Abstract
Background
Burnout is a syndrome characterized by emotional exhaustion, depersonalization, and a reduced sense of accomplishment, which commonly arises from chronic workplace stress in the medical field. Given the higher risk of burnout in younger age groups reported in some studies, the Society for Neuro-Oncology (SNO) Young Investigator (YI) and Wellness Committees combined efforts to examine burnout in the SNO YI membership to better understand and address their needs.
Methods
We distributed an anonymous online survey to SNO members in 2019. Only those meeting the definition of a YI were asked to complete the survey. The survey consisted of questions about personal and professional characteristics as well as the validated Maslach Burnout Inventory-Human Services Survey (MBI-HSS) questionnaire. Statistical analyses included descriptive statistics, univariate and multivariate analyses, and incorporation of previously defined burnout profiles.
Results
Data were analyzed for 173 participants who self-identified as YI. Measures of burnout showed that YI members scored higher on emotional exhaustion and depersonalization compared to normative population but similar to those in a prior SNO general membership survey. With respect to burnout profiles, 30% of YI respondents classified as overextended and 15% as burnout. Organizational challenges were the most common contributors to stress.
Conclusions
Similar to results from a previous survey completed by general SNO membership, the prevalence of burnout among neuro-oncology clinical and research YI is high, and is mainly characterized by overextension, warranting interventions at institutional and organizational levels.
Keywords: burnout, neuro-oncology, young investigator
Burnout is defined as an occupational phenomenon resulting from chronic workplace stress that has not been successfully managed.1 It is a syndrome characterized by emotional exhaustion (feelings of being emotionally overextended by work), depersonalization (an unfeeling and impersonal response towards recipients of one’s care or service), and a reduced sense of personal achievement (reduced feelings of competence and successful accomplishment at work).2,3
Chronic burnout can lead to physical health problems, such as cardiovascular disease, chronic fatigue, and gastrointestinal issues,4 and mental health disorders like depression, anxiety, substance abuse, poor self-care, increased risk of motor vehicle crashes, and increased risk of suicide.2,5–7 It is also associated with increased prevalence of familial and marital conflicts and of divorce.4 If unmanaged, it can negatively impact patient care, resulting in increased medical errors and decreased quality of care and patient satisfaction, as well as the health care system, leading to increased physician turnover, escalating costs, and reduced patient access.2,8–10 It has also been associated with malpractice lawsuits.11 Work-related risk factors include excessive workload, clerical burden, inefficient work processes, work-home conflicts, and limited peer and institutional support.12–14 On the individual level, living alone, female gender, and younger age have been associated with higher rates of burnout.14,15
Burnout is common among healthcare professionals,16,17 with prevalence approaching or exceeding 50% in national studies.4,15 It affects physicians-in-training and practicing physicians, as well as nurses, biomedical scientists, and doctoral students.18,19 It is particularly prevalent in the neuro-oncology practice, marked by the continuous care of patients with poor prognosis in the setting of slow advances in treatment. Indeed, an anonymous survey completed by 345 members of the Society for Neuro-Oncology (SNO) in 2016 and 121 members of the European Association of Neuro-Oncology (EANO) in 2017 revealed high burnout prevalence rates of 63% and 60%, respectively.12 Given the higher risk of burnout in younger age groups, specifically in the field of oncology,14 the SNO Young Investigator and Wellness Committees combined efforts to evaluate burnout in the SNO young investigator (YI) membership to better understand and address their needs.
Materials and Methods
A link to an anonymous, online survey (via SurveyMonkey) was distributed via email to SNO members, including physicians, basic scientists, and allied health professionals (nurses, nurse practitioners, physician assistants, social workers, pharmacists, psychologists), on March 12, 2019. Only those meeting the definition of young investigator—defined as within 10 years from completion of a doctorate degree or post-doctorate position, within 10 years from completion of the last clinical training program such as residency or fellowship, and/or within the first 10 years of practice—were asked to participate in the survey.
The survey was modeled after a similar survey sent to the general SNO and EANO membership in 2016 and 2017,12 respectively, although modified specifically for young investigators. It consisted of two parts. The first part comprised questions related to demographics (eg, age, marital status), professional characteristics (eg, practice environment, work hours), and previously identified potential burnout risk factors and protective factors such as habits (e.g., exercise, alcohol intake) and career circumstances (eg, academic vs private practice). Career satisfaction and current stress levels were assessed using direct questions. Compared to the survey sent to SNO and EANO general memberships, the SNO YI survey contained additional questions to examine the influence of family life, including children, and dealing with dying patients. The second part comprised the Maslach Burnout Inventory-Human Services Survey (MBI-HSS), a validated survey designed for professionals in human services including medical personnel and the most widely accepted standard for burnout assessment.3 The instrument contains 22 items that gauge emotional exhaustion (EE), depersonalization (DP), and personal achievement (PA). Higher scores for EE and DP, and lower scores for PA suggest a higher degree of burnout.
MBI-HSS Profiles
The MBI-HSS allows for categorization of participants into 1 of 5 profiles: the “Engaged” profile is on the positive end of the spectrum, while “Burnout” is the extremely negative profile, and “Ineffective,” “Overextended,” and “Disengaged” profiles make up for the middle with defined characteristics. The distinction between these profiles relies on the scores of 3 subscales, applying critical boundaries based on normative data.20 For the current analysis, the critical boundaries were based on a normative sample of 1104 participants from the field of medicine, as found in the fourth edition of the user’s manual. The profile criteria are defined in Supplementary Table 1. An engaged profile is defined by low scores on emotional exhaustion and depersonalization, and a high personal achievement score, while the burnout profile is characterized by opposite scores. An ineffective profile is defined by a low score on personal achievement only, and an overextended profile is mainly characterized by an isolated high emotional exhaustion level, whereas a disengaged profile is defined by an isolated high score on depersonalization. Burnout thematic analyses were conducted using MAXQDA software (Software V. MAXQDA 2018 [computer software]. Berlin, Germany: VERBI Software. Available from https://www.maxqda.com. 2017.)
