This instrument has been translated into and validated for Brazil [14]. It consists of nine questions that assess the frequency of depressive symptoms. The cut-off score for the classification of the scores as high, thus indicative of depressive disorder, was defined as ≥ 9.

The sample size was calculated considering the objectives of the longitudinal study, which is still in progress [9] and seeks to establish the incidence of burnout and identify its predictors among residents during the COVID-19 pandemic, corresponding to a cross-sectional evaluation of data obtained at the baseline of a longitudinal follow-up study. The sample size was calculated based on the following parameters:

  1. an expected prevalence of burnout of 28% among health professionals [15];

  2. an expected difference of 10 percentage points in the incidence of burnout between the exposure and control groups after 12 weeks of follow-up; and

  3. the offset of losses to follow-up (approximately 20%).

Thus, the minimum sample size was calculated as N = 1144 participants.

The data for the sample are provided as absolute and relative frequencies for categorical variables and as measures of central tendency and dispersion for continuous numerical variables. In bivariate analyses, associations between dichotomous categorical variables were verified using the chi-square test, with odds ratios and Cramer’s V used to estimate effect sizes. Differences between groups regarding continuous variables were verified by Student’s t test, with Welch correction for nonhomogeneous variances. Correlations were verified using Pearson’s r coefficient. Binomial logistic regression models were used to identify the presence of burnout and evaluate the independent contribution of several candidate predictor variables. Predictor variables that were significant in the bivariate analyses were incorporated into the multivariate analysis. Values of p < 0.05 were considered significant. The analyses were conducted in SPSS 25.

The study was approved by a local research ethics committee and the National Research Ethics Committee (Comitê de Ética em Pesquisa/Comissão Nacional de Ética em Pesquisa–CEP/CONEP), available at https://plataformabrasil.saude.gov.br/, under registration number CAAE: 33493920.0.0000.5558. All participants signed and received a copy of the informed consent form via e-mail.

Results

A total of 1,313 residents responded to the survey. The respondents were residents at 135 public, private and philanthropic health institutions from 25 federal units; 89.6% were affiliated with university hospitals.

The sample consisted of medical residents (51.3%, n = 674), nurses (8.8%, n = 115), pharmacists (6.9%, n = 91), nutritionists (6.2%, n = 82), psychologists (6.2%, n = 82), physical therapists (4.8%, n = 63), social workers (3.9%, n = 51), dentists (2.8%, n = 37), occupational therapists (1.7%, n = 22), and other residents (4.2%, n = 55). Among the participants, there was a predominance of the female gender and white race (Table 1). The provision of direct care to patients with COVID-19 was reported by 60.2% of the residents. Of the total, 17.8% reported having pre-existing diseases, among whom 93.1% were classified as being at increased risk for severe forms of COVID-19 [16].

Table 1. General characteristics of the study population.

Characteristics Total
n = 1313
Gender
    Female 1025 (78.2%)
    Male 285 (21.8%)
Race
    White 778 (59.3%)
    Non-white 535 (40.7%)
Nature of the educational institution
    Public 1277 (97.2%)
    Private or philanthropic 36 (2.8%)
University hospital
    Yes 1177 (89.6%)
    No 136 (10.4%)
Category of professional participant (1272 responses)
    Physician 674 (53.0%)
    Other health professional 598 (47%)
Providing direct care to patients with COVID-19
    Yes 790 (60.2%)
    No 523 (39.8%)
Presence of diseases (n = 1305)
    Yes 234 (17.9%)
    No 1071 (82.1%)
Increased risk for severe forms of COVID-19
    Yes 218 (16.7%)
    No 1087 (83.3%)
Perception of PPE availability
    Poor availability 281 (21.4%)
    Moderate to good availability 1032 (78.6%)
Perception of the educational organization of the residency programme
    Poor adequacy 558 (42.5%)
    Moderate to good adequacy 755 (57.5%)
Autonomy to decide behaviours at work
    Low autonomy 224 (17.1%)
    Moderate to high autonomy 1089 (82.9%)
Weekly workload
    ≤ 60 h 541 (41.2%)
    ≥ 60 h 772 (58.8%)
Activity outside the residency programme
    Yes 424 (32.3%)
    No 889 (67.7%)
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Regarding the weekly workday, 58.8% worked ≥ 60 hours per week; 67.7% did not work outside the residency programme; 78.6% reported that the adequacy of the availability of PPE for the provision of health care was moderate to good perception; 42.5% reported that the adequacy of the educational organization of their residency programme was poor; and 17.1% indicated low autonomy in deciding work behaviours (Table 1).

