Prevalence of risk of distress and associated factors among physicians, nurses and rehabilitation therapists in a community hospital: a cross-sectional study

INTRODUCTION

Distress, which encompasses burnout, fatigue, low mental and physical quality of life (QOL), depression, anxiety and stress, is linked to negative outcomes for both staff and patients.[1,2,3] The mental health of healthcare staff can impair their competency, career satisfaction, professionalism and patients’ quality of care.[4,5,6,7] Distress is also associated with a higher risk of suicidal ideation,[8] alcohol abuse,[8] malpractice suits[9] and healthcare attrition.[10,11] Unfortunately, for many healthcare professionals, there are multiple barriers to seeking help. Healthcare professionals might be reluctant to acknowledge personal struggles. This may stem from a professional culture of stoicism, fear regarding potential implications to their career or perceived stigma; there may also be a lack of awareness of how their distress compares to other healthcare professionals.[12,13]

While studies have explored distress among healthcare professionals, they typically studied only one domain of distress as an outcome (e.g., depression, burnout, anxiety) and were focused on healthcare professionals in acute care settings[7,14,15,16,17] and primary care.[7,18,19,20] Moreover, the majority of these studies focused primarily on physicians and nurses. Few studies have been done exploring distress among rehabilitation therapists.[21,22,23] The prevalence of distress ranges 67%–88% among doctors,[17,18,19,23,24,25] 30%–80% among nurses[14,15,23,26,27] and 29%–59% among rehabilitation therapists.[21,22,23,28]

Distress is a multidimensional construct, however, there are no studies in the literature on the prevalence of distress as a multidimensional construct (e.g., in a variety of domains such as burnout, depression, stress, fatigue and low mental and physical QOL) among physicians, nurses and rehabilitation therapists working in community hospitals. Given the different work environment, patient profile, services provided and longer contact time with patients (average length of stay is 28 days) in community hospitals as compared to acute care settings and primary care, the prevalence of distress in healthcare professionals working in community hospitals might differ from those working in acute care or primary care settings. As argued by Pereda-Torales et al.[29] and Vargas et al.,[30] the prevalence of distress and any evidence of the protective and risk factors for distress are of great interest to the scientific community. Hence, further research and evidence is needed to decrease and prevent the risk of distress among healthcare professionals working in community hospitals.

Community hospitals cater to patients who require a short period of continuing care or subacute care, usually after discharge from acute hospitals, and provide medical, nursing and rehabilitative care for patients who require an extended period of recovery from their medical or surgical conditions. Community hospitals also assist patients with unique care needs such as complex wound care, dementia care and palliative care. Services provided by community hospitals help patients transition back to their homes and community, support engagement in their usual roles and responsibilities, and enable them to actively contribute to their society again through facilitating reorganisation of physical, functional, psychological and social needs.[31]

An average Singaporean’s life expectancy stands at 83.1 years currently, which is among the highest in the world.[32] This number is expected to increase further. By 2030, the percentage of Singaporeans aged 65 years and above is expected to double by 20%.[33] This increased lifespan implies that more Singaporeans will be living longer with chronic conditions, thus increasing the healthcare needs of Singapore significantly. To address the growing healthcare needs, Singapore’s government implemented a Healthcare 2020 Masterplan to expand the healthcare infrastructure.[34] Singapore’s Ministry of Health plans to transform healthcare service delivery by expanding the capacity of intermediate care facilities between 2015 and 2020.[34] The acute care-centric model that Singapore previously adopted is now moving towards a new model of care wherein community hospitals (intermediate care facilities) will play a larger role. Manpower needs in intermediate care facilities are expected to grow by four times, from the current 4,000 to 15,000 in 2020.