Statistical Analysis
All surveys received from March 12 to April 1, 2019 were included in the analysis. All analyses were performed using IBM SPSS Statistics, Version 23 (IBM Corp. Released 2015. IBM SPSS Statistics for Windows, Version 23.0. Armonk, NY: IBM Corp). Descriptive statistics were used to characterize participants in terms of demographic characteristics, professional characteristics, career satisfaction, and current stress levels. Standard descriptive statistics were used to calculate and summarize MBI-HSS scale scores (EE, DP, and PA). Mean differences in MBI-HSS scores were evaluated with one-sample t-tests, independent-samples t-tests, and one-way ANOVAs with adjustment for multiple comparisons using Bonferroni method where appropriate.
Following the scoring provided in the fourth edition of the MBI-HSS manual, we calculated the percentage of participants exhibiting high burnout. Associations with high burnout among variables of interest were evaluated through chi-square tests for categorical variables and t-tests for continuous variables in univariate analyses. Then, to determine which combination of factors increased the risk of high burnout, all variables with P < .10 in the univariate analysis were fitted into a multiple logistic regression model for high burnout with backward selection with a significance level at P < .05.
Results
Personal and Professional Characteristics
One hundred eighty-three participants completed the survey. One hundred seventy-three (95%) self-identified as a Young Investigator (YI) and 1 participant skipped the initial logic question attesting to meeting criteria for a young investigator but completed the entire survey; this 1 participant who did not attest to meeting criteria for a YI was excluded from the final analysis. For 9 SNO members, the survey ended after their response to the first logic question, indicating they did not meet criteria as a YI.
Seventy four percent of participants were physicians, most of whom were neuro-oncology and medical oncology attending physicians (Table 1). The cohort also included 39 basic scientists and 2 advanced practicing providers. Fifty-two percent were females, and 92% were younger than 45 years and 82% were married or in a long-term relationship; 62% had children. Most of the participants were practicing in North America at the time of the survey, 13% practiced in Europe, while only 4% were practicing in South America, the Middle East, and Africa; the vast majority were practicing full-time in academic settings. About a third of participants spent no greater than 5 h per week on administrative work (ie, not related to patient care or research), while 57% spent up to 30 h per week on such tasks. Most of the participants were involved in research tasks, and 81% spent up to 40 h per week on research.
Table 1.
Demographic and Professional Characteristics of Young Investigator Participants
| Questions (number of answers) | Response | N (%) | Physicians N = 90 (%) |
Basic Scientists N = 32 (%) |
|---|---|---|---|---|
| Gender (n = 166) | Female | 85 (51.2) | 51 (56.7) | 12 (38) |
| Male | 81 (48.8) | 39 (43.3) | 20 (62) | |
| Age (n = 167) | 18–24 | 2 (1.2) | 1 (1.1) | 1 (3) |
| 25–34 | 48 (28.5) | 22 (24.4) | 13 (42) | |
| 35–44 | 104 (62.3) | 63 (70) | 13 (42) | |
| 45–54 | 11 (6.6) | 3 (3.3) | 4 (13) | |
| 55 and over | 1 (0.6) | 0 (0) | 0 | |
| Prefer not to say | 1 (0.6) | 1 (1.1) | 0 | |
| Marital status (n = 167) | Married | 118 (70.7) | 68 (75.6) | 22 (69) |
| Long-term relationship | 19 (11.4) | 8 (8.9) | 4 (13) | |
| Single | 27 (16.2) | 13 (14.4) | 5 (16) | |
| Widowed | 0 | 0 (0) | 0 (0) | |
| Divorced | 3 (1.8) | 1 (1.1) | 1 (3) | |
| Are there children in the household? (n = 166) | No | 63 (38) | 31 (34.4) | 14 (44) |
| Yes | 103 (62) | 59 (65.6) | 18 (56) | |
| Region of practice (n = 166) | Asia/Pacific | 15 (9) | 9 (10) | 1 (3) |
| Africa | 2 (1.2) | 1 (1.1) | 1 (3) | |
| Europe | 22 (13.3) | 12 (13.3) | 5 (16) | |
| Central America/ South America/Caribbean | 4 (2.4) | 3 (3.3) | 1 (3) | |
| North America | 121 (72.9) | 63 (70) | 24 (75) | |
| Middle East | 2 (1.2) | 1 (1.1) | 0 (0) | |
| Work setting (n = 161) | Academic | 149 (92.6) | 83 (92.2) | 31 (97) |
| Private | 10 (6.2) | 7 (7.8) | 1 (3) | |
| Industry | 2 (1.2) | 0 (0) | 0 (0) | |
| Work status (n = 161) | Full-time | 158 (98.1) | 88 (97.8) | 32 (100) |