The mean (SD) age, for the overall sample, was 27.8 (4.4) years, and the mean scores for the instruments were as follows: OLBI-D, 2.8 (0.8); OLBI-E, 3.6 (0.7); OLBI Total, 3.2 (0.7); BRCS, 12.4 (3.8); DASS-21 depression, 15.3 (11.3); DASS-21 anxiety, 12.1 (10.3); DASS-21 stress, 20.3 (10.7); PHQ-9, 12.0 (6.5); perception of autonomy, 6.5 (2.1); and adequacy of the educational structure, 5.8 (2.5).

A moderate to strong positive correlation was observed between DASS-21 anxiety and OLBI-E (r: 0.48 and p < 0.05) and OLBI-Total (r: 0.47 and p < 0.05); between DASS-21 stress and OLBI-D (r: 0.46 and p < 0.05), OLBI-E (r: 0.57 and p <0.05) and OLBI-Total (r: 0.58 and p < 0.05); and between PHQ-9 (depression) and OLBI-D (r: 0.53 and p < 0.05), OLBI-E (r: 0.64 and p <0.05) and OLBI-Total (r: 0.65 and p < 0.05).

Table 2 shows the differences between medical residents and nonmedical residents regarding the scores obtained for the instruments used to evaluate resilience (BRCS), distancing (OLBI-D), exhaustion (OLBI-E), burnout (OLBI-Total), perception of autonomy and adequacy of the educational structure.

Table 2. Scores for medical and nonmedical residents on the instruments used to assess resilience, distancing, exhaustion, burnout, perception of autonomy and adequacy of the educational structure.

Variable Nonmedical* (n = 639) Medical* (n = 674) Difference in means [95% CI] p**
BRCS Resilience 12.26 (3.65)  12.56 (3.88) -0.30 [-0.71; 0.11] 0.155
OLBI Distancing 2.74 (0.81) 2.81 (0.86) -0.07 [-0.16; 0.02] 0.115
OLBI Exhaustion 3.58 (0.69) 3.53 (0.78) 0.05 [-0.03; 0.13] 0.208
OLBI Total 3.16 (0.66) 3.17 (0.74) - 0.01  [-0.09; 0.06] 0.771
Perception of autonomy 6.58 (2.10) 6.43 (2.12) 0.15 [-0.08; 0.38] 0.191
Adequacy of the educational structure 5.34 (2.50) 6.18 (2.37) -0.84 [-1.10; -0.58] < 0.001
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* The values in the table are the mean (standard deviation).

**Significance level (p value) based on Student’s t test.

The mean age of the medical residents was 29.2 (4.4) years, and that of the nonmedical residents was 26.4 (4.0) years (p < 0.001). Low resilience was found in more than half of the participants in both types of residency programmes (Table 3). Medical residents considered the educational structure of their residency programme more adequate than did nonmedical residents. The number of medical residents who had work activity outside the training programme and who provided direct care to patients with COVID-19 was significantly higher than that of nonmedical residents (Table 3).

Table 3. Differences between medical residents and nonmedical residents regarding the various characteristics studied—bivariate analyses (unadjusted).

Variable or outcome Nonmedical n (%) Medical n (%) Odds ratio [95% CI] p*
Gender (n = 1310)
    Female 549 (86.3%) 476 (70.6%) 2.63 [1.98–3.47] < 0.001
Race (n = 1313)
    White 338 (52.9%) 440 (65.3%) 0.60 [0.48–0.75] < 0.001
BRCS—Resilience (n = 1313)
    Low 414 (64.8%) 399 (59.2%) 1.26 [1.01–1.58] 0.037
OLBI–Burnout (n = 1313)
    High 202 (31.6%) 236 (35%) 0.86 [0.68–1.08] 0.191
Autonomy (n = 1313)
    Moderate/High 535 (83.7%) 554 (82.2%) 1.11 [0.84–1.49] 0.462
Educational structure (n = 1313)
    Adequate 312 (48.8%) 443 (65.7%) 0.50 [0.40–0.62] < 0.001
Availability of PPE (n = 1313)
    Moderate/High 515 (80.6%) 517 (76.7%) 1.26 [0.97–1.64] 0.086
    Weekly workload (n = 1313)
    >60 h 294 (46%) 478 (70.9%) 0.35 [0.28–0.44] < 0.001
    Activity outside the residency programme (n = 1313)
    Yes 8 (1.3%) 416 (61.7%) 0.01 [0.00–0.02] < 0.001
    Direct care for patients with COVID-19 (n = 1313)
    Yes 246 (38.5%) 544 (80.7%) 0.15 [0.12–0.19] < 0.001
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* Significance level (p value) based on the chi-square test.