As the focus of care shifts towards community hospitals, it is anticipated that there will be an amplified demand for community hospital care as the population ages and experiences more chronic diseases and physical incapacity. This may increase the pressure on community hospital staff and contribute to additional distress, which can affect their health and the quality of care they provide. Hence, it is important to explore the prevalence of risk of distress and the associated factors of distress among community hospital staff. However, to our knowledge, no studies have been done in Singapore thus far. This will be the first study to report levels of distress among physicians, nurses and rehabilitation therapists in a community hospital. Findings may assist with understanding of contributors to distress and development of strategies to ameliorate distress found across this cohort.

METHODS

The aims of this study were twofold. First, we sought to evaluate the prevalence of risk of distress as a multidimensional construct (e.g. measured in a variety of domains such as burnout, depression, stress, fatigue and low mental and physical QOL) among physicians, nurses and rehabilitation therapists in a community hospital in Singapore. Second, we sought to investigate the influence of sociodemographic factors, health-related lifestyle factors and professional characteristics on the risk of distress.

A cross-sectional study was conducted among physicians, nurses and rehabilitation therapists in a 243-bedded community hospital located in the western region of Singapore, which receives most of its patients from all acute hospitals in Singapore and a minority are from private hospitals or nursing homes or are self-referrals. This community hospital provides inpatient rehabilitative care, subacute care (i.e., complex wound care, prolonged intravenous antibiotic therapy), palliative care, dementia care and chronic sick care. An anonymous survey was conducted via Microsoft Forms^® (https://forms.office.com) over a 3-month period from February 2019 to April 2019. The survey was voluntary, and consent was implied if participants responded. Approval was obtained from St Luke’s Hospital institutional review board (IRB-01-2019-01-03).

All physicians, nurses and rehabilitation therapists working in the community hospital were approached to participate in the study. According to previously published studies evaluating the risk of distress among healthcare professionals, the estimated overall prevalence of distress was approximately 55%.[17,23,24,25,28] We calculated that we would need a sample size of at least 95 participants based on this estimated overall distress prevalence of 55%, precision error of 10% and type 1 error of 5%.

The main outcome of interest in this study was distress. Risk of distress was assessed using the expanded Well-Being Index (eWBI) [Table 1]. The eWBI is a validated questionnaire intended to identify distress in a variety of dimensions, compared to instruments such as Beck Depression Inventory, Maslach Burnout, Beck Anxiety Inventory and State–Trait Anxiety Inventory, which are long, cumbersome to analyse and typically measure only one domain of distress (e.g., depression, burnout, anxiety).[17,27,28] Studies on physicians, nurses, medical students and a cohort of workers have shown that eWBI is a valid and reliable instrument to identify individuals in distress in a variety of countries, settings and occupations.[17,27,28,35,36,37] Dimensions of distress identified in eWBI include burnout, depression, stress, fatigue and mental and physical QOL. The eWBI is able to stratify the risk of distress and better identify those with higher overall well-being. For healthcare workers, the eWBI score correlates with meaning in work, high and low QOL, fatigue, burnout, recent suicidal ideation, intent to reduce work hours or turnover intention. Respondents were asked to answer seven yes/no items and two questions rated on a Likert scale. The total score for eWBI ranged from −2 (lowest risk) to 9 (highest risk). Physicians with an eWBI score ≥3[17] and nurses and rehabilitation therapists with an eWBI score ≥2 were at a greater risk of distress.[27,28]

Table 1.

Expanded well-being index (questions and scoring)

Scale	Points assigned
1. Have you felt burned out from your work?	Yes=1 No=0
2. Have you worried that your work is hardening you emotionally?	Yes=1 No=0
3. Have you often been bothered by feeling down, depressed, or hopeless?	Yes=1 No=0
4. Have you fallen asleep while sitting inactive in a public place?	Yes=1 No=0
5. Have you felt that all the things you had to do were piling up so high that you could not overcome them?	Yes=1 No=0
6. Have you been bothered by emotional problems (such as feeling anxious, depressed, or irritable)?	Yes=1 No=0
7. Has your physical health interfered with your ability to do your daily work at home and/or away from home?	Yes=1 No=0
8. The work I do is meaningful to me. • 7-point Likert scale • Very strongly disagree: 1 • Very strongly agree: 7	Low level of meaning in work (response option of 1 or 2)=+1 Neutral level of meaning in work (response option of 3 to 5)=0 High level of meaning in work (response option of a 6 or 7)=–1
9. My work schedule leaves me enough time for my personal/family life. • Strongly agree; agree; neutral; disagree; strongly disagree	Lower satisfaction with work-life integration (e.g. disagree; strongly disagree)=+1 Neutral=0 Higher satisfaction with work-life integration (agree, strongly agree)=–1