| Part-time | 3 (1.9) | 2 (2.2) | 0 (0) | |
| Time on administrative tasks* (n = 161) | 0 | 3 (1.9) | 0 (0) | 1 (3) |
| 1–5 h | 54 (33.6) | 32 (35.6) | 7 (22) | |
| 6–10 h | 46 (28.6) | 23 (25.6) | 13 (41) | |
| 11–20 h | 36 (22.4) | 23 (25.6) | 6 (19) | |
| 21–30 h | 10 (6.2) | 8 (8.9) | 1 (3) | |
| 31–40 h | 4 (2.5) | 1 (1.1) | 2 (6) | |
| 41–50 h | 5 (3.1) | 2 (2.2) | 2 (6) | |
| 51–60 h | 0 (0) | 0 (0) | 0 (0) | |
| > 60 h | 3 (1.9) | 1 (1.1) | 0 (0) | |
| Time on research tasks* (n = 159) | 0 | 3 (1.9) | 3 (3.3) | 0 (0) |
| 1–5 h | 29 (18.2) | 24 (26.7) | 0 (0) | |
| 6–10 h | 22 (13.8) | 16 (17.8) | 2 (6) | |
| 11–20 h | 29 (18.2) | 20 (22.2) | 3 (9) | |
| 21–30 h | 22 (13.8) | 11 (12.2) | 7 (22) | |
| 31–40 h | 23 (14.5) | 6 (6.7) | 9 (28) | |
| 41–50 h | 15 (9.4) | 6 (6.7) | 6 (19) | |
| 51–60 h | 8 (5) | 2 (2.2) | 1 (3) | |
| > 60 h | 8 (5) | 1 (1.1) | 4 (13) | |
| Time working at home* (n = 161) | 0–5 h | 55 (36.1) | 35 (38.9) | 12 (37) |
| 6–10 h | 49 (30.4) | 19 (21.1) | 15 (47) | |
| 11–20 h | 42 (26.1) | 30 (33.3) | 4 (13) | |
| 21–30 h | 5 (3.1) | 1 (1.1) | 1 (3) | |
| 31–40 h | 4 (2.5) | 3 (3.3) | 0 (0) | |
| 41–50 h | 1 (0.6) | 1 (1.1) | 0 (0) | |
| 51–60 h | 1 (0.6) | 0 (0) | 0 (0) | |
| > 60 h | 1 (0.6) | 1 (1.1) | 0 (0) | |
| Weeks of vacation in the past year (n = 161) | ≤2 | 80 (49.7) | 37 (41.1) | 19 (59) |
| 3–4 | 63 (39.1) | 42 (46.7) | 10 (32) | |
| 5–6 | 16 (9.9) | 10 (11.1) | 3 (9) | |
| ≥7 | 2 (1.2) | 1 (1.1) | 0 (0) | |
| Exercise* (n = 158) | < 30 min | 36 (22.8) | 20 (22) | 8 (25) |
| 30 min—2 h | 58 (36.7) | 35 (38.9) | 7 (22) | |
| 3–5 h | 42 (26.6) | 24 (26.7) | 11 (34) | |
| 6–10 h | 9 (5.7) | 3 (3.3) | 3 (9) | |
| > 10 h | 2 (1.3) | 0 (0) | 2 (6) | |
| No exercise | 11 (7) | 8 (8.9) | 1 (3) | |
| Quality time spent with family/friends* (n = 158) | Rarely | 15 (9.5) | 8 (8.9) | 2 (6) |
| Sometimes | 77 (48.7) | 45 (50) | 14 (44) | |
| Regularly | 64 (40.5) | 36 (40) | 15 (47) | |
| Never | 2 (1.3) | 1 (1.1) | 1 (3) | |
| Time spent in prayer/meditation/relaxation* (n = 158) | Rarely | 67 (42.4) | 37 (41.1) | 13 (41%) |
| Sometimes | 37 (23.4) | 22 (24.4) | 9 (28) | |
| Most days | 10 (6.3) | 7 (7.8) | 2 (6) | |
| Daily | 6 (3.8) | 4 (4.4) | 0 (0) | |
| Never | 38 (24.1) | 20 (22.2) | 8 (25) | |
| Alcohol use* (n = 158) | None | 43 (27.2) | ||
| 1–7 drinks | 89 (56.3) | |||
| 8–14 drinks | 22 (13.9) | |||
| ≥ 15 drinks | 4 (2.5) | |||
| Illicit drugs (n = 158) | Yes | 4 (2.5) | ||
| No | 154 (97.5) | |||
| Time on household activities other than childcare* (n = 157) | None of the time | 6 (3.8) | 6 (6.7) | 0 (0) |
| 1–3 h | 57 (36.3) | 36 (40) | 8 (25) | |
| 4–10 h | 80 (51) | 37 (41.1) | 22 (69) | |
| 11–20 h | 11 (7) | 8 (8.9) | 1 (3) | |
| > 20 h | 3 (1.9) | 2 (2.2) | 1 (3) | |
| Care for elderly or sick relative at home* (n = 158) | None | 137 (86.7) | 8 (8.9) | 3 (9) |
| 1–3 h | 12 (7.6) | 3 (3.3) | 1 (3) | |
| 4–10 h | 7 (4.4) | 1 (1.1) | 0 (0) | |
| 11–20 h | 2 (1.3) | 0 (0) | 0 (0) | |
| >20 h | 0 | 78 (86.7) | 28 (88) | |
| Time spent on hobbies* (n = 158) | 0 | 53 (33.5) | 33 (36.7) | 12 (38) |
| 1–3 h | 72 (45.6) | 42 (46.7) | 11 (34) | |
| 4–10 h | 30 (19) | 14 (15.6) | 8 (25) | |
| 11–20 h | 3 (1.9) | 1 (1.1) | 1 (3) | |
| > 20 h | 0 | 0 (0) | 0 (0) | |
| Hours of sleep per night (n = 153) | ≤ 4 h | 3 (1.9) | 2 (2.2) | 1 (3) |
| 5–7 h | 130 (85) | 78 (86.7) | 27 (85) | |
| ≥ 8 h | 20 (13.1) | 10 (11.1) | 4 (13) | |
| Current stress levelǂ (n = 153) | ≤ 3 | 15 (9.8) | ||
| 4–7 | 89 (58.2) | |||
| ≥ 8 | 49 (32) | |||
| Median | 6.4 | 7 | 7 | |
| Job satisfaction levelǂ (n = 153) | ≤ 3 | 25 (16.3) | ||
| 4–7 | 75 (49) | |||
| ≥ 8 | 53 (34.6) | |||
| Median | 6.2 | 7 | 6 | |
| Presence of institutional mechanism to prevent/support BO (n = 150) | Yes | 46 (30.7) | 28 (31.1) | 11 (34) |
| No | 64 (42.7) | 39 (43.3) | 12 (38) | |
| I don’t know | 40 (26.7) | 23 (25.6) | 9 (28) | |
| Adequate income (n = 161) | Yes | 57 (35.4) | 33 (37) | 7 (22) |
| No | 104 (64.4) | 57 (63) | 25 (78) | |
| Currently experiencing burnout (n = 153) | Yes | 70 (45.8) | 31 (34) | 14 (44) |
| No | 53 (34.6) | 44 (49) | 11 (34) | |
| I don’t know | 30 (19.6) | 15 (17) | 7 (22) | |
| Current burnout level (n = 53) | High | 18 (34) | 12 (39) | 5 (36) |
| Moderate | 29 (54.7) | 14 (45) | 9 (64) | |
| Low | 6 (11.3) | 5 (16) | 0 (0) | |