The overall prevalence of burnout in our study was 33.4%. There was a significant association between burnout and the variables race, presence of pre-existing diseases, perception of autonomy, perception of adequacy of the educational structure, perception of availability of PPE, weekly workload and low resilience (Table 4).

Table 4. Association between burnout (OLBI) and various characteristics of the participants—bivariate analyses (not adjusted).

Variable Burnout frequencies* Odds ratio p **
n (%) [95% CI]
Gender (n = 1310)
    Male 94 (33%) 1.02 0.903
    Female 342 (33.4%) [0.77–1.35]
Race (n = 1313)
    White 279 (35.9%) 0.76 0.020
    Non -white 159 (29%.7) [0.60–0.96]
    Presence of diseases (n = 1305)
    No 331 (30.9%) 1.82 <0.001
    Yes 105 (44.9%) [1.36–2.43]
    Autonomy to decide behaviours at work (n = 1313)
    Low 132 (58.9%) 0.27 <0.001
    Moderate/High 306 (28.1%) [0.20–0.37]
    Perception of the educational organization of the residency programme (n = 1313)
    Inadequate 280 (50.2%) 0.26 <0.001
    Adequate 158 (20.9%) [0.21–0.34]
    Perception of adequacy of PPE availability (n = 1313)
    Low 140 (49.8%) 0.41 <0.001
    Moderate/High 298 (28.9%) [0.31–0.54]
    Weekly workload (n = 1313)
≤ 60 h 161 (29.8%) 1.32 0.021
> 60 h 277 (35.9%) [1.04–1.67]
    Activity outside the residency programme (n = 1313)
    No 291 (32.7%) 1.09 0.487
    Yes 147 (34.7%) [0.85–1.39]
    Direct provision of care to patients with COVID-19 (n = 1313)
    No 171 (32.7%) 1.05 0.679
    Yes 267 (33.8%) [0.83–1.33]
BRCS—Resilience (n = 1313)
    Moderate/High 93 (18.6%) 3.23 < 0.001
    Low 345 (42.4%) [2.48–4.20]
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* OLBI score ≥ mean + 1 SD unit

** Significance level (p value) according to the chi-square test.

There was no difference in the degree of resilience (BRCS) between genders (low resilience: female 62.3% [n = 639], male 60.4% [n = 172]; p = 0.540; OR 1.08; 95% CI 0.83–1.42) or between races (white 61.6% [n = 479], non-white 62.4% [n = 334]; p = 0.752; OR 1.03; 95% CI 0.83–1.3).

There were differences between genders regarding the type of residency programme, weekly workload, activity outside the residency programme and direct provision of care to patients with COVID-19 (Table 5).

Table 5. Differences between genders regarding the various variables studied.

Variable Male Female Odds ratio p*
n (%) n (%) [95% CI]
RACE (n = 1310)
    Non-white 124 (43.5%) 410 (40.0%) 0.87 0.286
[0.66–1.13]
Presence of illness (n = 1302)
    Yes 57 (20.2%) 175 (17.2%) 0.82 0.235
[0.59–1.14]
    Type of residency programme (n = 1310)
    Physician 198 (69.5%) 476 (46.4%) 0.38 < 0.001
[0.29–0.50]
BRCS—Resilience (n = 1310)
    Low 172 (60.4%) 639 (62.3%) 1.09 0.540
[0.83–1.42]
    Autonomy to decide behaviours at work (n = 1310)
    Moderate/High 226 (79.3%) 861 (84.0%) 1.37 0.062
[0.98–1.91]
Perception of adequacy of the educational structure (n = 1310)
    Adequate 162 (56.8%) 592 (57.8%) 1.04 0.782
[0.80–1.35]
    Perception of adequacy of PPE availability (n = 1310)
    Moderate/High 219 (76.8%) 811 (79.1%) 1.14 0.406
[0.84–1.56]
    Weekly workload (n = 1310)
    >60 h 185 (64.9%) 585 (57.1%) 0.72 0.017
[0.55–0.94]
    Activity outside the residency programme (n = 1310)
    Yes 142 (49.8%) 281 (27.4%) 0.38 < 0.001
[0.29–0.50]
    Direct provision of care to patients with COVID (n = 1310)
    Yes 213 (74.7%) 575 (56.1%) 0.43 < 0.001
[0.32–0.58]
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* Significance level (p value) according to the chi-square test

All individual characteristics that were significantly associated with burnout in the unadjusted (bivariate) analyses remained significant independent predictors of burnout syndrome in the multivariate analysis by logistic regression (Fig 1).

Fig 1. Multivariate logistic regression analysis of predictors associated with burnout (OLBI) among health residents.

