Factors associated with self-reported burnout level in allied healthcare professionals in a tertiary hospital in Singapore

Yao Hao Teo; Jordan Thet Ke Xu; Cowan Ho; Jui Min Leong; Benjamin Kye Jyn Tan; Elisabeth Ker Hsuen Tan; Wei-An Goh; Elson Neo; Jonathan Yu Jing Chua; Sean Jun Yi Ng; Julia Jie Yi Cheong; Jeff Yi-Fu Hwang; See Ming Lim; Thomas Soo; Judy Gek Khim Sng; Siyan Yi

doi:10.1371/journal.pone.0244338

. 2021 Jan 6;16(1):e0244338. doi: 10.1371/journal.pone.0244338

Factors associated with self-reported burnout level in allied healthcare professionals in a tertiary hospital in Singapore

Yao Hao Teo ¹, Jordan Thet Ke Xu ¹, Cowan Ho ¹, Jui Min Leong ², Benjamin Kye Jyn Tan ¹, Elisabeth Ker Hsuen Tan ¹, Wei-An Goh ¹, Elson Neo ¹, Jonathan Yu Jing Chua ¹, Sean Jun Yi Ng ¹, Julia Jie Yi Cheong ¹, Jeff Yi-Fu Hwang ², See Ming Lim ³, Thomas Soo ³, Judy Gek Khim Sng ², Siyan Yi ^2,^4,^5,^6,^*

Editor: Jenny Wilkinson⁷

¹Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore

²Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Singapore

³Occupational Health Clinic, Department of Medicine, National University Hospital, National University Health System, Singapore, Singapore

⁴KHANA Center for Population Health Research, Phnom Penh, Cambodia

⁵Center for Global Health Research, Touro University California, Vallejo, CA, United States of America

⁶School of Public Health, National Institute of Public Health, Phnom Penh, Cambodia

⁷Endeavour College of Natural Health, AUSTRALIA

Competing Interests: The authors have declared that no competing interests exist.

^✉

* E-mail: ephsyi@nus.edu.sg

Roles

Yao Hao Teo: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Writing – original draft, Writing – review & editing

Jordan Thet Ke Xu: Conceptualization, Formal analysis, Funding acquisition, Investigation, Methodology, Resources, Writing – original draft, Writing – review & editing

Cowan Ho: Conceptualization, Investigation, Methodology, Project administration, Resources, Supervision, Visualization, Writing – original draft, Writing – review & editing

Jui Min Leong: Data curation, Formal analysis, Investigation, Methodology, Resources, Validation, Writing – original draft, Writing – review & editing

Benjamin Kye Jyn Tan: Conceptualization, Investigation, Methodology, Project administration, Resources, Validation, Writing – original draft, Writing – review & editing

Elisabeth Ker Hsuen Tan: Conceptualization, Investigation, Methodology, Project administration, Resources, Supervision, Writing – original draft

Wei-An Goh: Conceptualization, Investigation, Methodology, Project administration, Resources, Supervision, Validation, Writing – original draft

Elson Neo: Conceptualization, Investigation, Methodology, Project administration, Resources, Supervision, Validation, Writing – original draft

Jonathan Yu Jing Chua: Conceptualization, Investigation, Methodology, Project administration, Supervision, Validation, Writing – original draft

Sean Jun Yi Ng: Conceptualization, Investigation, Methodology, Project administration, Resources, Supervision, Validation, Writing – original draft

Julia Jie Yi Cheong: Conceptualization, Investigation, Methodology, Project administration, Resources, Writing – original draft

Jeff Yi-Fu Hwang: Conceptualization, Funding acquisition, Investigation, Methodology, Resources, Supervision, Validation, Visualization, Writing – review & editing

See Ming Lim: Conceptualization, Funding acquisition, Investigation, Methodology, Resources, Supervision, Validation, Visualization, Writing – review & editing

Thomas Soo: Conceptualization, Funding acquisition, Investigation, Methodology, Resources, Supervision, Validation, Visualization, Writing – review & editing

Judy Gek Khim Sng: Conceptualization, Funding acquisition, Investigation, Methodology, Resources, Supervision, Validation, Visualization, Writing – review & editing

Siyan Yi: Conceptualization, Investigation, Methodology, Project administration, Resources, Supervision, Validation, Visualization, Writing – review & editing

Jenny Wilkinson: Editor

PMCID: PMC7787466 PMID: 33406132

Abstract

Background

Burnout has adverse implications in healthcare settings, compromising patient care. Allied health professionals (AHPs) are defined as individuals who work collaboratively to deliver routine and essential healthcare services, excluding physicians and nurses. There is a lack of studies on burnout among AHPs in Singapore. This study explored factors associated with a self-reported burnout level and barriers to seeking psychological help among AHPs in Singapore.

Methods

We conducted a cross-sectional study in a sample of AHPs in a tertiary hospital from October to December 2019. We emailed a four-component survey to 1127 eligible participants. The survey comprised four components: (1) sociodemographic characteristics, (2) Maslach Burnout Inventory (MBI-HSS), (3) Areas of Worklife Survey, and (4) Perceived Barriers to Psychological Treatment (PBPT). We performed a multiple logistic regression analysis to identify factors associated with burnout. Adjusted odds ratios (AORs) and associated 95% confidence intervals (CIs) were computed.

Results

In total, 328 participants completed the questionnaire. The self-reported burnout level (emotional exhaustion>27 and/or depersonalization>10) was 67.4%. The majority of the respondents were female (83.9%), Singaporean (73.5%), aged 40 years and below (84.2%), and Chinese ethnicity (79.9%). In the multiple logistic regression model, high burnout level was negatively associated with being in the age groups of 31 to 40 (AOR 0.39, 95% CI 0.16–0.93) and 40 years and older (AOR 0.30, 95% CI 0.10–0.87) and a low self-reported workload (AOR 0.35, 95% CI 0.23–0.52). High burnout level was positively associated with a work experience of three to five years (AOR 5.27, 95% CI 1.44–20.93) and more than five years (AOR 4.24; 95% CI 1.16–16.79. One hundred and ninety participants completed the PBPT component. The most frequently cited barriers to seeking psychological help by participants with burnout (n = 130) were ‘negative evaluation of therapy’ and ‘time constraints.’

Conclusions

This study shows a high self-reported burnout level and identifies its associated factors among AHPs in a tertiary hospital. The findings revealed the urgency of addressing burnout in AHPs and the need for effective interventions to reduce burnout. Concurrently, proper consideration of the barriers to seeking help is warranted to improve AHPs' mental well-being.

Introduction

Burnout is a prolonged response to chronic emotional and interpersonal stressors on the job, comprising three dimensions: exhaustion, cynicism, and inefficiency [1]. These dimensions are further defined as follows: exhaustion of emotional or physical capacity due to stress, a degree of indifference or detachment from various aspects of work, and a sense of inadequacy or reduced personal accomplishment, respectively [1].

In healthcare settings, burnout negatively impacts outcomes at the individual, interpersonal, and institutional levels. At the individual level, burnout is associated with reduced job satisfaction, increased absenteeism, medical errors, sickness, injury, and accidents among healthcare providers [2, 3]. These individual-level impacts may lead to reduced care quality and higher mortality among patients [4, 5]. From an interpersonal perspective, burnout is associated with emotional dissonance due to chronic exhaustion and cynicism [6]. Emotional dissonance is described as a conflict between personal emotions and organizational demands. On an institutional level, burnout is linked to a higher turnover of healthcare workers [7, 8] and decreased workforce efficiency [9], posing a substantial economic burden on the healthcare system [10].

The pernicious nature of burnout in healthcare settings has prompted numerous studies on its prevalence in physicians and nurses in Singapore and internationally. For example, high burnout levels and their associated factors among physicians and nurses have been reported in Singapore [11, 12]. Extensive research involves the barriers to seeking help for doctors, such as fear of stigma, lack of available time, and lack of convenient access [13, 14].

Allied health professionals (AHPs) are defined as individuals who work collaboratively to deliver routine and essential healthcare services, excluding physicians and nurses [15, 16]. AHPs include, but are not limited to, occupational therapists, physiotherapists, pharmacists, medical social workers, and radiographers [17]. This system is similarly adopted in the United Kingdom [18] and the United States [19] and plays an essential role in improving hospital efficiencies and access to care [19]. In Singapore, the Allied Health Professions Council (AHPC) defines and classifies allied health occupations similar to other countries [20].

Studies in other countries have reported a high prevalence of burnout in AHPs. In the United States, physiotherapists and occupational therapists reported high rates of emotional exhaustion (58%), negative feelings about their work and their clients (94%), and an almost non-existent sense of personal accomplishment (1%) [21]. However, there are currently no studies examining burnout levels and their associated risk factors among AHPs in Singapore.