| Previous burnout (n = 152) | Yes | 80 (52.6) | 50 (56) | 8 (25) |
| No | 47 (30.9) | 28 (31) | 15 (47) | |
| I don’t know | 25 (16.5) | 12 (13) | 9 (28) | |
| Timeline of previous burnout (n = 79) | < 2 years ago | 41 (51.9) | 26 (52) | 7 (47) |
| > 2 years ago | 38 (48.1) | 24 (48) | 8 (53) | |
| History of depression (n = 150) | Yes | 26 (17.3) | 16 (18) | 7 (22) |
| No | 110 (73.3) | 66 (73) | 20 (63) | |
| I don’t know | 6 (4) | 2 (2) | 3 (9) | |
| Prefer not to answer | 8 (5.3) | 6 (7) | 2 (6) | |
| History of anxiety (n = 150) | Yes | 36 (24) | 18 (20) | 13 (41) |
| No | 101 (67.3) | 67 (74) | 14 (44) | |
| I don’t know | 8 (5.3) | 1 (1) | 5 (16) | |
| Prefer not to answer | 5 (3.3) | 4 (4) | 0 (0) | |
| Profession (n = 150) | Physician | 101 (67.3) | ||
| Basic scientist | 39 (26) | |||
| Other | 8 (5.3) | |||
| Advanced practicing provider | 2 (1.3) | |||
| Neurosurgeon (n = 101) | Yes | 22 (21.8) | ||
| No | 79 (78.2) | |||
| Position (n = 78) | In training | 16 (20.5) | ||
| Attending/Consultant | 62 (79.5) | |||
| Specialty (n = 74) | Neuro/Medical Oncology | 50 (67.6) | ||
| Neurology | 6 (8.11) | |||
| Neurosurgery | 2 (2.7) | |||
| Radiation Oncology | 8 (10.8) | |||
| Pathology | 3 (4) | |||
| Other | 5 (6.8) |
*Based on a typical week.
ǂOn a scale from 1 to 10, 10 being the highest score.
Career Satisfaction and Lifestyle Characteristics
On a 10-point scale, with 10 being the highest score, the mean stress level reported was 6.4 (95% CI: 6.1, 6.8, median = 7), and the mean job satisfaction level was 6.2 (95% CI: 5.8, 6.6, median = 7). Seventy-eight percent of participants reported that organizational challenges were the main stressful aspect of their jobs. Such challenges were primarily related to administrative burden such as work politics and issues with colleagues, multiple demands such as workload and work pressure, and funding concerns. Thirty-seven percent attributed stress to factors impacting employee well-being, including lack of appreciation, limited career advancement opportunities, work-life balance issues, and uncertainty about the future. On the other hand, the satisfying aspects of job positions were patient care, patient, and family interactions, research involvement, a good work environment providing personal and professional growth opportunities, and the feeling of making an impact and finding satisfaction in one’s profession. Sixty-four percent felt they had an inadequate income.
Regarding lifestyle questions, lack of exercise (<30 minutes/week) was reported in 23% of participants, and only 33.6% reported exercising more than 120 min a week, and 54% reported having no more than 6 h of sleep per night. Almost 50% of participants had less than 2 weeks of vacation in the previous year, 41% reported spending time with family and friends on a regular basis, and 66% reported never or rarely praying, meditating, or using relaxation techniques. Twenty-seven percent reported never consuming alcohol in a typical week, while 56% reported having between 1 and 7 drinks per week, and 2.5% reported having more than 15 drinks per week. Only 3% reported using illicit drugs.
Finally, when asked about mood problems, 24% reported a history of anxiety, and 17% reported a history of depression/anxiety; while 5% preferred not to answer questions about a history of mood issues.
The answers about habits, lifestyle, well-being, and job satisfaction are further detailed in Table 1.
MBI-HSS
One hundred thirty-one participants completed the MBI-HSS, and the results are presented in Tables 2 and 3. One-sample t-tests demonstrated YI scored higher than the normative population in all 3 scales, particularly in emotional exhaustion and depersonalization [EE: t(130) = 3.95, P < 0.001, r = 0.33; DP: t(128) = 2.99, P = 0.003, r = 0.26; PA: t(127) = −1.94, P = 0.055, r = 0.17]. However, there were no significant differences when compared to the SNO general membership on all 3 scales (Table 2).
Table 2.
MBI-HSS Summary Scores Among Young Investigators
| Emotional Exhaustion | Depersonalization | Personal Achievement | |
|---|---|---|---|
| Number of answers N (%) Mean (SD) Median Range |
131 (75) 26.4 (12.1) 25 3–54 |
129 (74) 8.8 (6.5) 9 0–30 |
128 (74) 35 (9.1) 37 5–48 |
| Possible score range Normative group mean SNO general membership |
0–54 22.2* 25.3 |
0–30 7.1* 8.1 |
0–48 36.5 36.5 |
* P < .05.