Fig 1

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Discussion

The aim of this study was to evaluate the prevalence of burnout in post-graduate student residents in health professions in Brazil in the context of the COVID-19 pandemic. A notable aspect of this study is that it comparatively evaluates different residency programmes, reinforcing that data on nonmedical health residencies are scarce in the scientific literature.

Our sample had similar representativeness regarding the number of medical residents (n = 674) and nonmedical residents (n = 639). In a survey conducted on the website of the Ministry of Education (Committees of Medical and Multiprofessional Residencies in Health), no data were available on the composition of health residency programmes in Brazil regarding gender.

In health programmes, women represent the majority gender in post-graduate programmes in general [17]. However, there is a lack of data in studies with a methodology similar to that used herein. Almeida et al. [18] stated that females are more vulnerable to mental health problems, such as higher levels of stress, anxiety, depression and posttraumatic stress symptoms. Furthermore, there are data that indicate that women seek health services twice as often as men [19], which may justify a greater interest in participating in scientific research focused on mental health and the prevention of future problems.

Among health professionals working at the forefront of epidemic care, being female, of a younger age [20, 21] and in training [22] are risk factors for mental disorders, especially burnout. However, in our study, there was no association between burnout prevalence and gender or the provision of direct care to patients with COVID-19. Mental disorders, in general, are more frequent among women; biological, cultural and social components, such as overload resulting from double work shifts (family and external) and high socio-family demands, are indicated as predisposing factors for the emergence of psychological disorders in the female population [23].

The overall prevalence of burnout in our sample was 33.3%, with no significant differences between medical residents and nonmedical residents (35% vs. 31.6%, p = 0.191). Da Cruz Gouveia et al. [24] reported a prevalence of 27.9% in a Brazilian study that described the factors associated with burnout syndrome in residents of a university hospital in a pre-pandemic period [24]. In a recent study conducted in Japan to evaluate the prevalence of burnout in health professionals during the COVID-19 pandemic, an overall prevalence of 31.4% was reported [25].

In our study, the individual characteristics independently associated (multivariate analysis) with a higher prevalence of burnout were the presence of chronic diseases and weekly workload > 60 h, and those associated with a lower prevalence of burnout were non-whites, perception of greater autonomy to decide behaviours at work, perception of an adequate educational structure of the residency programme, adequate availability of PPE, and greater resilience.

The emergence of burnout results from work overload and often occurs during the first two years of resident training, occurring cumulatively in up to 74% of resident physicians [26]. The results from a Chinese study conducted with medical professionals and nurses to assess burnout outside of a pandemic context indicate that long working hours contribute to the occurrence of burnout [27].

There is evidence in the scientific literature of a reduction in the prevalence of burnout among residents after the implementation of limits to working hours. In a study with 118 residents and interns, those who worked > 80 hours per week had a burnout prevalence 31% higher than that for those who worked < 80 hours per week [28]. Although burnout is usually attributed to high demands or work-related stress, the impact of excessively high workloads cannot be neglected. In our study, more than half of the participants (51.9%) had a weekly workday that exceeded 60 hours, which was a predictor of burnout. Significant differences were found between the types of residency programmes with respect to the variables activity outside the residency programme and weekly workload. This difference is justified because nonmedical residents must dedicate themselves exclusively to the residency programme and cannot perform work activities outside the programme [29].

In our study, we did not observe an association between burnout and the direct provision of care to patients with COVID-19, despite greater exposure to the risk of infection resulting from this activity. A study conducted in Iran with 266 nurses evaluated the level of burnout during the COVID-19 pandemic; in front line and non-front line workers, the work stress and burnout scores for the group exposed to COVID-19 were significantly higher than those for the non-exposure group [30]. The differences between the populations studied and the working conditions between the two countries may contribute to explaining the differences observed in the studies.

Additionally, in our study, the presence of pre-existing diseases in residents increased the chance of developing burnout by 76%. Consistent with this finding, Lo et al. [31] suggested that health problems such as viral and respiratory infections, diabetes, cardiovascular diseases, obesity and liver diseases can result from burnout. This study was conducted with workers from a monitor manufacturing company in central Taiwan [31].

A strong correlation was observed between the presence of burnout and the poor adequacy of the educational structure of the programmes, which was reported in high percentages by both medical and nonmedical participants (34.3% and 51.2%, respectively). This finding reinforces the fact that burnout is also driven by organizational factors in addition to individual factors. Other factors related to training, such as the high demand for learning in relatively short periods of time and the strict supervision of behaviour (limitation of autonomy), represent additional risks for residents compared to physicians [32].

The low perception of PPE availability also had an impact on the development of burnout in our study. Consistent with this finding, a study conducted to identify factors that contribute to burnout among health professionals during the COVID-19 pandemic found that available and adequate PPE was considered a protective factor for burnout and that a lack of PPE was a causative agent of stress [1].