The total score for the expanded well-being index ranges from –2 (lowest risk) to 9 (highest risk)

Sociodemographic characteristics included age, gender, marital, number of children, number of children aged <5 years, access to childcare, highest education attained and religion. Health-related lifestyle characteristics include smoking and alcohol drinking habits, physical activity and sleeping habits.

Professional characteristics included occupational status, number of years spent working in a community hospital, working conditions (working hours/week, shift work, i.e., night shifts), time spent in nonpatient care activities (if applicable), amount of vacation time available and used, and absenteeism (workdays missed due to mental or physical illness).

Univariate comparisons of proportions, means and medians were done using Student’s t-test, Fisher’s exact test and chi-square test. Intercorrelations of the independent variables were examined using Pearson’s r test and Cramer’s V test before running the regression analyses. We related the probability of being distressed with the variables of interest using exact logistic regression models, initially considering each variable separately to evaluate the unadjusted association with distress (bivariate analysis) and subsequently introducing variables into a multivariate model to evaluate potentially independent associations when adjusting for confounders. The strength of the associations identified is presented as odds ratios (OR) together with their 95% confidence intervals (CI). All statistical tests were two sided. Statistical significance was defined as P < 0.05. All analyses were performed using Stata version 15.0 (StataCorp LP, College Station, TX, USA).

RESULTS

Of 297 eligible participants (19 physicians, 220 nurses, 58 rehabilitation therapists), 268 responded, giving an overall response rate of 90.5% (median age 35 years, 88.1% female). The specific response rates were as follows: physicians: 94.7%, nurses 88.2% and rehabilitation therapists 96.6%. Descriptive characteristics of responders are shown in Table 2.

Table 2.

Baseline characteristics of respondents (N=268).