Table 3.
Burnout Profile Characteristics in Young Investigators
| Engaged | Ineffective | Overextended | Burnout | ||
|---|---|---|---|---|---|
| Gender | Male | 18 (47) | 16 (48) | 16 (41) | 10 (50) |
| Female | 20 (53) | 17 (52) | 23 (59) | 10 (50) | |
| Agea | 0–25 | 0 (0) | 2 (6) | 0 (0) | 0 (0) |
| 25–44 | 36 (95) | 27 (85) | 36 (92) | 19 (95) | |
| 45+ | 0 (0) | 3 (9) | 1 (5) | 1 (5) | |
| Marital status | Married/long-term relationship | 34 (89) | 23 (70) | 35 (89) | 16 (80) |
| Single | 4 (11) | 8 (24) | 4 (11) | 4 (20) | |
| Widowed/divorced | 0 (0) | 2 (6) | 0 (0) | 0 (0) | |
| Children in household | No | 12 (32) | 14 (42) | 17 (44) | 8 (40) |
| Yes | 26 (68) | 19 (58) | 22 (56) | 12 (60) | |
| Profession | Physician | 31 (82) | 20 (61) | 26 (67) | 13 (65) |
| Advanced practice provider | 0 (0) | 1 (3) | 0 (0) | 1 (5) | |
| Basic scientist | 5 (13) | 10 (30) | 11 (28) | 5 (25) | |
| Other | 2 (5) | 2 (6) | 2 (5) | 1 (5) | |
| Region of practice | Asia/Pacific | 2 (5) | 2 (6) | 3 (8) | 3 (15) |
| Africa | 1 (3) | 1 (3) | 0(0) | 0 (0) | |
| Europe | 3 (8) | 9 (27) | 4 (11) | 1 (5) | |
| Center America/South America/ Caribbean | 1 (3) | 0 (0) | 0 (0) | 0 (0) | |
| North America | 31 (82) | 19 (58) | 30 (79) | 15 (75) | |
| Middle East | 0 (0) | 1 (3) | 0 (0) | 0 (0) | |
| Other | 0 (0) | 1 (3) | 1 (3) | 1 (5) | |
| Practice setting | Academic | 37 (97) | 32 (97) | 35 (90) | 18 (90) |
| Private | 1 (3) | 1 (3) | 4 (10) | 2 (10) | |
| Work status |
Full-time | 38 (100) | 32 (97) | 38 (97) | 20 (100) |
| Part-time | 0 (0) | 1 (3) | 1 (3) | 0 (0) | |
| Time on administrative tasksb | 0–5 h | 12 (32) | 12 (36) | 14 (36) | 7 (35) |
| 6–10 h | 15 (39) | 10 (30) | 9 (23) | 3 (15) | |
| 11–20 h | 10 (26) | 8 (24) | 7 (18) | 6 (30) | |
| 21–30 h | 1 (3) | 2 (6) | 4 (10) | 2 (10) | |
| 30 + h | 0 (0) | 1 (3) | 5 (13) | 2 (10) | |
| Time on research tasksb | 0–5 h | 7 (22) | 4 (13) | 8 (21) | 8 (40) |
| 6–10 h | 6 (16) | 5 (16) | 5 (13) | 2 (10) | |
| 11–20 h | 9 (24) | 4 (13) | 6 (15) | 3 (15) | |
| 21–30 h | 5 (14) | 5 (16) | 6 (15) | 3 (15) | |
| 30 + h | 9 (25) | 14 (44) | 14 (36) | 4 (20) | |
| Time working at homeb | 0–5 h | 11 (29) | 13 (39) | 18 (47) | 9 (45) |
| 6–10 h | 11 (29) | 9 (27) | 12 (31) | 4 (20) | |
| 11–20 h | 12 (32) | 8 (24) | 9 (23) | 6 (30) | |
| 21–30 h | 2 (5) | 1 (3) | 0 (0) | 0 (0) | |
| 30 + h | 2 (5) | 2 (6) | 0 (0) | 1 (5) | |
| Adequate income | No | 17 (45) | 23 (70) | 30 (77) | 17 (85) |
| Yes | 21 (55) | 10 (30) | 9 (23) | 3 (15) | |
| Institutional mechanism | No | 7 (18) | 16 (48) | 21 (54) | 11 (55) |
| Yes | 15 (39) | 12 (36) | 8 (21) | 7 (35) | |
| I don’t know | 16 (42) | 5 (15) | 10 (26) | 2 (10) | |
| Job satisfaction | Median score | 8 | 7 | 5 | 5 |
| Weeks of vacationc | 2 or less | 18 (48) | 12 (36) | 21 (54) | 11 (55) |
| 3–4 | 17 (44) | 16 (47) | 14 (36) | 6 (30) | |
| 5 or more | 3 (9) | 5 (15) | 4 (11) | 3 (15) | |
| Exerciseb | <30 min | 8 (21) | 10 (30) | 16 (41) | 4 (20) |
| 30 min–2 h | 16 (42) | 11 (33) | 12 (31) | 7 (35) | |
| 3–5 h | 10 (26) | 10 (30) | 10 (26) | 7 (35) | |
| > 5 h | 4 (11) | 2 (6) | 1 (3) | 2 (10) | |
| Time spent with family and friendsb | Never/rarely | 0 (0) | 5 (15) | 4 (10) | 5 (25) |
| Sometimes | 17 (45) | 13 (39) | 24 (62) | 7 (35) | |
| Regularly | 21 (55) | 15 (45) | 11 (28) | 8 (40) | |
| Time spent in prayer/ meditation/relaxation |
Never/rarely | 16 (42) | 22 (66) | 31 (79) | 15 (75) |
| Sometimes | 15 (39) | 8 (24) | 7 (18) | 2 (10) | |
| Most days/daily | 7 (19) | 3 (9) | 1 (3) | 3 (15) | |
| Alcohol useb | Moderate drinker | 36 (95) | 33 (100) | 34 (87) | 17 (85) |
| Heavy drinker | 2 (5) | 0 (0) | 5 (13) | 3 (15) | |
| Illicit drug use | No | 38 (100) | 33 (100) | 38 (97) | 18 (90) |
| Yes | 0 (0) | 0 (0) | 1 (3) | 2 (10) | |
| Time on household activitiesb | 0–3 h | 18 (48) | 16 (50) | 11 (29) | 7 (35) |
| 4–10 h | 17 (45) | 13 (41) | 23 (59) | 11 (55) | |
| 10 + h | 3 (8) | 3 (9) | 5 (13) | 2 (10) | |
| Care for elderly at home | No | 33 (87) | 28 (85) | 35 (89) | 17 (85) |
| Yes | 5 (13) | 5 (15) | 4 (11) | 3 (15) | |
| Time spent on hobbiesb | None | 15 (39) | 7 (21) | 19 (49) | 4 (20) |
| 1–3 h | 13 (34) | 18 (55) | 17 (44) | 8 (40) | |
| 3 + h | 10 (27) | 8 (24) | 3 (8) | 8 (40) | |
| Hours of sleepd | <7 | 22 (58) | 15 (45) | 23 (59) | 10 (50) |