The mean BRCS score was numerically lower (suggesting a lower degree of resilience) among nonmedical residents than among medical residents, although the difference did not reach statistical significance (Table 2). However, when evaluating the proportions of individuals with low resilience, there was a significant difference between the groups, with a higher frequency of low resilience among non-physicians (Table 3).

Regarding the limitations of the study, we recognize the possibility of selection bias towards individuals who agreed to participate in the study. The findings do not necessarily reflect the reality of individuals who chose not to participate. However, participation is voluntary in any clinical study. Thus, the possibility of not reflecting those who chose not to participate is inherent to any survey and not only to this study.

In addition, there was a clear predominance of responses from residents associated with university hospitals, which generally have a better educational and physical structure than do most non-profit, non-university hospitals of similar size (with some exceptions). Therefore, the reality of non-university hospitals may not be adequately reflected in the data from this study.

The study is also limited by the exclusive use of digital forms for remote data collection and the use of validated instruments for the evaluation of burnout, resilience, anxiety, stress and depression, without in-person clinical evaluations for the confirmation of the diagnoses suggested by the instruments.

Despite the limitations mentioned, the results of this study may be useful for developing strategies to prevent or mitigate the damage caused by burnout among residents and provide better working conditions and support for the mental health of these professionals in training.

Conclusions

The results of this study indicate a high prevalence of burnout among health professionals in training in the context of the COVID-19 pandemic. Individual characteristics as well as those related to working conditions are associated with the occurrence of burnout in this population.

Supporting information

S1 Data

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Acknowledgments

We would like to thank the University Hospital of Brasília, especially the Superintendency and the Division of Teaching and Research, and EBSERH for the support provided to this study.

Data Availability

All relevant data are within the paper and its Supporting Information files.

Funding Statement

This study was funded in part by the University of Brasília (UnB). No additional external funding was received for this study.

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Decision Letter 0

Mohammad Hossein Ebrahimi

19 Jul 2022

PONE-D-22-10606High prevalence of burnout syndrome among medical and nonmedical residents during the COVID-19 pandemicPLOS ONE

Dear Dr. Pinho,

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1. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: This study addresses an important issue concerning the COVID-19 impact. Nevertheless, I do have several concerns in this particular study I would like to address.

Comments in the article which needs clarification, rewrites and/or additional information

Title:

Why authors included the burnout only in the title despite measuring other conditions as anxiety, stress, depression, Resilience and others? Even should refer to it.

Also why authors involved medical and non-medical residents despite there were no difference between them regarding burnout syndrome at the end. (The overall prevalence of burnout in our sample was 33.3%, with no significant differences between medical residents and non-medical residents (35% vs. 31.6%, p = 0.191).

The Suggestive title (High prevalence of burnout and its associated factors among sample of Brazilians health residents during the COVID-19 pandemic).

Abstract:

Rewrite methods as: Healthcare residents were assessed for burnout (Oldenburg Burnout Inventory (OLBI)), resilience (brief resilient coping scale (BRCS), anxiety, stress and depression (depression, anxiety and stress scale (DASS-21) and Patient Health Questionnaire (PHQ-9)…………………etc.

Introduction:

• In early 2020, COVID-19, caused by a new coronavirus (SARS-CoV-2): write full name of SARS before writing the abbreviation (Severe acute respiratory syndrome (SARS).

• The objective of the present study was to evaluate the prevalence of burnout syndrome among health professionals in training, medical residencies (how to evaluate the prevalence, better to replace the action verb to determine) and write the goal if possible (what is the long-term outcome after determining the prevalence?

Material and methods:

• Mention the study design and study period (i.e. a descriptive online cross-sectional survey design was conducted from ……….to …….. (Write date beside month and year).

• Determine the type of sampling and the sampling technique (is it convenience sampling? How the authors reach to the healthcare residents all across the Brazil as mentioned in the abstract? Was it probability or non-probability sampling?

• How the sample size was calculated? Which software program was used (please write the reference) does they have list for all the public and private hospitals and health centers?

• Write in details about the study participants, selection criteria (inclusion and exclusion). Write the examples for medical and non-medical residents. Was there a pilot study?

• Concerning the questionnaire, was it structured as written? Or semi-structured?

What about the questions for the socio-demographic data and chronic diseases?

• In the data analysis and management: what about the normality test? The authors used parametric tests which mean that data was normally distributed. Also write the dependent and independent variables (please clarify and write in details)

Results:

• In table 1 the number of female (n=1025), male (n=285) isn’t equal to the total number (n= 1313 as mentioned in the table)

• Question (Increased risk for severe forms of COVID-19 in table 1) : what does this question mean, how the participant know whether they are at increased risk or not?