This study aims to identify the self-reported burnout levels and explore their associations with sociodemographic factors and the work environment among AHPs in Singapore. Based on the evidence from studies on doctors and nurses [11, 12, 22], we hypothesized that burnout levels among AHPs in Singapore would be similarly high, and age and work experience would be significantly associated with burnout levels. Our secondary objective is to identify significant barriers in seeking psychological help among AHPs with a high burnout level.

Materials and methods

Study design and sampling

We conducted a cross-sectional study among AHPs working in a tertiary acute care hospital between October 2019 to December 2019. Based on previous studies looking at the prevalence of burnout in AHPs and the total number of AHPs in Singapore [23, 24], we determined the sample size through the application of a single proportion formula with the assumption of 60% prevalence, 5% marginal error, and 95% confidence level (CI). The minimum required sample size for the study was 348.

Inclusion and exclusion criteria

We defined AHPs according to the definition recommended by Singapore’s AHPC–all healthcare professionals who work collaboratively to deliver routine and essential healthcare services, excluding physicians and nurses [15, 16]. AHPs in a tertiary hospital of all seniority levels were included in this study [16].

Questionnaire design and measurement

We developed an electronic survey and emailed all AHP staff working for the tertiary hospital to request their participation. The survey comprised four components: (1) sociodemographic characteristics, (2) Maslach Burnout Inventory (MBI-HSS), (3) Areas of Worklife Survey (AWS), and (4) Perceived Barriers to Psychological Treatment (PBPT).

Sociodemographic questions were adapted from the Singapore National Health Survey 2010 [25], covering residency status, age, gender, ethnicity, income levels, caregiver status, occupation, employment history, physical activity levels, and mental health.

We assessed burnout by using the Maslach Burnout Inventory (MBI), in particular, the MBI-Human Services Survey for Medical Personnel MBI-HSS(MP) [26]. MBI has been widely used in different settings [27] and is the best-known questionnaire used in most clinical studies assessing burnout [28]. The questionnaire consisted of nine questions under emotional exhaustion (EE), five questions under depersonalization (DP), and eight questions under personal accomplishment (PA). Participants were asked to rate on a Likert scale of 0 (never) to 6 (every day) on how often they experienced the symptoms, and the total scores for each subsection were tallied. Higher EE and DP scores correspond to a higher burnout level, while, conversely, lower PA scores signify a higher burnout level. The scale’s validity has previously been demonstrated in similar studies in Japan and China, countries with strong Asian cultural influence [29–31]. It has also been used to evaluate burnout levels in studies in Singapore [11, 32].

The maximum score was 54 points for EE, 30 points for DP, and 48 points for PA. No universal cut-off score has been recommended to define burnout. In a systematic review of burnout among healthcare professionals, burnout was defined using the cut-offs of EE>27 or DP>10, with PA excluded in the majority of the included studies [27]. PA was also excluded from previous studies because its association with burnout has been more variable and complex [1]. It has been postulated that PA may be a function of EE and DP because a work situation with overwhelming demands may also erode one’s PA [1]. Hence, we defined a high burnout level as experiences of a high level of EE (EE>27), DP (DP>10), or both [33]. We also included an analysis of a high burnout level defined according to EE>27, DP>10, or PA<33 (Appendix 1).

The AWS is a 28-item scale that is part of the MBI toolkit [34]. The scale examines the dimensions of an individual’s work life and predicts their relationship with burnout [35]. The six dimensions assessed in the survey were: workload, control, reward, community, fairness, and values. “Workload” (five items) refers to the employee’s ability to cope with work demands. “Control” (four items) refers to the level of active involvement of an employee in work decisions. “Reward” (four items) refers to rewards that place higher value and recognition on an employee’s work. “Community” (five items) refers to the overall quality of social interaction at work. “Fairness” (six items) refers to the general equity of decisions made at the workplace. Furthermore, “Values” (four items) refers to the dissonance between personal and organizational values [36]. Respondents were asked to rate on a Likert scale of 1 (strongly disagree) to 5 (strongly agree) on their perceptions of work setting qualities that play a role in burnout. The item scores in each domain are then averaged. A higher AWS score indicates a more balanced relationship, rather than a conflicted one [35], between the respondent and their work [37].

The last component of the survey comprised the 27-item PBPT questionnaire [38]. Items are classified into nine domains: stigma, lack of motivation, emotional concerns, negative evaluations of therapy, misfit of therapy to needs, time constraints, participation restriction, availability of services, and cost [38]. We asked participants to rate on a 5-point Likert scale the degree to which each item hindered them from seeing a counselor or a therapist. A score of four to five was deemed as “substantial barriers.” A domain is deemed to represent a “substantial barrier” if at least one item within that domain was reflected as a “substantial barrier.” Given the lengthy questionnaire and to improve the overall response rate [39], we made the PBPT questionnaire component optional for participants in this study.

Data analyses

We used R Commander version 2.7.11 to perform all statistical analyses. We computed Cronbach’s alpha for each MBI subscale and AWS domain to assess reliability. We performed bivariate analyses of the demographic factors and the AWS dimensions to examine their association with burnout level using Pearson’s Chi-square test or Fisher’s exact tests (when a cell count was smaller than five). We identified factors associated with burnout levels by using multiple logistic regression analysis. We entered variables with statistical significance (p<0.05) in bivariate analyses simultaneously in the multiple logistic regression model. For respondents who completed the optional component on PBPT, we recorded the incidence of expressing a variable as a “substantial barrier” among participants who experienced a high burnout level.

Ethical considerations

The National Healthcare Group Domain Specific Review Board approved this study (2019/00477). No identifiable information of participants was collected. We stored all data on REDCap, a secure, Health Insurance Portability, and Accountability Act compliant, web-based server. We included a participant information sheet in the email, providing all relevant information on participant anonymity and consent for voluntary participation.

Results

Sociodemographic characteristics

Among the 1127 eligible AHPs invited, 345 participated in the survey. However, we excluded 17 questionnaires due to incomplete entries. We included a total of 328 respondents in the analyses, providing a response rate of 29.1%. Compared to those who did not participate, our participants were more likely to be female, non-Singaporeans/non-SPR, 21 to 30 years old, and had more than three years of working experience.

Table 1 shows the sociodemographic characteristics of the respondents. The majority of the respondents were Singaporean (73.5%), aged 40 years and below (84.2%), female (83.9%), and Chinese ethnicity (79.9%). Almost all respondents were working full time (94.2%). More than half of the respondents had worked for more than five years in the same organization. Approximately half of the respondents worked as frontline staff and reported low levels of physical activity. Only a small proportion of the respondents reported a history of mental illness or had sought help from a professional within the past year for mental illness. The Cronbach’s alpha coefficients for EE, DP, and PA in MBI-HSS in this study were 0.93, 0.81, and 0.85, respectively, suggesting that the overall measurement was reliable.

Table 1. Sociodemographic characteristics of allied health professionals in a tertiary hospital in Singapore.

Variables		Number (n = 328)
Variables		n (%)
Residency status
	Singaporean	241 (73.5)
	Permanent resident	59 (18.0)
	Foreigner	28 (8.5)
Age group
	21 to 30	137 (41.8)
	31 to 40	139 (42.4)
	41 years and above	52 (15.9)
Sex
	Male	53 (16.1)
	Female	275 (83.9)
Ethnic group
	Chinese	262 (79.9)
	Non-Chinese	66 (20.1)
Average monthly household income
	Less than S$5000	25 (7.6)
	S$5000 to S$9000	118 (36.0)
	S$9000 and above	109 (33.2)
	Not disclosed	76 (23.2)
Caregiver status
	Yes	72 (22.0)
	No	229 (69.8)
	Do not wish to disclose	27 (8.2)
Occupation
	Clinical psychologist	5 (1.5)
	Radiographer	32 (9.8)
	Dietician	19 (5.8)
	Medical technologist	96 (29.3)
	Medical social worker	19 (5.8)
	Occupational therapist	33 (10.0)
	Pharmacist	40 (12.2)
	Physiotherapist	28 (8.5)
	Podiatrist	5 (1.5)
	Respiratory therapist	5 (1.5)
	Speech therapist	17 (5.2)
	Others	29 (8.8)
Duration working at the current organization
	<1 year	24 (7.3)
	1–2 years	47 (14.3)
	3–5 years	64 (19.5)
	>5 years	193 (58.8)
Nature of work
	Front line staff	175 (53.4)
	Administrator	11 (3.3)
	Junior management	55 (16.8)
	Senior management	20 (6.1)
	Others	67 (20.4)
Employment status
	Full time	309 (94.2)
	Part-time	19 (5.8)
Average number of night shifts per month
	1–3 times	33 (10.0)
	4–6 times	18 (5.5)
	7 or more times	4 (1.2)
	Not applicable	273 (83.2)
Level of physical activity^†
	Low	185 (56.6)
	Moderate	74 (22.6)
	High	68 (20.8)
Previous history of mental illness^*
	Yes	7 (2.2)
	No	311 (97.8)
Sough medical help in the past year
	Yes	15 (4.7)
	No	305 (95.3)

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*Mental illness refers to a behavioral or psychological syndrome or pattern in an individual that causes clinically significant distress. It warrants diagnosis and management by a medical professional [40, 41].