| 7 or more | 16 (42) | 18 (55) | 16 (41) | 10 (50) | |
| History of depression | No | 31 (82) | 28 (85) | 24 (62) | 9 (45) |
| Yes | 7 (18) | 4 (12) | 9 (23) | 4 (20) | |
| I don’t know/prefer not to answer | 0 (0) | 1 (3) | 6 (15) | 7 (35) | |
| History of anxiety | No | 33 (87) | 23 (70) | 21 (54) | 8 (40) |
| Yes | 4 (11) | 7 (21) | 13 (33) | 8 (40) | |
| I don’t know/prefer not to answer | 1 (3) | 3 (9) | 5 (13) | 4 (20) | |
| Current stress level | Median score | 7 | 7 | 8 | 8 |
| Experiencing current burnout | No | 31 (82) | 21 (64) | 4 (10) | 0 (0) |
| Yes | 6 (16) | 4 (12) | 24 (62) | 17 (85) | |
| I don’t know | 1 (3) | 8 (24) | 11 (28) | 3 (15) | |
| History of burnout prior to the survey | No | 14 (37) | 13 (39) | 9 (23) | 2 (10) |
| Yes | 17 (45) | 16 (48) | 21 (54) | 15 (75) | |
| I don’t know | 7 (18) | 4 (12) | 9 (23) | 3 (15) |
Twenty-nine percent were identified as Engaged in their work, with no burnout in any of the 3 areas. Young investigators with burnout were mostly identified as feeling overextended (29.8%) in their work, while 16.1% were classified in the burnout profile, which includes high levels of emotional exhaustion, depersonalization, and low personal achievement (Figure 1).
Figure 1.
Distribution of burnout profiles among Young Investigators (YI). Outer circle: general YI population. Middle circle: YI basic scientists. Inner circle: YI physicians.
We observed gender, age, and family characteristics differences in the profile results. The Overextended profile included more females in the whole cohort, and more female physicians, but more male basic scientists. The ineffective profile, defined by a low personal achievement, included more participants younger than 34 years than the other profiles. More participants with children in their household were in the engaged profile, characterized by low emotional exhaustion and depersonalization and high personal achievement (Table 3).
Regarding the job settings, the engaged profile was the most prevalent among general participants and physicians who reported spending less than 10 h per week on administrative work, whereas 50% of participants who spent greater than 10 h belonged to the burnout profile. The average number of hours per week dedicated to research was heterogenous among profiles. Most participants, other than physicians who spent more than 30 h per week on research, belonged to the ineffective and overextended profiles, and none of the basic scientists who spent more than 40 h per week on research belonged to the engaged profile. Sixty-two percent of the general participants who spent greater than 10 h of their total weekly hours working from home belonged to the more favorable profiles (ie, engaged and ineffective), and only 16% belonged to the burnout profile. However, 2 out of the 5 basic scientists who reported completing work-related tasks at home for greater than 10 h per week belonged to the burnout profile. Sixty-seven percent of the general participants, and only 45% of those of the engaged profile, did not consider their income adequate for the time and effort they devoted to their profession, while this was reported in 78% of basic scientists. Twenty-six percent of the participants did not know whether their institution provides a mechanism to prevent or support burnout, and only 32% reported such mechanisms, among whom 64% belonged to the more favorable profiles (ie, engaged and ineffective). In summary, the engaged profile included a higher percentage of individuals who have children, spend less time on administrative work and research, consider their income adequate, and have institutional mechanisms to prevent or support burnout, as well as physicians who spent greater time completing work-related tasks at home. A high burden of administrative work, and greater remote work from home in basic scientists was observed in the burnout profile. The incidence of burnout among physicians was 12.7% in North America, 8.3% in Europe, and 33.3% in Asia. Twenty-nine percent of physicians with private practice, and 13% of those with academic practice belonged in the burnout profile. Basic scientists were practicing mainly in North America (75%) and Europe (16%). Twenty-one percent of those practicing in North America belonged in the burnout profile and they all worked in academic settings.