• Activity outside the residency program: give example for these activities as a note below the table.

• Where is the table for these results? (The mean (SD) age, for the overall sample, was 27.8 (4.4) years, and the mean scores for the instruments were as follows: OLBI-D, 2.8 (0.8); OLBI-E, 3.6 (0.7); OLBI Total, 3.2 (0.7); BRCS, 12.4 (3.8); DASS-21 depression, 15.3 (11.3); DASS-21 anxiety, 12.1 (10.3); DASS-21 stress, 20.3 (10.7); PHQ-9, 12.0 (6.5); perception of autonomy, 6.5 (2.1); and adequacy of the educational structure, 5.8 (2.5).

• Table 4: check the entire percentage % in the table. How it was calculated? for example

Number of male 94 (33%), number of female 342 (33.4%) how is it?

• Table 5: Differences Between Genders Regarding the Various Variables Studied. Why authors made this table despite it wasn’t from the objectives of the study to show the gender difference.

• Where the table for correlation as it is mentioned in the discussion and the abstract?

Discussion:

• The discussions missed data and interpretations on the regression analysis also the recommendations at the end.

Reviewer #2: #P 2 Line 37: after [18] There must be some word, it seems to be missing.

#P2 Line 46-53 : These statements should not be included in the introduction part with the discussion, theses should be moved to the discussion part. As well as line 64 onwards till the end of the introduction part.

Introduction part should be written in past tense as well the methodology part.

#P5 Research design should the written before the heading of "materials and methods"

Reviewer #3: The study appears to be well thought out and the manuscript is technically sound and well written. The sample size was quite large and the statistical analyses were appropriate and rigorously performed. All data underlying findings are available in the manuscript. The conclusions are also, supported by the data, and the findings are likely to be of global interest.

However, the authors should make a stronger justification for the paper and show how the paper contributes to new knowledge.

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PLoS One. 2022 Nov 22;17(11):e0267530. doi: 10.1371/journal.pone.0267530.r002

Author response to Decision Letter 0

17 Oct 2022

RESPONSE TO REWIEWERS:

1. Reviewer #1:

• Title:

Questions 1 and 2:

1. Why authors included the burnout only in the title despite measuring other conditions as anxiety, stress, depression, Resilience and others? And

2. Why authors involved medical and non-medical residents despite there were no difference between them regarding burnout syndrome at the end?

Answer: The absence of a significant difference between the medical and non-medical residents was a result, a finding of the research, considering that the work regimes and assignments are different in the different kinds off residents, due to the inherent differences in the residents professions. Initially, it was plausible to assume that there would be a difference between the groups, this was a question inherent to the reseach and after identifying this finding, it needed to be reported.

3. The Suggestive title (High prevalence of burnout and its associated factors among sample of Brazilians health residents during the COVID-19 pandemic).

Answer: We agree.

• Abstract:

1. Rewrite methods as: Healthcare residents were assessed for burnout (Oldenburg Burnout Inventory (OLBI)), resilience (brief resilient coping scale (BRCS), anxiety, stress and depression (depression, anxiety and stress scale (DASS-21) and Patient Health Questionnaire (PHQ-9)…………………etc.

Answer: We agree with the suggested way, however, we would end up losing important information that would be suppressed with this new rewriting. This information would be: sample constitution - national; derivation of information: July to September 2020 and the mode of reach: digital forms that contained the assessment instruments. Removing that fragment would remove this elementary information, so we would like to keep it as it is written.

• Introduction:

1. In early 2020, COVID-19, caused by a new coronavirus (SARS-CoV-2): write full name of SARS before writing the abbreviation (Severe acute respiratory syndrome (SARS).

Answer: Done as requested

2. The objective of the present study was to evaluate the prevalence of burnout syndrome among health professionals in training, medical residencies (how to evaluate the prevalence, better to replace the action verb to determine) and write the goal if possible (what is the long-term outcome after determining the prevalence?

Answer: For this research, the objective was to determine the prevalence of previously unknown burnout in this specific population and its associated factors. For the line of research that is still going on, there is a longitudinal research arm, which is not the target of this specific manuscrript. It has a long-term outcome that is to determine the incidence of burnout in individuals who did not have burnout at baseline and its risk factors. The practical implications of the prevalence findings are addressed in the discussion section.

• Materials and Methods:

1. Mention the study design and study period (i.e. a descriptive online cross-sectional survey design was conducted from ……….to …….. (Write date beside month and year).