† Low physical activity refers to sedentary, little, or no exercise. Moderate physical activity refers to a low level of exertion or aerobic exercises for 20–60 min per week. High physical activity refers to aerobic exercises for > 1 h per week.

Burnout level and associated sociodemographic factors

The self-reported burnout level among AHPs in this study was 67.4%. A majority of the respondents reported a high burnout level on EE (n = 203, 61.9%), less than half reported a high level on DP (n = 139, 42.4%), and more than one-third had both high EE and DP (n = 122, 37.1%). Among the occupational groups, dieticians (94.7%) and pharmacists (82.5%) had the highest burnout levels.

Table 2 shows the sociodemographic characteristics of AHPs stratified by burnout levels. Full-time workers were significantly more likely to experience a high burnout level than part-time workers. Respondents with more than one year of work experience were significantly more likely to experience a high burnout level than those with less than one year of work experience. Respondents who had sought professional mental help in the past year were significantly more likely to have a high burnout level than those who did not.

Table 2. Sociodemographic characteristics of allied health professionals stratified by burnout levels.

Variables		Low burnout (n = 221)	High burnout (n = 107)
Variables		n (%)	n (%)	p-value
Residency status				0.10
	Singaporean	164 (68.0)	77 (32.0)
	Permanent Resident	43 (72.9)	16 (27.1)
	Foreigner	14 (50.0)	14 (50.0)
Age group				<0.01
	21 to 30	101 (73.7)	36 (26.3)
	31 to 40	94 (67.6)	45 (32.4)
	41 years and above	26 (50.0)	26 (50.0)
Gender				1.00
	Male	36 (67.9)	17 (32.1)
	Female	185 (67.3)	90 (32.7)
Ethnic group				0.14
	Chinese	182 (69.5)	80 (30.5)
	Non-Chinese	39 (59.1)	27 (40.9)
Average monthly household income				0.14
	Less than S$5000	18 (72.0)	7 (28.0)
	S$5000 to S$9000	85 (72.0)	33 (28.0)
	S$9000 and above	64 (58.7)	45 (41.3)
	Not disclosed	54 (71.1)	22 (28.9)
Caregiver status				0.96
	Yes	48 (66.7)	24 (33.3)
	No	154 (67.2)	75 (32.8)
	Do not wish to disclose	19 (70.4)	8 (29.6)
Occupation				0.07
	Clinical psychologist	2 (40.0)	3 (60.0)
	Radiographer	19 (59.4)	13 (40.6)
	Dietician	18 (94.7)	1 (5.3)
	Medical technologist	64 (66.7)	32 (33.3)
	Medical social worker	15 (78.9)	4 (21.1)
	Occupational therapist	21 (63.6)	12 (36.4)
	Pharmacist	33 (82.5)	7 (17.5)
	Physiotherapist	17 (60.7)	11 (39.3)
	Podiatrist	3 (60.0)	2 (40.0)
	Respiratory therapist	2 (40.0)	3 (60.0)
	Speech therapist	10 (58.8)	7 (41.2)
	Others	17 (58.6)	12 (41.4)
Duration working at the current organization				<0.01
	<1 year	9 (37.5)	15 (62.5)
	1–2 years	31 (66.0)	16 (34.0)
	3–5 years	53 (82.8)	11 (17.2)
	>5 years	128 (66.3)	65 (33.7)
Nature of work				0.98
	Front line staff	116 (66.3)	59 (33.7)
	Administrator	7 (63.6)	4 (36.4)
	Junior management	38 (69.1)	17 (30.9)
	Senior management	14 (70.0)	6 (30.0)
	Others	46 (68.7)	21 (31.3)
Employment status				<0.01
	Full time	214 (69.3)	95 (30.7)
	Part time	7 (36.8)	12 (63.2)
Average number of night shifts per month				0.24
	1–3 times	25 (75.8)	8 (24.2)
	4–6 times	12 (66.7)	6 (33.3)
	7 or more times	1 (25.0)	3 (75.0)
	Not applicable	183 (67.0)	90 (33.0)
Level of physical activity^†				0.58
	Low	129 (69.7)	56 (30.3)
	Moderate	49 (66.2)	25 (33.8)
	High	43 (63.2)	25 (36.8)
Previous history of mental illness^*				0.43
	Yes	6 (85.7)	1 (14.3)
	No	206 (66.2)	105 (33.8)
Sough medical help in the past year				<0.01
	Yes	15 (100.0)	0 (0.0)
	No	198 (64.9)	107 (35.1)

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AWS domains and association with burnout levels

The Cronbach’s alpha coefficients for workload, control, reward, community, fairness, and values were 0.78, 0.77, 0.89, 0.86, 0.82, and 0.78, respectively. As shown in Fig 1, all AWS domains were significantly associated with a higher burnout level (p≤0.01), with workload, control, and reward showing the most significant differences in the mean scores between participants with a low and high burnout level.

AWS individual statements and association with burnout levels

Fig 2 presents the absolute mean score differences of responses to individual AWS statements between participants with a high and low burnout level. The majority of the mean score differences in all domains were significant. The workload domain had the highest absolute difference compared to the other domains. In particular, the statements “I have so much work to do on the job that it takes me away from my personal interests” (question 3) and “I do not have time to do the work that must be done” (question 1) in the workload domain scored the highest absolute difference in mean scores among all questions.

Factors associated with burnout levels

In the multiple logistic regression model (Table 3), AHPs who had lower mean scores in the workload subdomain of the AWS, indicative of a high workload burden, were almost three times more likely to have a high burnout level than those who had higher mean scores. Compared to respondents aged 30 years and below, older AHPs aged 31 and above were significantly less likely to have a high burnout level. Moreover, respondents who had worked in the current organization for more than three years were approximately five times more likely to experience a higher burnout level than respondents who had worked in the current organization for less than one year.

Table 3. Factors associated with burnout levels in a multiple logistic regression analysis.

		Coefficient (SE)	AOR (95% CI)	p-value
AWS domain
	Workload	-1.05 (0.21)	0.35 (0.23, 0.52)	<0.01
	Control	-0.46 (0.29)	0.63 (0.35, 1.10)	0.11
	Reward	-0.34 (0.25)	0.71 (0.43, 1.17)	0.18
	Community	-0.39 (0.26)	0.68 (0.40, 1.12)	0.13
	Fairness	-0.18 (0.32)	0.83 (0.45, 1.54)	0.56
	Values	-0.10 (0.30)	0.90 (0.50, 1.63)	0.74
Age group
	21 to 30	Reference	1.00	-
	31 to 40	-0.93 (0.45)	0.39 (0.16, 0.94)	0.04
	41 years and above	-1.20 (0.55)	0.30 (0.10, 0.87	0.03
Duration working at the current organization
	<1 year	Reference	1.00	-
	1–2 years	0.61 (0.68)	1.84 (0.50, 7.23)	0.37
	3–5 years	1.66 (0.68)	5.27 (1.44, 20.91)	0.01
	>5 years	1.44 (0.68)	4.23 (1.16, 16.76)	0.03
Employment status
	Part-time	Reference	1.00	-
	Full-time	0.62 (0.60)	1.86 (0.58–6.42)	0.30
Sought medical help in the past year
	No	Reference	1.00	-
	Yes	17.13 (854.15)	27398446.44 (NA)	0.98

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Abbreviations: AWS, Areas of Worklife Survey; SE, standard error; CI, confidence interval; AOR, adjusted odds ratio.

Perceived barriers to seeking psychological help

Of the total, 57.9% (n = 190) of participants completed the optional component on PBPT, of which 130 had a high burnout level. Table 4 shows that, among the participants with a high burnout level, the most frequently cited barriers to seeking psychological help were ‘negative evaluation of therapy’ (60%) and ‘time constraints’ (50%).

Table 4. Perceived barriers to seeking psychological help among participants with a high burnout level (n = 130).

	n (%)
Stigma	29 (48.3)
Lack of motivation	16 (26.7)
Emotional concerns	16 (26.7)
Negative evaluation of therapy	36 (60.0)
Misfit of therapy to needs	27 (45.0)
Time constraints	30 (50.0)
Participation restriction	27 (45.0)
Availability of services	25 (41.7)
Cost	21 (35.0)

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Discussion

This study is the first to investigate the self-reported burnout level and its related factors among AHPs in Singapore. We found a high burnout level at 67.4% among AHPs in a tertiary hospital. Based on the job demands-resources model of burnout, high EE and DP scores in our study demonstrates a high probability of resource conservation by AHPs. AHPs may spend less time with patients, resulting in increased clinical errors [42] and negatively impacting patient care. However, compared to a study conducted among physical and occupational therapists in the United States, while the EE scores were similar (58% vs. 62% in our study), the DP scores in our study were significantly lower (94% vs. 42% in our study) [21]. The relatively lower depersonalization scores may be attributed to the participants’ organizational factors, such as different healthcare systems and attitudes towards work between AHPs in Asian and Western societies [43]. The lower level could be culturally equivalent to the United State’s higher levels due to differences in the participants’ attitudes towards surveys and response patterns [44–46].