In the questionnaire section addressing lifestyle practices, the burnout profile included more participants who reported having taken less than 2 weeks of vacation in the previous year, and the lowest percentage of participants who reported spending quality time with family and friends on a regular basis and those spending more than 3 h per week on household chores. It included the least proportion of participants who reported sleeping more than 6 h per night. Half of participants belonging to the overextended profile and 39% of the engaged participants did not spend any time on their hobbies, compared to only 20% of the participants with ineffective and burnout profiles. Seventy-five percent of the burnout participants and 80% of the overextended participants rarely or never participated in praying, meditation, or relaxation techniques, compared to only 42% in the engaged profile. Also, the burnout and overextended profiles included higher proportions of heavy drinkers compared to the ineffective and engaged profiles.
On a 10-point scale, the reported mean current stress level was 5.9 (95% CI: 5.3, 6.5, median = 7) in the engaged profile, 6.1 (95% CI: 5.4, 6.7, median = 7) in the Ineffective profile 7.5 (95% CI: 7.0, 7.9, median = 8) in the overextended profile, and 7.2 (95% CI: 6.3, 8.1, median = 8) in the burnout profile. Anxiety and depression were more prevalent in the latter 2 profiles. Organizational challenges were the most stressful aspect of the job in all profiles and consisted of administrative burden, increased workload, and funding concerns. Feelings of lack of appreciation at work, limited career advancement opportunities, and uncertainty about the future were also common in the overextended and burnout profiles.
Finally, participants tended to underreport burnout on a self-report question “are you currently experiencing burnout” as compared to use of the MBI-HSS, and 18% of participants did not know whether they had been experiencing burnout or had experienced it at the time of or prior to the survey. Thirty-nine percent of all participants reported burnout at the time of the survey, among whom only 11.8% belonged to the Engaged profile. Sixty-nine (53%) participants reported a past experience of burnout and were equally distributed among the profiles. Of the 20 participants who belonged in the burnout profile, 85% reported experiencing burnout at the time of the survey, and 75% reported experiencing burnout in the past.
No personal or professional characteristics were found to be significantly associated with high burnout among participants on univariate analysis and multiple logistic regression models.
Comparison to General SNO Membership
When the SNO membership survey was conducted in 2016, the fourth edition of MBI-HSS had not been published yet and the third edition was used, where a “high burnout” profile corresponded to the ineffective, overextended, disengaged, and burnout profiles from the fourth edition.21 In that general SNO survey, 75% of the participants were physicians, 9% were basic scientists, and 16% were allied health professionals.12 There were no remarkable differences between the general SNO membership survey and YI survey regarding the gender, marital status, and prevalence of children in the household. Fifty-two percent of the respondents in the general SNO membership survey fell in the 25–44 year-old age group. Young age did not appear to be a risk factor associated with high burnout in the general SNO membership, whereas in the YI SNO survey, the prevalence of high burnout was 70% in participants younger than 44 years, and 22% in older participants. However, not surprisingly, only 8% of the YI survey population were older than 44.
There was a similar distribution of the number of hours spent on administrative work and the number of hours of work at home, and the associated high burnout prevalence was also similar between the 2 groups. The prevalence of high burnout was higher in the YI group (77% vs 66%). However, this difference did not reach statistical significance. Among the general SNO members who did not have the burnout profile, the mean score of job satisfaction was 7.4, mildly lower than the YI cohort. Among participants with a high burnout profile, the mean job satisfaction score was the same in both groups. The prevalence of high burnout was lower in members who felt they had an adequate income compared to those who did not in both groups.
The general SNO cohort had a similar distribution to the YI cohort regarding the time spent on exercise and on prayer/meditation/relaxation techniques, and the amount of quality time spent with family and friends. Participants who spent less than 30 min per week on exercise had a higher prevalence of high burnout in both cohorts (79% in SNO), as did those who never/rarely spent quality time with their friends and family compared to spending such time on a regular basis (68% vs 51% in SNO). The prevalence of high burnout in participants who never or rarely participated in prayer/meditation/relaxation techniques was higher in YI than the general SNO members (84% and 80%, vs 63% and 62%), whereas the prevalence was lower in YI members who participated in such more often compared to the general SNO members (48% vs 65%). The distribution and high burnout prevalence were similar between the 2 groups in regards to drinking habits, time dedicated to household chores, taking care of elderly or sick relatives, and hobbies, and hours of night-time sleep.
The prevalence of high burnout was higher in YI members who reported anxiety, similar to the SNO members, whereas the significantly high prevalence among general SNO members who reported depression was not appreciated in the YI cohort. The means of current stress in high burnout participants were similar between the 2 groups.
Finally, the prevalence of high burnout was higher in participants of both cohorts who self-reported burnout compared to those who did not.
Discussion
We are reporting the results of a survey conducted on SNO members who identify as young investigators with the aim to evaluate the prevalence of burnout in such a population. As expected, and similar to the SNO general membership, YI had higher mean scores on emotional exhaustion and depersonalization than the general population. Ninety-two percent of the participants were younger than 45 years, which is expected in the YI definition. Only 2 participants were advanced practice providers, hence further evaluation and characterization of this subgroup could not be performed.
This study used the fourth edition of MBI-HSS, which categorizes participants into 5 burnout profiles, rather than classifying them as belonging or not to the “high burnout” profile in the third edition used in previous studies, including the previously published general SNO membership survey. The “high burnout” profile includes all but the engaged profiles. We discovered that 71% of the YI had at least one symptom of burnout. This rate is higher than the 45% reported in an ASCO survey of US oncologists in 2014,15 and the 61% reported in the general SNO membership survey,12 but is similar to the high burnout rate reported among young oncologists in 2017.14 This high rate was coupled to an above-average median stress level. As in other studies, work-related stress was mainly attributed to organizational challenges. However, the median job satisfaction level was also above average, especially among physicians. The rate of participants identified in the engaged profile was higher among physicians than basic scientists, consistent with the SNO membership results. However, it should be noted that the number of basic scientists participating in our survey was small.