Answer: Everything the reviewer asked for has been entered. This is not just a descriptive study. This study uses an inferential approach (when we are looking for factors associated with burnout with an estimate of the probability of occurrence at random values) and makes inferences (in terms of the prevalence itself with confidence intervals). Although the analytical capacity of cross-sectional studies is admittedly lower than that of longitudinal studies, especially experimental studies, we effectively also used analytical procedures in this study, because groups were compared using statistical inferential analytical procedures with estimation of values and confidence intervals.

2. Determine the type of sampling and the sampling technique (is it convenience sampling? How the authors reach to the healthcare residents all across the Brazil as mentioned in the abstract? Was it probability or non-probability sampling?

Answer: The study adopted a convenience sampling and a non-probability sampling (the information is already included in the manuscript, in the paragraph that begins with the sentence: "The study adopted a convenience, non-probability (non-random) sampling approach"). The study adopted a convenience, non-probability (non-random) sampling approach. The way in which the residents were recruited was described in this same paragraph: “Recruitment occurred via e-mail, messages on social networks, posters in hospitals and the university hospital intranet containing QR codes with links to the survey forms. The codes and links were sent to the 7,215 residents of 40 university hospitals affiliated with the Brazilian Hospital Services Company (Empresa Brasileira de Serviços Hospitalares - EBSERH). EBSERH is a public company linked to the Ministry of Education established to manage federal university hospitals. Health professional residents at any healthcare institution in the country, considering the inherently unconstrained reach of the messages on social networks, were also allowed to participate”.

3. How the sample size was calculated? Which software program was used (please write the reference) does they have list for all the public and private hospitals and health centers?

Answer: The sample was calculated not exclusively aiming at the cross-sectional study, but at the size, the longitudinal line that is still in progress and that will be reported when finished.

The G*Power 3.1.9.7 software was used. (already inserted in the references).

Initially, we had the e-mail of residents linked to EBSERH. As we also chose a mean of distributing the survey invitations that included social networks, considering that they do not have “barriers”, and also considering the inherently unconstrained nature of the reach of messages on social networks, we already anticipated the possibility that these invitations would reach residents outside the EBSERH conglomerate.

We didn't have a list of all the public and private hospitals in the country. We had a list of all active residents in the EBSERH network.

For this reason, the possibility of including participants from any institutions in the country that train human resources, in a postgraduate regime, in health área was expanded.

4. Write in details about the study participants, selection criteria (inclusion and exclusion). Write the examples for medical and non-medical residents. Was there a pilot study?

Answer: The eligibility criteria used for the inclusion of participants was: aged 18 years or above and postgraduate student in a medical residency or multidisciplinary residency program who has been designated for activities that involve direct patient care during the COVID-19 pandemic. The exclusion criteria have been defined as the explicit or assumed refusal to participate in the study as indicated by no response to telephone or electronic form interview attempts. The medical residents were physicians in training in several subspecialties of medicine such as: internal medicine, surgery, pediatrics, gynecology and obstetrics, orthopedics, cardiology, dermatology, among others. Non-medical residents were health professionals in training in other areas such as: nursing, nutrition, physical therapy, among others. There was a pilot study, a protocol entitle: Mental health and burnout syndrome among postgraduate students in medical and multidisciplinary residencies during the COVID-19 pandemic in Brazil: protocol for a prospective cohort study, that was published in 2021. And there will be a longitudinal follow up that is still in progress.

5. Concerning the questionnaire, was it structured as written? Or semi-structured?

Answer: The questionnaire was fully structured, as mentioned in the methodology.

6. What about the questions for the socio-demographic data and chronic diseases?

Answer: The most relevant chronic diseases were listed in the interview so that the participant could only select the option. There were also “OTHER” and “NONE” options. There were no open questions in the questionnaire.

7. In the data analysis and management: what about the normality test? The authors used parametric tests which mean that data was normally distributed.

Answer: Parametric tests were used as a standard technique only to fulfill the formality in the evaluation of the statistical distribution of the variable. However, due to the large sample size and the central limit theorem, parametric tests that have more resources at their disposal could be perfectly applicable. For this reason, they were chosen. Formally, the normality tests indicated that the distributions were not normal, they were significant.

8. Also write the dependent and independent variables (please clarify and write in details)

Answer: The dependent variable was the burnout score measured by the OLBI instrument. For some analyses, this score was dichotomized between high and low, as described in the methodology section. The independent variables, the predictors of burnout, were gender, race, presence of illness, autonomy at work, perception of the pedagogical organization of the residency program, perception of adequacy of the availability of PPE, weekly workload, work activity outside the residency program, direct delivery of care to COVID 19 patients, and resilience.

Additionally, as secondary and exploratory objectives of the work, differences between the sexes and between the professional categories of the residents were tested regarding the same independent variables used for prediction and regarding the actual occurrence of burnout.