Of note, the high self-reported burnout level in pharmacists (82.5%) and dieticians (94.7%) is concerning. We postulate that pharmacists may be prone to experiencing burnout and lower job satisfaction than other occupations, with more job variety reported in previous studies [47]. However, similar studies have shown that dieticians score lower EE than comparison groups of doctors, nurses, and social workers [23], indicating lower burnout. Hence, the high burnout level among dieticians may be due to other organizational or demographic factors. As the sample size of pharmacists and dieticians in this study was small, these associations were not statistically significant. Further studies will be warranted to identify the associated factors of burnout.

In the multiple regression analysis, we found a higher burnout level in the younger group of 21 to 30 than in AHPs aged 31 years and above. Previous studies have supported this trend of burnout affecting younger employees [2, 3]. The lower burnout level in older participants may be explained by their better coping or occupational handling stress [48, 49]. Work experience may play an essential role in burnout. Employees who have worked for a longer duration (three years and above) in the same organization were more likely to have a high burnout level than those working for less than a year. We postulate that this could be due to long-term exposure to the patient suffering at the workplace, resulting in emotional exhaustion [50, 51].

We found that heavier self-reported workloads are associated with a higher burnout level among the AHPs. It was the only subdomain of the AWS significantly associated with burnout after adjusting for covariates. Previous studies have shown the adverse effects of increased workloads among healthcare workers, manifesting burnout [52, 53]. In our study, we demonstrated that this association holds for AHPs in Singapore. In particular, the association of heavier workload among AHPs with a high burnout level is most apparent when the workload interferes with their “personal interests” and “work that must be done.”

Hence, the identified associated factors of burnout levels highlight the need to address potential stressors at work. Concurrently, given that heavy self-reported workload and more extended work experience is associated with a high burnout level, workplace interventions are crucial. Based on this study, the association of heavier self-reported workload among AHPs with a high burnout level is most apparent when the workload becomes excessive or interferes with their interests. We propose that future studies look at interventions conducted at both personal and workplace levels [54].

Evidence-based strategies have shown the effectiveness of interventions that target personal coping skills such as mindfulness and stress management training [55, 56], and cognitive-behavioral interventions in reducing occupational stress levels [57].

Workplace strategies could be explored in future studies. Protected time, proper shift allocations, flexibility in working structure, and adequate workforce distribution could be highly beneficial [58, 59]. A case example will be the United Kingdom-commissioned review [60]. The review proposes a whole-system workplace intervention, from understanding local staff requirements, multi-level staff engagement, strong visible leadership, support for well-being at board level, and a focus on management capability to improve mental well-being and lower burnout.

Lastly, among participants who completed the PBPT questionnaire and experienced a high burnout level, ‘negative evaluation of therapy’ and ‘time constraints’ were identified as the most frequently cited barriers to seeking psychological help. Firstly, negative evaluation of therapy may be attributed to the high prevalence of negative attitudes towards mental illnesses in Asian societies such as Singapore [61, 62]. Participants may experience similar negative perceptions of therapy for mental health. Hence, interventions in improving the public perception towards mental health and therapy may reduce barriers to seeking help. Secondly, time constraints highlight that the daily responsibilities of AHPs may contribute to burnout and compete for time, hence a barrier in undergoing therapy. Daily responsibilities include formal duties to their patients and adjunct activities such as documentation, communication, following up on treatment, performing roll calls, or handing over. These auxiliary activities underestimate the time spent on the job [63]. Accounting for the adjunct activities and enforcing stricter regulations in total work hours may be essential to improve uptake of AHPs in seeking help for their burnout.

Study limitations

There are a few limitations to this study. First, the response rate to the survey was only 29.1%. The low response rate may translate to a significant non-response bias for the study. Despite utilizing approaches to increase the response rate, such as through the engagement of respective departmental heads and email reminders, the survey response remained low. The low response rate may be due to hospital privacy protocols that limited the survey administration to emails and prevented physical surveys. Second, burnout is multi-factorial, and this study may not capture the full spectrum of variables. Factors that were not covered in this study include the increasing computerization of practice [64] and the participants’ personality traits [65]. Third, this study's cross-sectional nature does not allow the authors to determine causal relationships between the risk factors and burnout. Further longitudinal studies will be needed. Fourth, other inventories such as the Copenhagen Burnout Inventory can be explored in future studies to offer new insights into burnout [66]. Fifth, as there are limited validation studies of MBI in Asian countries, MBI may have limited validity in characterizing burnout as a self-reported tool. Lastly, participant response could have been influenced by social desirability bias due to the highly stigmatized perception of burnout in the workplace.

Conclusions

This study is the first to show a high burnout level and identify its associated factors among AHPs in Singapore. The self-reported burnout level among AHPs in this study was 67.4%. The identified risk factors included increased self-reported workload, lesser work experience, and younger age. Besides, respondents with a high burnout level reported the lack of motivation and time constraints as significant barriers to seeking psychological help for burnout. The findings revealed the significance and urgency of addressing burnout in these vulnerable target groups. There is also a potential need to implement individual and organizational interventions such as mindfulness and stress management training, cognitive-behavioral interventions, or workplace interventions that target organizational, cultural, social, and physical aspects of staff health. These interventions should be implemented with proper consideration of the barriers to reduce burnout risk effectively. Further longitudinal studies will help explore the causal relationship between the risk factors and burnout to characterize burnout's nature better.

Supporting information

S1 Data

(XLSX)

Click here for additional data file.^{(173.3KB, xlsx)}

Acknowledgments

The authors would like to thank the faculty members of Saw Swee Hock School of Public Health and the Yong Loo Lin School of Medicine, whose advice and ideas were integral to this study's success. The authors would like to thank the anonymous reviewers whose input and feedback significantly improved this manuscript. Lastly, the authors would like to thank the Community Health Project team members for their contributions to the study's conceptualization.

Data Availability

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

Funding Statement

The authors received no specific funding for this work.

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Prevalence and associated factors of burnout in allied healthcare professionals in a tertiary hospital in Singapore

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Reviewer #1: The authors are congratulated for their work, which has been properly written and structured. To be able to publish it, it would be advisable that the authors could take into account the following minor comments:

The introduction is correct, but it can be expanded a little more. What is known about these job positions in other countries? What consequences have been observed in other countries with respect to burnout? What specific functions do they perform and why do they exist in some countries and not in others? What is their specific role in Singapore?

In reference 19, "In Singapore" is repeated twice. Study hypotheses are required.

Method: The type of mental illness must be described.

Discussion: What is the reason for a low response rate?

Reviewer #2: Thank you very much for allowing me to review this interesting and important study. I fully agree that there is too little in this area yet, in Singapore and beyond. This being said, I do have a number of concerns about your methods and interpretation. I have tried to order them in importance.

As you (partly) state in the limitations section, your study suffers from a low response rate. In addition, I am not convinced by the validity evidence you cite for all three scales. For these reasons, I advise you to be very careful with the word "prevalence". But let me go into more detail on the two points first, before elaborating why.

In regards to the former, do you have any information on the distribution of demographic characteristics in the target population? Does the sample reflect the target population in key characteristics, or were eg females more likely to respond to your email? This would be valuable information to judge on the representativeness of the sample despite the low response rate. In the limitations section, you state that "The low response rate may lead to a falsely lower burnout rate in our study." Kindly add an explanatory sentence. Could it not also be the other way around, ie people with burnout symptoms being more prone to answering a survey which speaks to their own experiences?

In regards to the latter, are there no validation studies in Singapore or at least in a sligthly "closer" linguistic and cultural context, especially for the MBI? As for the alpha's that you report from the referenced studies, I'm not sure there is much value in that, especially since you present your own alpha's in the results section (although I'm not quite sure why also for PA, which you don't use), which are much more meaningful.

Assuming there are not well-fitting and robust validation studies, it would be good to explicitly address the issue in the limitation section. Plenty of literature has shown that many self-reported screening tools developed in Europe or North America have only limited validity in other parts of the world due to differences in experiences, differences in answer behavior, etc.

For the above two reasons, I advise you to be very careful with the word "prevalence". You use a non-validated self-reported screening tool in a population with low response rate, so if anything, your results will be in the ball park of the "actual" burnout situation, but likely quite a bit off (as it would be evaluated by a clinical psychologist). You could for instance write "68% reported symptoms indicative of burnout" or "68% had high scores" instead. Just to be very clear - I do not mean to invalidate your study in any way, quite the opposite, but find it important to make the limitations very clear to readers without background in epidemiology and diagnostics.