No specific burnout risk factors were identified in univariate analysis. However, participants belonging to the burnout profile reported longer time spent on administrative work, shorter vacation time, less night-time sleep, and heavy drinking. They also spent less quality time with family and friends, as well as on household work, on prayer, meditation, or relaxation. However, it is not clear whether burnout was a consequence of or a cause for such lifestyle patterns. From our analysis, if organizations want to make the greatest impact in reducing the risk of burnout in the specialized field of neuro-oncology, and seek to maintain a high-performing workforce, reducing administrative burden is the place to focus. We note that respondents find their patient-centered work meaningful, but the administrative work is what contributes significantly to burnout.
Similar to the results of the EANO and SNO survey, many participants did not have insight into their own burnout. This might be explained by an impaired emotional self-awareness, and/or lack of familiarity with the symptoms of burnout. Only 32% of participants reported the presence of an institutional mechanism to prevent or address burnout, while the rest reported the absence or unawareness of such a mechanism, which may also contribute to the lack of familiarity with burnout features. The lower prevalence of burnout among participants who reported the presence of institutional support mechanisms highlights the importance of establishing such mechanisms in work environments and spreading awareness against burnout.
Our study is subject to several limitations. There might be a response bias given that the results relied on the self-reporting of the participants, especially for the questions related to anxiety and depression. However, it is difficult to objectively evaluate lifestyle and work patterns of participants. Furthermore, the survey was anonymous and was sent via email, and the participants were given enough time to complete it, limiting any potential embarrassment or rush while answering the questions. Another limitation is that the total number of YI in the SNO membership is unknown, making it difficult to evaluate whether the survey reached everyone of interest and whether our sample was representative of the whole cohort. Furthermore, the survey responses do not represent a longitudinal evaluation of participants’ well-being, but rather their well-being and practices at a specific timepoint. Also, this survey was conducted before the COVID-19 pandemic. An international survey completed in 2021 outlined several problems practitioners and researchers had encountered during the pandemic, including changes in clinical practice, reduced financial reimbursement, increased working hours, increased stress and fear for own health and that of relatives, and interruption of clinical, basic, and translational research.22 Although this survey was not limited to YI, one can assume that the burden of the pandemic might have been more impactful in such a population and that the incidence of burnout in YI might have increased during the pandemic.23,24
On the other hand, our study has several strengths. The survey was based on the one sent to the general SNO members and used the latest version of MBI-HSS, allowing comparisons between the general SNO members and YI. This study also highlights a special population of professionals who are involved in the care of patients with guarded prognoses in the beginning of their careers.
The high prevalence of burnout observed in our survey raises the importance of implementing interventions to address burnout in neuro-oncology practitioners, early in their careers. The potential negative consequences of burnout on the mental and physical health and the professional life of healthcare personnel have increased international awareness and attention to the issue. Many strategies have been associated with reduced anxiety and burnout among healthcare professionals, by focusing on mindfulness, stress management, communication skill training, and participation in small group programs that promote community, connectedness, and meaning.25–28 For its part, SNO launched SNOCares, an initiative of the SNO Wellness Committee, in 2017 after the results of the Neuro-Oncology Burnout and Career Satisfaction Survey, to address burnout in SNO members. The Wellness Committee has organized various activities to provide ways of managing stress during the annual meeting, such as yoga sessions, chair massages, complimentary stress balls, and tip cards containing daily routine relaxation techniques.29 The results of the current survey have also led to the development of the Fellow and Early Clinician/Investigator Career Retreats that started in 2019 and have been a regular part of the annual SNO meeting. These retreats are focused on educating YI about job opportunities, job search and negotiation, applying for grants, dealing with finances, getting involved in the local and national communities, and enhancing their careers. On institutional levels, implementing repeated dialog between YI and leadership is essential, and YI should be closely mentored and evaluated regularly for burnout to help manage it at its early stages. They should be provided with resources to promote resilience, self-care, and work-life balance.30 Administrative burdens should be reduced, and YI should be offered a protected time for aspects of work they find meaningful, such as research, education, or quality improvement projects.27,30 Finally, there is a need to establish policies to mitigate and prevent burnout, especially in YI who already face multiple challenges in their early career. This is ensured by selecting the right leaders who can recognize the needs of their peers and mentees.
Supplementary Material
Contributor Information
Gilbert Youssef, Center for Neuro-Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts, USA.
Alvina Acquaye-Mallory, Neuro-Oncology Branch, Center for Cancer Research, The National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA.
Elizabeth Vera, Neuro-Oncology Branch, Center for Cancer Research, The National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA.
Milan G Chheda, Division of Oncology, Department of Medicine, Washington University School of Medicine, Saint Louis, Missouri, USA.
Gavin P Dunn, Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA.
Jennifer Moliterno, Yale School of Medicine, Chenevert Family Brain Tumor Center, Yale Cancer Center and Smilow Cancer Hospital, New Haven, Connecticut, USA.
Barbara J O’Brien, Department of Neuro-Oncology, University of Texas MD Anderson Cancer Center, Houston, Texas, USA.
Monica Venere, Department of Radiation Oncology, James Cancer Hospital and Comprehensive Cancer Center, The Ohio State University College of Medicine, Columbus, Ohio, USA; Tzagournis Medical Research Facility, Columbus, Ohio, USA.
Shlomit Yust-Katz, Neuro-Oncology Unit, Davidoff Cancer Center at Rabin Medical Center and Faculty of Medicine, Tel-Aviv University, Tel-Aviv, Israel.
Eudocia Q Lee, Center for Neuro-Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts, USA.
Terri S Armstrong, Neuro-Oncology Branch, Center for Cancer Research, The National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA.
Acknowledgments
We would like to thank the Society of Neuro-Oncology, including Chas Haynes, for their support with this work.
Funding
No external funding supported this work.
Conflict of interest statement
The authors do not have any conflicts of interest to declare.
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