• Results:

1. In table 1 the number of female (n=1025), male (n=285) isn’t equal to the total number (n= 1313 as mentioned in the table)

Answer: All calculated numbers and percentages indicated in the table refer to valid values, that is, those for which data were available for each of the variables.

For example: men (n=285) and women (n=1025) and 13 individuals for whom there was no information for sex in the database, that is, missing data. And for each variable, there may be some degree of missing data.

2. Question (Increased risk for severe forms of COVID-19 in table 1) : what does this question mean, how the participant know whether they are at increased risk or not?

Answer: Participants who had chronic comorbidities (chronic heart disease, diabetes mellitus, chronic lung disease, chronic kidney disease, chronic arterial hypertension), immunosuppression, pregnancy, alcohol consumption, smoking, neoplasia in treatment and others, were classified as high-risk patients for severe forms of COVID-19.

3. Activity outside the residency program: give example for these activities as a note below the table.

Answer: In Brazil, medical residents are authorized to exercise professional activity in extra time to the residency program (NOTE: footnote inserted in table 1).

4. Where is the table for these results? (The mean (SD) age, for the overall sample, was 27.8 (4.4) years, and the mean scores for the instruments were as follows: OLBI-D, 2.8 (0.8); OLBI-E, 3.6 (0.7); OLBI Total, 3.2 (0.7); BRCS, 12.4 (3.8); DASS-21 depression, 15.3 (11.3); DASS-21 anxiety, 12.1 (10.3); DASS-21 stress, 20.3 (10.7); PHQ-9, 12.0 (6.5); perception of autonomy, 6.5 (2.1); and adequacy of the educational structure, 5.8 (2.5).

Answer: Initially, it was chosen to avoid the redundancy of information between text and table, as a general rule. It was identified that this portion of results could be accommodated in the text without needing to be reported in a new table.

5. Table 4: check the entire percentage % in the table. How it was calculated? for example number of male 94 (33%), number of female 342 (33.4%) how is it?

Answer: The denominators of the fraction that generated the results were inserted for better visualization of the table (inserted in table 4). Then a footnote was created clarifying how the percentages were calculated.

6. Table 5: Differences Between Genders Regarding the Various Variables Studied. Why authors made this table despite it wasn’t from the objectives of the study to show the gender difference.

Answer: Tables and analyzes were additionally performed, testing differences between genders and professional categories for the same variables used as predictors of burnout.

7. Where the table for correlation as it is mentioned in the discussion and the abstract?

Answer: Paragraph 6 of the discussion presents the findings in the logistic regression analysis, the independent predictors that persisted as relevant within the multivariate model.

• Discussion:

1. The discussions missed data and interpretations on the regression analysis also the recommendations at the end.

Answer: Paragraph 6 of the discussion presents the findings in the logistic regression analysis, the independent predictors that persisted as relevant within the multivariate model.

2. Reviewer #2:

• #P 2 Line 37: after [18] There must be some word, it seems to be missing.

There is no missing word in this sentence: "Almeida et al. [18] stated that females are more vulnerable to mental health problems, such as higher levels of stress, anxiety, depression and posttraumatic stress symptoms".

• #P2 Line 46-53 : These statements should not be included in the introduction part with the discussion, theses should be moved to the discussion part. As well as line 64 onwards till the end of the introduction part. Introduction part should be written in past tense as well the methodology part.

• #P5 Research design should the written before the heading of "materials and methods": It is already described in the methodology.

3. Reviewer #3:

• The authors should make a stronger justification for the paper and show how the paper contributes to new knowledge.

Although burnout in health professionals undergoing training is not new, it is possible that stressors related to the COVD-19 pandemic may contribute to the increased prevalence of this phenomenon in this population.

The high prevalence of burnout found showed that the reality is complex, and it is not possible to attribute the findings to a single factor. Through this study, it was possible to conclude that, in the context of the pandemic, the increase in working hours and inadequate physical and pedagogical structural conditions, in addition to the difficulty in adapting to a stressful situation, characterized by less resilience, contributed negatively to mental health of medical and multiprofessional residents.

The knowledge from this study, mainly related to the predictors associated with the development of burnout, may be useful for the elaboration of strategies to mitigate the damage caused by this phenomenon, propose actions that reduce the potential damages and the creation of better working conditions and health for this population, essential for the proper functioning of the establishments providing health services to the population.

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Decision Letter 1

Mohammad Hossein Ebrahimi

19 Oct 2022

High prevalence of burnout syndrome among medical and nonmedical residents during the COVID-19 pandemic

PONE-D-22-10606R1

Dear Dr. Pinho,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

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Kind regards,

Mohammad Hossein Ebrahimi

Academic Editor

PLOS ONE

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