Given that you use only two dimensions to define burnout, I would further be careful with presenting an overall prevalence. This will be confusing to readers trying to compare across contexts, as the "norm" is use of all three subscales for an overall burnout prevalence. I speak from the position of somebody who has recently tried to perform a meta-analysis with MBI studies, and ended up extremely frustrated as comparability was very difficult due to this element and others. I therefore think it would be better and clearer to report on the two dimensions separately.

It would further be good if you added a sentence or two to the description of each measure in the methods section allowing the reader to understand how you arrived at the numeric values, and where to situate your cut-off scores on the maximum range of scores. How many items per dimension, which response scale did you use, how was it scored, how did you combine the individual items, and what was the final theoretical range per domain? Again, this was the main reason why I couldn't include most studies in the meta-analysis. This is particularly important for AWS, as it's impossible to make much sense of Figures 2 and 3 without this background information, but also to be able to put into perspective (and be able to compare across settings) the burnout cutoff scores. For burnout, since in Figure 1 you report low, moderate, high, it would also be good to give thresholds for low-moderate, not just for moderate-high.

In terms of results, I'm not sure I find what you did in Table 3 meaningful, and I don't agree with the conclusions you draw on its basis. Since the measure is about psychological help related to burnout, why do you compare people with burnout with people without, rather than to examine the frequency of cited factors in the group of respondents with burnout? For instance, you conclude that "lack of motivation" is associated with burnout, but in the sense that people without burnout have more frequent lack of motivation to seek help for burnout - which is not surprising since they also don't have need! The much more interesting information to me is that in the burned out group, 1/4 experience lack of motivation to seek help (if I read the proportion right). Consider reorganizing the table and aligning the interpretation. In the discussion, you write that "‘lack of

motivation’ and ‘time constraints’ were identified as significant barriers to seeking psychological help.". However, if I read the table right, the most frequently cited barriers in the burned out group are actually "negative evaluation of therapy", "time constraints", and "stigma".

And finally, a few more minor comments:

In terms of organization of results, it's a bit unconventional to mix sample description and substantial results - I suggest you separate this (effectively removing the sentence on burnout prevalence by cadre).

Table 1 seems is missing the non-Singaporeans, females, non-Chinese ethnic, caregiver-non, not being frontline staff, not being employed full time, high level of physical activity, no history of mental illness, and no help seeking in last year. The way I read the proportions, proportions of participants with and without burnout cannot easily be calculated for them from the respective other groups (one would for instance have to subtract the burned out males from all burned out participants and same for the non-burned-out to then know how many females and how they are distributed among burned out and not burned out).

In the methods, you say that you didn't use PA, but you present the data in Figure 1. Kindly align or clarify. For PA, did you reverse scores, ie Figure 1 high refers to low personal accomplishment (indicative of burnout)? Or is high high personal accomplishment (ie no burnout)?

Discussion - differences in DP prevalences US-Singapore: You attribute this to the setting, but it might well be to differences in responding to items. Since there seems to be no robust validation/equivalence study, I would at least point out that it might also just be a methodological artefact, or explicitly state the assumption of cultural equivalence.

"Compared to younger employees, older workers have been better at handling occupational stress and are less prone to burnout [43]." Consider revising this strong statement (eg "A study in Iran, for instance, has found (or just speculated?) that older workers might be better at handling occupational stress and thereby be less prone to burnout). If this is a "proven fact" (by many robust studies), then kindly cite accordingly.

Similarly, kindly be careful with statements implying causality. For instance, on the association workload - burnout. Since all is self-report to my understanding (and not even self-reported facts such as number of work hours), could it not also be that burned out people perceive their workload to be higher?

I don't fully understand how you infer "In particular, the association of heavier workload among burnout AHPs is most apparent when the workload interferes with their “personal interests” and “work that must be done.”" Kindly explain.

Reviewer #3: The paper focuses on burnout and refers in particular to healthcare professionals. Burnout is one of the most studied variables in healthcare and there is established literature on it. Since the World Health Organization has included burnout in the revision of the ICD-11 as an occupational phenomenon, this research topic is still current and interesting.

Title: I suggest to avoid the term “in allied”

Abstract: Results: the authors say “Burnout was positively associated with a longer work experience of 3 to 5 years” Do you think 3-5 years is a longer work experience?. Also, authors state that “and more than five years (AOR 4.24; 95% CI 1.16-16.79)”, unclear 5 years of experience in the profession or position?

In abstract authors say that, “there is a lack of studies on burnout among allied health professionals in Singapore”, but in introduction you state “For example, high rates of burnout and its associations among physicians and nurses have been reported in Singapore. Extensive research involves the barriers to seeking help for doctors, such as fear of stigma, lack of available time, and lack of convenient access” and latter “However, there are currently no studies examining the prevalence and associations of burnout among AHPs in Singapore, and also in discussion “This study is the first to investigate the prevalence of burnout and its related factors among AHPs in Singapore” Please reconcile, these sentences are contradictory”.

Why the authors use the term “Allied health professionals” and not only healthcare professionals ? There is any difference

This sentence is not clear: “In Singapore, In Singapore, the Allied Health Professions Council (AHPC) further classified allied health occupations”

Study design and sampling: “among AHPs”. Clarify what kind of professionals?

“between October 2019 to December 2019”. Did the survey coincide with COVID-19 pandemic?

I am not clear, what is the difference between “Staff members in the tertiary hospital” and “AHP”, also authors state “definition established by the AHPC in Singapore”.

I’m not clear, you say “Among the three subscales, PA was excluded from this study because its association with burnout has been more variable and complex, similar to previous studies”, the MBI includes all 3 dimensions. If the authors do not believe that this instrument was valid, why did they not use another measurement instrument such as the Copenhagen Burnout Inventory?

Questionnaire design and measurement: please establish the cut-off score of low, medium and high levels in the three dimensions, and not only the cut-off for high EE and D, and low PA.

In table 1. Columns “With burnout and Without burnout” are not clear, what criteria did the authors follow to consider burnout, if they did not take PA into account?

Also authors say: “The Cronbach’s alpha coefficients for EE, DP, and PA in MBI-HSS were 0.93, 0.81, and 0.85, respectively, suggesting that the overall measurement was reliable”, Is this data your this study? I understand that you collected PA data but you did not analyse it?

Discussion: “workplace interventions are crucial”. Please expand what kind of interventions could be carried out

Editing by a written English expert would improve word choice and overall flow.

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PLoS One. 2021 Jan 6;16(1):e0244338. doi: 10.1371/journal.pone.0244338.r002

Author response to Decision Letter 0

3 Nov 2020

Responses to reviewers (R1)

PONE-D-20-26669

Prevalence and associated factors of burnout in allied healthcare professionals in a tertiary hospital in Singapore

PLOS ONE

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2. In your revised cover letter, please address the following prompts:

We will update your Data Availability statement on your behalf to reflect the information you provide.

RESPONSE:

We have uploaded the anonymized data set as a Supporting Information file.

Academic Editor: Dr. Jenny Wilkinson, PhD

RESPONSE:

We would like to take this opportunity to thank the Academic Editor for your kind consideration of our manuscript and your guidance for improving it. We have addressed all reviewers’ comments very carefully and provided a point-by-point response to each comment. We hope you will agree that the manuscript has been significantly improved and could be published in PLOS ONE.

Reviewer #1

The authors are congratulated for their work, which has been properly written and structured. To be able to publish it, it would be advisable that the authors could take into account the following minor comments:

RESPONSE:

We thank the reviewer for your constructive feedback. We have made the changes according to the reviewer’s comments where possible and provided a point-by-point reply below, which we hope will address the reviewer’s feedback.

1. The introduction is correct, but it can be expanded a little more. What is known about these job positions in other countries? What consequences have been observed in other countries with respect to burnout? What specific functions do they perform and why do they exist in some countries and not in others? What is their specific role in Singapore?

RESPONSE:

Thank you for your comment. Your suggestions have been incorporated in Page 3 Paragraph 4 under Introduction. We have also expanded on the various points as follows.

For our definition of Allied Health Professionals, we have referred to the definition provided by the US Association of Schools Advancing Health Professions. This definition refers to Allied Health Professionals as “individuals, distinct from medicine and nursing, who work collaboratively to deliver routine and essential healthcare services. They include but are not limited to occupational therapists, physiotherapists, pharmacists, medical social workers, and radiographers.” We hope this definition helps to improve the clarity of the Introduction.

Thank you for pointing out the importance of describing the specific role Allied Health Professionals play in Singapore compared to other countries. In Singapore, the Allied Health Professions Council regulates the "professional standards for practice, conduct & ethics of registered allied health professionals in Singapore.” Their definition of an Allied Health Professional is similar to that stated above and includes similar groups of healthcare professionals, such as occupational therapists and physiotherapists.

Beyond Singapore, Allied Health Professionals play similar roles in the United Kingdom and the United States. They improve hospital efficiency and access to healthcare and promote better health-related quality of life. This is again similar to the definition provided by the US Association of Schools Advancing Health Professions.