Variable	n (%)/Mean±SD		Variable	n (%)/Mean±SD
	Not distressed (n=191)	Distressed (n=77)		Not distressed (n=191)	Distressed (n=77)
Sociodemographic			Professional characteristic
Age (yr)	38.3±9.9	34.4±8.5	Occupation
Gender			Nurse	146 (75.3)	48 (24.7)
Male	19 (59.4)	13 (40.6)	Physician	10 (55.6)	8 (44.4)
Female	172 (72.9)	64 (27.1)	Rehabilitation therapist	35 (62.5)	21 (37.5)
Marital status			Employment
Single	101 (73.7)	36 (26.3)	Full-time	180 (70.6)	75 (29.4)
Married	90 (68.7)	41 (31.3)	Part-time	11 (84.6)	2 (15.4)
No. of children	0.72±1.1	1.2±0.7	Palliative care
Have children aged <5 years			No	173 (70.6)	72 (29.4)
No	49 (84.5)	9 (15.5)	Yes	18 (78.3)	5 (21.7)
Yes	21 (65.6)	11 (34.4)	No. of years working in a CH	7.3±5.1	6.3±4.4
Childcare supporta			Work duration (h/wk)
No	25 (65.8)	13 (34.2)	<40	33 (82.5)	7 (17.5)
Yes	45 (86.5)	7 (13.5)	40–49	141 (73.4)	51 (26.6)
Religion			50–59	12 (50.0)	12 (50.0)
Christian	74 (66.7)	37 (33.3)	60–80	4 (40.0)	6 (60.0)
Catholic	38 (66.7)	19 (33.3)	≥80	1 (50.0)	1 (50.0)
Buddhist	45 (83.3)	9 (16.7)	Night shifts
Islam	5 (71.4)	2 (28.6)	No	63 (64.9)	34 (35.1)
Hinduism	11 (78.6)	3 (21.4)	Yes	128 (74.8)	43 (25.2)
Freethinker	18 (72.0)	7 (28.0)	Absenteeism in the past year (day)
Highest education level			<5	153 (75.0)	51 (25.0)
Secondary School (O/N Levels or equivalent)	16 (88.9)	2 (11.1)	5–9	28 (70.0)	12 (30.0)
Professional qualification/ITE/JC/diploma)	55 (74.3)	19 (25.7)	10–14	8 (47.1)	9 (52.9)
University degree	100 (69.9)	43 (30.1)	>14	2 (28.6)	5 (71.4)
Post-graduate degree	16 (88.9)	13 (39.4)	No. of night shifts (wk)	5.40±1.9	5.02±1.8
Health-related			Administrative work
Smoker			No	139 (78.5)	38 (21.5)
No	187 (70.8)	77 (29.2)	Yes	52 (57.1)	39 (42.9)
Yes	4 (100.0)	0 (0.0)	Administrative work (h/wk)
Drink alcohol			<5	25 (67.6)	12 (32.4)
No	184 (71.3)	74 (28.7)	5–9	6 (31.6)	13 (68.4)
Yes	7 (70.0)	3 (30.0)	10–14	16 (66.7)	8 (33.3)
Regular exercise			15–19	2 (40.0)	3 (60.0)
Yes	64 (68.1)	30 (31.9)	≥20	3 (50.0)	3 (50.0)
No	127 (73.0)	47 (27.0)	Vacation time taken in the past year (day)	18.9±4.6	10.2±2.2
Sleep (h/day)
>8	51 (87.9)	7 (12.1)
4–8	140 (66.2)	70 (33.8)

^aInclude infant care/childcare or having a domestic helper and having relatives who take care of your children. CH: community hospital, ITE: institute of technical education, JC: junior college, SD: standard deviation

The overall prevalence of risk of distress was 28.7% (physicians 44.4%, nurses 24.7%, rehabilitation therapists 37.5%; P = 0.062). Bivariate analysis showed significant association between number of children, having children less than 5 years of age, having childcare support, sleep hours/day, hours worked per week, doing administrative work, absenteeism and age [Table S1, Supplemental Digital Appendix]. Workers who were older (OR 0.95, 95% CI 0.92–0.98) had fewer odds of distress. Workers who had more children (OR 1.7, 95% CI 1.51–1.95), had children less than 5 years of age (OR 2.69, 95% CI 1.32–5.53), had 4–8 h of sleep per day (OR 3.64, 95% CI 1.57–8.44) and worked more hours in a week (50–59 h/week: OR 4.71, 95% CI 1.50–14.78; 60–80 h/week: OR 7.07, 95% CI 1.57–31.86) were more likely to report absenteeism (10–14 days/year: OR 3.38, 95% CI 1.24–9.21; >14 days/year: OR 7.5, 95% CI 1.41–39.8), and those who had administrative duties (OR 2.74, 95% CI 1.58–4.75) had greater odds of distress. A subgroup analysis of workers who had children revealed that those who did not have childcare support (OR 3.34, 95% CI 1.18–9.47) had higher odds of distress.

Multivariable logistic regression showed that after adjusting for age, gender, occupation, education and sleep, workers who were involved in administrative work in addition to patient care (adjusted OR 2.64, 95% CI 1.43–4.85) had greater odds of distress. In addition, workers who did not have childcare support (adjusted OR 3.24, 95% CI 1.92–10.52) had greater odds of distress [Table S2, Supplemental Digital Appendix].