Thank you for pointing out the importance of stating the consequences of burnout in other countries as well. We have incorporated it on Page 3, Paragraph 2. Previous studies have demonstrated a wide variety of consequences that we have divided into individual and institutional categories. These studies showed that burnout had been observed to be associated with increased absenteeism, medical errors, sickness, injury and even accidents on an individual level. On an institutional level, burnout was linked to a higher turnover of healthcare workers, decreased workforce efficiency, and higher economic burden on the healthcare system. These consequences were reported in burnout studies done on populations in the United States, Poland, and Switzerland.

2. In reference 19, "In Singapore" is repeated twice.

RESPONSE:

Thank you for pointing it out. We sincerely apologize for the typo. We have removed the repeated “In Singapore.”

3. Study hypotheses are required.

RESPONSE:

Thank you for your feedback. We have included the hypothesis in the manuscript on Page 4, Paragraph 2. Our study was conducted with the hypothesis that there will be a high level of burnout among Allied Health Professionals in Singapore.

As there have been no studies of burnout among Allied Health Professionals in Singapore, we based this hypothesis on the existing burnout studies done on the physician and nursing populations in Singapore. These studies found a significant self-reported burnout prevalence at 30-40% of the study population. We thus hypothesized that our study results would be similar due to shared workplace challenges and occupation settings in healthcare.

The study by Dhaliwal et al. on burnout among nurses in Singapore found “age” and “job grade” were significantly associated with burnout defined by MBI. Similarly, W. Y Tay reported “increased number of years working as a nurse [...] to be significantly associated with burnout.” It was in reference to these studies we based our hypothesis.

4. Method: The type of mental illness must be described.

RESPONSE:

Thank you for your comment. We agree that there is a need for a specific description of mental illness. We chose to phrase mental illness as a broader classification to accept any type of mental illness. We have included in the footnote of Table 1 on Page 9, defining mental illness as “a behavioral or psychological syndrome or pattern that occurs in an individual which causes clinically significant distress. It warrants diagnosis and management by a medical professional”. Nonetheless, we understand that this might pose ambiguity to respondents and hence our survey participants were all provided contact details of the Principal Investigators should they have any queries.

5. Discussion: What is the reason for a low response rate?

RESPONSE:

Thank you for your comment. Our study team initiated various strategies to increase the response rate. Firstly, we approached the healthcare institution’s administrative coordinators and worked with them to disseminate the survey to their staff. The survey was emailed to them, and they were actively encouraged and reminded to take part in it. Secondly, we coordinated with public health professionals from the Saw Swee Hock School of Public Health and sought their prior experience in improving recruitment. Finally, we worked with the leadership of allied health departments from the National University Health System to optimize the reach of our emails.

We understand that physically administering the survey might have generated a higher response rate. However, hospital privacy guidelines prevented access to workplaces and the conduction of physical surveys.

We acknowledge that this low response rate has potentially introduced bias into our study. We have included this point under Study Limitations on pages 16-17, stating that the low response rate translated to a ‘significant non-response bias for the study.’ Nevertheless, we think these findings may still be relevant to policymakers because of the dearth of studies on burnout in Allied Health Professionals. It provides a starting point for future, more comprehensive studies to characterize the true extent of the burnout problem and explore potential interventional strategies for burnout.

Reviewer #2

Thank you very much for allowing me to review this interesting and important study. I fully agree that there is too little in this area yet, in Singapore and beyond. This being said, I do have a number of concerns about your methods and interpretation. I have tried to order them in importance.

RESPONSE:

We thank the reviewer for your insights and constructive feedback. We have provided a point-by-point reply below and incorporated the reviewer’s suggestions where possible, which we hope will address the reviewer’s concerns.

1. In regards to the former, do you have any information on the distribution of demographic characteristics in the target population? Does the sample reflect the target population in key characteristics, or were eg females more likely to respond to your email? This would be valuable information to judge on the representativeness of the sample despite the low response rate. In the limitations section, you state that "The low response rate may lead to a falsely lower burnout rate in our study." Kindly add an explanatory sentence. Could it not also be the other way around, ie people with burnout symptoms being more prone to answering a survey which speaks to their own experiences?

RESPONSE:

Thank you for pointing this out. We agree with the comments. Therefore, we have included statements comparing the demographics of respondents with non-respondents. (Page 7, paragraph 1) In summary, compared to those who did not participate, those who participated were more likely to be female, non-Singaporeans/non-SPR, 21 to 30 years old, and had more than 3 years of working experience.

We also agree that our inference in the limitations section is an unsupported statement. We agree that the low response rate may lead to either a falsely lower or higher burnout rate. Hence, we have removed the statement.

2. In regards to the latter, are there no validation studies in Singapore or at least in a slightly "closer" linguistic and cultural context, especially for the MBI?

As for the alpha's that you report from the referenced studies, I'm not sure there is much value in that, especially since you present your own alpha's in the results section (although I'm not quite sure why also for PA, which you don't use), which are much more meaningful.

RESPONSE:

Thank you for your comment. There have been limited validation studies in Asian societies in our literature review. To address this issue, we highlighted how the MBI had been used in studies conducted in China, Japan, and Singapore. These include Wang Z et al., Nishimura K et al., and See KC, respectively.

Thank you for pointing out the value in reporting the Cronbach alpha’s of the referenced articles. We agree with you on only presenting the more meaningful values from our studies. Hence, we have removed the Cronbach alpha’s of the referenced studies.

We agree with your comment on explicitly addressing a lack of validation studies in the limitation section. On Page 17, Paragraph 2, we have included this limitation, stating, ‘as there are limited validation studies of MBI in Asian countries, MBI may have limited validity in characterizing burnout as a self-reported tool.’

3. For the above two reasons, I advise you to be very careful with the word "prevalence". You use a non-validated self-reported screening tool in a population with low response rate, so if anything, your results will be in the ball park of the "actual" burnout situation, but likely quite a bit off (as it would be evaluated by a clinical psychologist). You could for instance write "68% reported symptoms indicative of burnout" or "68% had high scores" instead. Just to be very clear - I do not mean to invalidate your study in any way, quite the opposite, but find it important to make the limitations very clear to readers without background in epidemiology and diagnostics.

RESPONSE:

Thank you for your advice on using the word ‘prevalence.’ We agree with your comment and have incorporated your suggestion throughout the manuscript. The use of ‘prevalence’ has been changed to ‘self-reported levels’ in the title, abstract, and throughout the manuscript.

Thank you for your suggestion. We have reported the levels of burnout according to the 2 dimensions separately (Page 9, Paragraph 1), stating ‘A majority of the respondents reported high level of burnout on EE (n=203, 61.9%), less than half reported a high level on DP (n=139, 42.4%), and more than one-third had both high EE and DP (n=122, 37.1%).’

Thank you for pointing this out. We have decided to report on the overall self-reported burnout levels according to the pre-specified cut-offs of EE>27 and/or DP>10 based on the following reasons. While there is no universal cut-off score to define burnout, in a recent systematic review of burnout in healthcare professionals, burnout was defined using the cut-offs of only EE>27 and/or DP>10, with PA excluded, in the majority of participants. Furthermore, it has been postulated by Maslach that PA’s association has been more variable and complex, and it may be a function of EE and DP instead. Hence, we defined burnout according to the cut-offs of EE>27 and/or DP>10. We have included this explanation in the methodology to minimize confusion for the readers (Page 5, Paragraph 2).

4. It would further be good if you added a sentence or two to the description of each measure in the methods section allowing the reader to understand how you arrived at the numeric values, and where to situate your cut-off scores on the maximum range of scores. How many items per dimension, which response scale did you use, how was it scored, how did you combine the individual items, and what was the final theoretical range per domain? Again, this was the main reason why I couldn't include most studies in the meta-analysis. This is particularly important for AWS, as it's impossible to make much sense of Figures 2 and 3 without this background information, but also to be able to put into perspective (and be able to compare across settings) the burnout cutoff scores. For burnout, since in Figure 1 you report low, moderate, high, it would also be good to give thresholds for low-moderate, not just for moderate-high.

RESPONSE:

Thank you for the suggestion. We agree and have added descriptions for MBI and AWS, including number of items, dimensions, and the relevant information on scoring and cut-offs used under the “Questionnaire design and measurement” pages 4-6. For convenient reference, the cut offs for low, moderate, high used in Figure 1 previously are as follows:

EE High: >27, Moderate: 19-26, Low 0-18

DP High: >10, Moderate: 6-9, Low: 0-5

PA High: 0-33, Moderate: 34-39, Low: >40

However, we have removed the individual low, moderate, high cutoffs, and the original figure 1 from the paper to improve clarity to readers, as the cutoffs were not meaningful for discussion in the subsequent analyses.

5. In terms of results, I'm not sure I find what you did in Table 3 meaningful, and I don't agree with the conclusions you draw on its basis. Since the measure is about psychological help related to burnout, why do you compare people with burnout with people without, rather than to examine the frequency of cited factors in the group of respondents with burnout? For instance, you conclude that "lack of motivation" is associated with burnout, but in the sense that people without burnout have more frequent lack of motivation to seek help for burnout - which is not surprising since they also don't have need! The much more interesting information to me is that in the burned out group, 1/4 experience lack of motivation to seek help (if I read the proportion right). Consider reorganizing the table and aligning the interpretation. In the discussion, you write that "‘lack of

motivation’ and ‘time constraints’ were identified as significant barriers to seeking psychological help." However, if I read the table right, the most frequently cited barriers in the burned out group are actually "negative evaluation of therapy", "time constraints", and "stigma".

RESPONSE:

Thank you for pointing this out. We agree with this comment. Therefore, we have reorganized the table and only analyzed (Page 14, Paragraph 1) and discussed (Page 16, Paragraph 4) the frequently cited barriers to seeking psychological help among participants who experienced high levels of burnout. For convenient reference, among burnout participants, the most frequently cited barriers to seeking psychological help were ‘negative evaluation of therapy’ (60%), and ‘time constraints’ (50%).

6. In terms of organization of results, it's a bit unconventional to mix sample description and substantial results - I suggest you separate this (effectively removing the sentence on burnout prevalence by cadre).

RESPONSE:

Thank you for your suggestion. We have separated it into Table 1 (sample description) and Table 2 (substantial results).

7. Table 1 seems is missing the non-Singaporeans, females, non-Chinese ethnic, caregiver-non, not being frontline staff, not being employed full time, high level of physical activity, no history of mental illness, and no help seeking in last year. The way I read the proportions, proportions of participants with and without burnout cannot easily be calculated for them from the respective other groups (one would for instance have to subtract the burned out males from all burned out participants and same for the non-burned-out to then know how many females and how they are distributed among burned out and not burned out).

RESPONSE:

Thank you for your comment. We agree that it is difficult to be calculated and is confusing for readers. Therefore, we have added the missing data into Tables 1 and 2, and we hope this improves clarity for readers.

8. In the methods, you say that you didn't use PA, but you present the data in Figure 1. Kindly align or clarify. For PA, did you reverse scores, ie Figure 1 high refers to low personal accomplishment (indicative of burnout)? Or is high personal accomplishment (ie no burnout)?

RESPONSE:

Thank you for your comment. By convention, low PA scores in the MBI correspond to high burnout. The cut-offs are as follows:

EE High burnout: >27, Moderate: 19-26, Low 0-18

DP High burnout: >10, Moderate: 6-9, Low: 0-5

PA High burnout: 0-33, Moderate: 34-39, Low: >40

We have removed the cut-offs, PA, and the original figure 1 from the paper to improve clarity to readers, as they are not meaningful for discussion in the subsequent analyses. The rationale for why PA was not used is explained in the methodology (Page 5, paragraph 2), and question 3 of the reviewer’s comments.

9. Discussion - differences in DP prevalence US-Singapore: You attribute this to the setting, but it might well be to differences in responding to items. Since there seems to be no robust validation/equivalence study, I would at least point out that it might also just be a methodological artefact, or explicitly state the assumption of cultural equivalence.

RESPONSE:

Thank you for your comment. We agree with your suggestion. We acknowledge that there are inherent cultural differences between the two populations. This is supported by previous studies. Dolnicar, S. et al. stated that a difference in the cultural background is a “significant potential source of misinterpretation in cross-cultural studies.” We agree that this would affect the response to the surveys and the study’s results, especially with the differences in the concept of depersonalization and work by our study participants. Hence, we have assumed that there is cultural equivalence to be able to compare the results.

10. "Compared to younger employees, older workers have been better at handling occupational stress and are less prone to burnout [43]." Consider revising this strong statement (eg "A study in Iran, for instance, has found (or just speculated?) that older workers might be better at handling occupational stress and thereby be less prone to burnout). If this is a "proven fact" (by many robust studies), then kindly cite accordingly.

RESPONSE:

Thank you for the comment. We agree and have revised the statement accordingly for greater clarity. The revised sentence now reads, “The lower burnout level in older participants may be explained by their better coping or occupational handing of stress [51,52].” We have cited the following two studies: Scheibe et al. reported an “older-age advantage for recovery from work-demands,” and Hsu HC, who reported on the increased “resilience of older workers.”

11. Similarly, kindly be careful with statements implying causality. For instance, on the association workload - burnout. Since all is self-report to my understanding (and not even self-reported facts such as number of work hours), could it not also be that burned out people perceive their workload to be higher?

RESPONSE:

Thank you for pointing it out. We have accordingly revised the word ‘contributed’ to ‘associated with.’ We have also changed ‘workload’ to ‘self-reported workload’ to improve clarity in the abstract, discussion, and conclusion.

12. I don't fully understand how you infer "In particular, the association of heavier workload among burnout AHPs is most apparent when the workload interferes with their “personal interests” and “work that must be done.”" Kindly explain.

RESPONSE:

Thank you for your comment. ‘Personal interests’ and ‘work that must be done’ refer to questions 3 and 1 of the AWS questionnaire, respectively. Both questions are part of the workload domain and have the highest absolute mean difference between burnout and non-burnout groups (Figure 3) among all questions. Hence, this association of heavier workload was noted to be most apparent in the context of these individual statements (part of the workload domain). We have added this explanation in the results to improve clarity (Page 12, Paragraph 2).

Reviewer #3

The paper focuses on burnout and refers in particular to healthcare professionals. Burnout is one of the most studied variables in healthcare and there is established literature on it. Since the World Health Organization has included burnout in the revision of the ICD-11 as an occupational phenomenon, this research topic is still current and interesting.

RESPONSE:

We thank the reviewer for your constructive feedback. We have incorporated the reviewer’s suggestions where possible and provided a point-by-point reply below, which we hope have addressed the reviewer’s concerns.

1. Title: I suggest to avoid the term “in allied”

RESPONSE:

Thank you for the suggestion. After careful consideration, we have decided to use the term “Allied Health Professionals” throughout our paper, to distinguish from “Health Professionals,” which would include doctors and nurses. For our definition of Allied Health Professionals, we have referred to the definition provided by the US Association of Schools Advancing Health Professions, which define Allied Health Professionals as “individuals, distinct from medicine and nursing, who work collaboratively to deliver routine and essential healthcare services. They include but are not limited to occupational therapists, physiotherapists, pharmacists, medical social workers, and radiographers.”

However, in consideration of your suggestion, we have made corrections to make the definition of “Allied Health Professionals” clearer to readers, both through corrections in our Abstract (Page 2, Paragraph 1) and Introduction (Page 3, Paragraph 4). We thank the reviewer for this opportunity to improve the clarity of our manuscript.

2. Abstract: Results: the authors say “Burnout was positively associated with a longer work experience of 3 to 5 years” Do you think 3-5 years is a longer work experience? Also, authors state that “and more than five years (AOR 4.24; 95% CI 1.16-16.79)”, unclear 5 years of experience in the profession or position?

RESPONSE:

Thank you for the suggestion. We have accepted the edit and the sentence now reads, "Burnout was positively associated with a work experience of 3 to 5 years and more than 5 years, as compared to that of 1 to 2 years.” This can be found in the 3rd paragraph of the Abstract (Page 2 Paragraph 3)

3. In abstract authors say that, “there is a lack of studies on burnout among allied health professionals in Singapore”, but in introduction you state “For example, high rates of burnout and its associations among physicians and nurses have been reported in Singapore. Extensive research involves the barriers to seeking help for doctors, such as fear of stigma, lack of available time, and lack of convenient access” and latter “However, there are currently no studies examining the prevalence and associations of burnout among AHPs in Singapore, and also in discussion “This study is the first to investigate the prevalence of burnout and its related factors among AHPs in Singapore” Please reconcile, these sentences are contradictory”.

RESPONSE:

Thank you for the suggestion. We have accepted the suggestion and have made clearer the definition of “Allied Health Professionals” right from the start. The sentence now reads “Allied Health Professionals (AHPs) are defined as individuals, excluding physicians and nurses, who work collaboratively to deliver routine and essential healthcare services.”

This sentence can be found in the 1st paragraph of the Abstract (Page 2 Paragraph 1) and the 4th paragraph of the Introduction (Page 3 Paragraph 4).

4. Why the authors use the term “Allied health professionals” and not only healthcare professionals? There is any difference.

RESPONSE:

Thank you for your suggestion. We have clarified our definition of Allied Health Professionals, similar to the comment above.

5. This sentence is not clear: “In Singapore, In Singapore, the Allied Health Professions Council (AHPC) further classified allied health occupations”

RESPONSE:

Thank you for your suggestion. We agree and have made the relevant changes. We have defined AHPs in the earlier part of that paragraph (Page 3 Paragraph 4). Hence, we have rephrased it to “In Singapore, the Allied Health Professions Council (AHPC) defines and classifies allied health occupations similar to that of other countries” to improve clarity that our definition of AHPs is similar to international definitions. We would also like to sincerely apologize for the typo that was present in the original sentence.