DISCUSSION

This study was performed because of a lack of research, particularly in Singapore, on the prevalence of and contributors to distress among community hospital healthcare professionals, particularly physicians, nurses and rehabilitation therapists.

In our study, we found that the overall prevalence of distress (28.7%) was lower among healthcare professionals in the community hospital.[17,27,28] The prevalence of distress among community hospital physicians (44.4%), nurses (24.7%) and rehabilitation therapists (37.5%) was also lower compared to the rates reported in previous studies primarily conducted in acute and primary care physicians, nurses and rehabilitation therapists (29%–88%).[14,15,17,18,19,21,22,23,24,25,26,27,28] Community hospitals practice team-based care, which emphasises participatory decision-making, team cohesiveness and coordination of care as the focus is on supporting patient re-enablement and transition back to home and community. This more supportive work environment and longer time with patients might be a possible explanation for a lower prevalence of distress among community hospital healthcare professionals. Our Asian work values could have also played a contributary role in blunting the effect of distress. Previous studies have shown that Asian work values encompassing endurance, hard work, collectivism and guanxi (relation orientation, protecting others’ reputation and respecting social order) are positively associated with work well-being.[38,39] Asians may also be less likely to express distress in psychological terms, especially in cultures where the expression of emotional distress might be discouraged.[40]

The findings of previous studies were largely consistent with that of the current study; there were similar risk factors of distress, namely, administrative burden[41,42,43] and lack of childcare support.[44,45,46] Both factors are related to the conflict and pressure in trying to compromise and balance between competing time demands, such as clinical practice and family commitments, and are associated with increased turnover intention.[43,44] Excessive administrative duties divert focus and time from more clinically important activities of healthcare professionals, such as the provision of actual care to patients and improving the quality of care. Patient care might even be compromised if administrative duties prevent patients from receiving appropriate or timely treatment.[43] A study in Swiss healthcare professionals found that healthcare professionals providing informal care to dependent children (double-duty child caregivers) experienced the risk of distress due to difficulties in achieving work–life balance.[46]

These findings have important management implications in developing strategies to ameliorate distress. Priority should be given to reducing the amount of non-essential or unreasonable illegitimate administrative duties and role conflicts for healthcare professionals by reallocating their work capacity to core tasks. This might also serve to make hospital task management more resource efficient. The expansion of family-friendly services or childcare support is also necessary to allow healthcare professionals to successfully juggle both work and family.

This study differs and stands out from other studies. This study did not focus on a single health profession, as is usually the case, but was integrative with regards to the study population of healthcare professionals in a community hospital. This is also the first study to report levels of distress among physicians, nurses and rehabilitation therapists in a community hospital. We also used a well-established and validated measure to ensure the validity and reliability of the study findings.

Besides these strengths, this study also has some limitations. Due to its cross-sectional design, it does not allow for causality to be inferred. As participants self-selected to participate, the study was subject to selection and response bias. The number of cases of physicians was not sufficiently large to allow for stratified and multivariate association analyses simultaneously (comparisons between different health professions and for adjustments for possible confounders). Our results might have been affected by a type II error (failure to detect a true difference) due to the small number of samples in certain categories (e.g. physicians in the occupational status variable). Finally, community hospitals are heterogeneous and may have unique characteristics in terms of services provided, work scope and patient needs. This single-centre study may not be generalised to the entire community hospital sector.

Future research should use a longitudinal study design in multiple community hospitals to disentangle the complex temporal structure that connects each factor to the others in the professional lives of community hospital healthcare professionals. As this study was done in 2019 before the coronavirus disease 2019 (COVID-19) pandemic, it would be interesting to study whether the prevalence rates of distress and its associating factors in prepandemic times differed from the rates during the pandemic and postpandemic times, given the unique stressors experienced by healthcare workers and their families during COVID-19.

In conclusion, this study contributed to the knowledge on prevalence and understanding of distress among healthcare professionals in a community hospital setting in Singapore. The inability to balance, integrate or reconcile administrative duties and lack of childcare support with competing demands of clinical work and family life are associated with higher risk of distress. We hope that our findings can stimulate community hospitals to check for distress among staff and inform strategies by healthcare administrators to the risks of distress.

Financial support and sponsorship

Nil.

Conflicts of interest

There are no conflicts of interest.

Supplemental digital content

Appendix at http://links.lww.com/SGMJ/A85

APPENDIX

Table SI.

Bivariate analysis of variables associated with distress

Variable Socio-demographics		OR	95%CI	p value	Variable Professional Characteristics		OR	95%CI	p value
Age (years)		0.95	0.92-0.98	0.003	Occupation	Overall			0.062
Gender	Male	1
	Female	0.50	0.25-1.16	0.117		Nurse	1
Marital	Single	1				Physician	2.43	0.91-6.52	0.077
Status	Married	1.24	0.73-2.11	0.430		Rehabilitation therapist	1.83	0.97-3.43	0.062
Number of children		1.70	1.51-1.95	0.021	Employed	Full-time	1
Children < 5	No	1				Part-time	0.44	0.09-2.02	0.288
years of age	Yes	2.69	1.32-5.53	0.007	Palliative care	No	1
Childcare	Yes	1				Yes	0.67	0.24-1.87	0.441
	No	3.34	1.18-9.47	0.020	Number of years working in a CH		0.96	0.90-1.01	0.131
Religion	Overall			0.272	Hours worked/week	Overall			0.014
	Christian	1				<40	1
	Catholic	1	0.51-1.97	1.000		40-49	1.71	0.71-4.10	0.233
	Buddhist	0.40	0.18-0.91	0.028		50-59	4.71	1.50-14.78	0.008
	Islam	0.80	0.15-4.32	0.795		60-80	7.07	1.57-31.86	0.011
	Hinduism	0.55	0.14-2.08	0.374		>80	4.71	0.29-84.8	0.293
	Free-thinker	0.88	0.33-2.31	0.788	Night shifts	No	1
Highest level of education completed	Overall			0.141		Yes	1.61	0.93-2.76	0.087
	Secondary School i.e O’ or N’levels/Equivalent	1			No of night shifts		0.90	0.74-1.08	0.249
	Professional qualification/ITE/Junior	2.76	0.58-13.1	0.201	Administrative work		2.74	1.58-4.75	<0.0005
	College/Diploma/Equivalent
	University Degree	3.44	0.76-15.62	0.109	Hours, administrative work/week	Overall			0.079
	Post-graduate Degree	5.20	1.02-26.47	0.047		<5	1
Health-related lifestyle						5-9	4.51	1.38-14.80	0.013
Drinks alcohol	No	1				10-14	1.04	0.35-3.11	0.942
	Yes	1.07	0.27-4.23	0.928		15-19	3.13	0.46-21.25	0.244
Regular exercise	Yes	1				>20	2.08	0.36-11.89	0.409
	No	1.27	0.73-2.19	0.398	Sick leave taken, past year	Overall			0.010
Sleep, hours/day	> 8		1			<5	1
	4-8	3.64	1.57-8.44	0.003		5-9	1.29	0.61-2.71	0.510
						10-14	3.38	1.24-9.21	0.018
						>14	7.50	1.41-39.8	0.018
					Vacation time taken, past yea		1.02	0.96-1.08	0.554

OR: odds ratio 95%CI: 95 percent confidence interval

Table S2.

Multivariate factors associated with distress

All Participants (N = 268)
Variable	Adjusted OR	95% CI	p value
Administrative work+	2.64	1.43-4.85	0.002
Subgroup Analysis of Participants with Children (N= 90)
Variable	Adjusted OR	95% CI	p value
No childcare support ß	3.24	1.92-10.52	0.040

⁺ Adjusted for age, gender, occupation, education, number of years working in a CH and sleep ^ß Adjusted for age, gender, occupation, education, sleep, marital status, number of children and children < 5 years of age