6. Study design and sampling: “among AHPs”. Clarify what kind of professionals?

RESPONSE:

Thank you for your suggestion. We have clarified our definition of Allied Health Professionals. As we have earlier defined AHPs in the introduction, we similar included AHPs in the study design as “healthcare professionals, excluding physicians and nurses, who work collaboratively to deliver routine and essential healthcare services” on Page 4, Paragraph 4.

7. “between October 2019 to December 2019”. Did the survey coincide with COVID-19 pandemic?

RESPONSE:

To clarify, the survey did not coincide with the COVID-19 pandemic.

8. I am not clear, what is the difference between “Staff members in the tertiary hospital” and “AHP”, also authors state “definition established by the AHPC in Singapore”.

RESPONSE:

Thank you for your suggestion. We agree and have made the relevant changes. We have removed the lines “Definition established by the AHPC in Singapore,” as we have earlier fully defined AHPs in the 4th paragraph of the Introduction (Page 3, Paragraph 4). As such, the inclusion criteria on page 4 now reads, “We included AHPs according to the definition stated in the introduction - all healthcare professionals, excluding physicians and nurses, who work collaboratively to deliver routine and essential healthcare services. AHPs in a tertiary hospital of all seniority levels were included in this study,” with no reference to AHPC.

9. I’m not clear, you say “Among the three subscales, PA was excluded from this study because its association with burnout has been more variable and complex, similar to previous studies”, the MBI includes all 3 dimensions. If the authors do not believe that this instrument was valid, why did they not use another measurement instrument such as the Copenhagen Burnout Inventory?

RESPONSE:

Thank you for the opportunity to clarify. The MBI originally came with all 3 subscales. While there is no universal cut-off score to define burnout, in a recent systematic review of burnout in healthcare professionals, burnout was defined using the cut-offs of only EE>27 and/or DP>10, with PA excluded, in the majority of participants. Furthermore, it has been postulated by Maslach that PA’s association has been more variable and complex, and it may be a function of EE and DP instead. Hence, we defined burnout according to the cut-offs of EE>27 and/or DP>10. We have included this explanation in the methodology to minimize confusion for the readers (Page 5, Paragraph 2).

We agree that other measurement instruments exist. Our analysis suggests that the overall measurement using MBI was reliable. The Cronbach’s alpha coefficients for EE, DP, and PA in MBI-HSS were 0.93, 0.81, and 0.85, respectively (Page 7, paragraph 2). Also, it is the most widely used measurement instrument. Nevertheless, we thank you for your suggestion and believe that other inventories like the Copenhagen Burnout Inventory can be explored in future studies to offer new insights into burnout. We have included this in the 2nd last line of the “Study Limitations” section on Page 17, Paragraph 2.

10. Questionnaire design and measurement: please establish the cut-off score of low, medium and high levels in the three dimensions, and not only the cut-off for high EE and D, and low PA.

RESPONSE:

Thank you for your comment. The cut offs are as follows:

EE High: >27, Moderate: 19-26, Low 0-18

DP High: >10, Moderate: 6-9, Low: 0-5

PA High: 0-33, Moderate: 34-39, Low: >40

We have removed this and the original figure 1 from the paper to improve clarity to readers, as the cutoffs were not meaningful for discussion in the subsequent analyses.

11. In table 1. Columns “With burnout and Without burnout” are not clear, what criteria did the authors follow to consider burnout, if they did not take PA into account? Also authors say: “The Cronbach’s alpha coefficients for EE, DP, and PA in MBI-HSS were 0.93, 0.81, and 0.85, respectively, suggesting that the overall measurement was reliable”, Is this data your this study? I understand that you collected PA data but you did not analyze it?

RESPONSE:

Thank you for pointing this out. We have decided to report on the overall self-reported burnout levels according to the pre-specified cut-offs of EE>27 and/or DP>10. Our rationale for using this cut-off is explained in question 8 of reviewer 3. For convenient reference: While there is no universal cut-off score to define burnout, in a recent systematic review of burnout in healthcare professionals, burnout was defined using the cut-offs of only EE>27 and/or DP>10, with PA excluded, in the majority of participants. Furthermore, it has been postulated by Maslach that PA’s association has been more variable and complex, and it may be a function of EE and DP instead. Hence, we defined burnout according to the cut-offs of EE>27 and/or DP>10. We have included this explanation in the methodology to minimize confusion for the readers on Page 5, Paragraph 2.

12. Discussion: “workplace interventions are crucial”. Please expand what kind of interventions could be carried out.

RESPONSE:

Thank you for the comment. We have expanded on interventions in our manuscript as per your suggestion. We have categorized our proposed interventions into personal and workplace suggestions and have expanded on each point within the manuscript.

Given that heavy self-reported workload and more extended work experience are associated with burnout in our study, we expanded on the kind of workplace interventions could be explored in future studies. Referencing previous studies, possible workplace strategies include “Protected time, proper shift allocations, flexibility in working structure, and adequate manpower distribution”.

Thank you for your suggestion. We believe the revision per your comment has made our proposed interventions clearer and more actionable.

PLoS One. doi: 10.1371/journal.pone.0244338.r003

Decision Letter 1

Jenny Wilkinson

30 Nov 2020

PONE-D-20-26669R1

Factors associated with self-reported burnout level in allied healthcare professionals in a tertiary hospital in Singapore

PLOS ONE

Dear Dr. Yi,

Please submit your revised manuscript by Jan 14 2021 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

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Additional Editor Comments (if provided):

Thank you for your responses to reviewers and accompanying manuscript changes. These has addressed the comments with one minor revision now needed. In the Abstract Results new abbreviations have been added which have not been previously explained (i.e. EE and DP)

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PLoS One. 2021 Jan 6;16(1):e0244338. doi: 10.1371/journal.pone.0244338.r004

Author response to Decision Letter 1

1 Dec 2020

Editor Comments:

RESPONSE: We apologized for the oversight. We have spelled out the abbreviations per the editor's advice. The sentence now read:

"The self-reported burnout level (emotional exhaustion>27 and/or depersonalization>10) was 67.4%."

Please see lines 42-43.

We have also proofread the revised manuscript with a few minor changes made. Please see the 'Revised manuscript with track changes_R2.'

PLoS One. doi: 10.1371/journal.pone.0244338.r005

Decision Letter 2

Jenny Wilkinson

8 Dec 2020

Factors associated with self-reported burnout level in allied healthcare professionals in a tertiary hospital in Singapore

PONE-D-20-26669R2

Dear Dr. Yi,

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.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

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Jenny Wilkinson, PhD

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

PLoS One. doi: 10.1371/journal.pone.0244338.r006

Acceptance letter

Jenny Wilkinson

14 Dec 2020

PONE-D-20-26669R2

Factors associated with self-reported burnout level in allied healthcare professionals in a tertiary hospital in Singapore

Dear Dr. Yi:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

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Factors associated with self-reported burnout level in allied healthcare professionals in a tertiary hospital in Singapore

Yao Hao Teo

Jordan Thet Ke Xu

Cowan Ho

Jui Min Leong

Benjamin Kye Jyn Tan

Elisabeth Ker Hsuen Tan

Wei-An Goh

Elson Neo

Jonathan Yu Jing Chua

Sean Jun Yi Ng

Julia Jie Yi Cheong

Jeff Yi-Fu Hwang

See Ming Lim

Thomas Soo

Judy Gek Khim Sng

Siyan Yi

Roles

Abstract

Background

Methods

Results

Conclusions

Introduction

Materials and methods

Study design and sampling

Inclusion and exclusion criteria

Questionnaire design and measurement

Data analyses

Ethical considerations

Results

Sociodemographic characteristics

Table 1. Sociodemographic characteristics of allied health professionals in a tertiary hospital in Singapore.

Burnout level and associated sociodemographic factors

Table 2. Sociodemographic characteristics of allied health professionals stratified by burnout levels.

AWS domains and association with burnout levels

Fig 1. Comparisons of the mean scores of the Areas of Worklife Survey domains stratified by burnout levels (n = 328).

AWS individual statements and association with burnout levels

Fig 2. Comparisons of difference in mean scores of Areas of Worklife Survey statements in all domains between participants with a high (n = 221) and low (n = 107) burnout level.

Factors associated with burnout levels

Table 3. Factors associated with burnout levels in a multiple logistic regression analysis.

Perceived barriers to seeking psychological help

Table 4. Perceived barriers to seeking psychological help among participants with a high burnout level (n = 130).

Discussion

Study limitations

Conclusions

Supporting information

Acknowledgments

Data Availability

Funding Statement

References

Decision Letter 0

Jenny Wilkinson

Roles

Author response to Decision Letter 0

Decision Letter 1

Jenny Wilkinson

Roles

Author response to Decision Letter 1

Decision Letter 2

Jenny Wilkinson

Roles

Acceptance letter

Jenny Wilkinson

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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