Abstract
This is the first multicenter survey to clarify occupational environments and health and stress status in various medical professionals (MPs). The survey questionnaire included the General Health Questionnaire (GHQ), Effort-Reward Imbalance (ERI), Quality of Working Life (QWL), medical incidents, and demographic data. We collected 3,335 questionnaires from 7,698 MPs and analyzed 3,036 of them. The MPs were categorized into 6 groups: nurses (n = 1,821); physicians (706); dentists (83); pharmacists (77); MPs involved in disease diagnoses (MP-diagnosis), including clinical laboratory technicians and radiographers (261); and MPs involved in patient treatment (MP-treatment), including physical therapists, occupational therapists, speech therapists, and other therapists (88). Working hours were the longest for physicians, and the night shift work was the greatest for nurses. Mental health (GHQ) was the worst in nurses and was the best in physicians. ERI was worse in nurses and MP-treatment than in other occupations. QWL in maintaining personal values was the worst in physicians. Different health/stress statuses must be considered when assisting MPs and forming policy guidelines.
Introduction
“Working style reform” is a Japanese government priority policy that aims to create a society that enables various work styles with a good work-life balance. The latest White Paper data on Health, Labor, and Welfare reported that the number of Japanese medical professionals (MPs) in Japan are approximately 1.3 million (Table 1) [1]. Despite the increasing number of MPs in all occupations, low retention rates of physicians and nurses have been reported, indicating a chronic human resource shortage in the medical field. According to a survey by the Organization for Economic Co-operation and Development (OECD), Japan has 2.5 physicians per 1,000 people, ranking 33rd out of 38 OECD countries and last among the G7 nations [2]. The number of nurses per 1,000 people is 11.8, higher than the OECD average and among the top nine OECD countries, although nursing incomes in Japan were lower than the OECD average [2].
Table 1. Detailed number of distributed and analyzed questionnaires in this study
|
In Japan*
|
At 5 Universities
|
| Number of MPs
|
Number of medical professionals based on the database**
|
|
Distributed numbers
|
Received numbers
|
Response rate (%)
|
Analyzed numbers
|
Valid response rate (%)
|
| Total N |
Total
(6 groups)
|
(%) |
Kyushu U |
UOEH |
Saga U |
Kurume U |
Fukuoka U |
Total |
Total (6 groups) |
(%) |
Total
(6 groups)
|
(%) |
Total (6 groups) |
(%) |
Total (6 groups) |
(%) |
| Nurse
|
1,312,396 |
1,312,396 |
54.4 |
1,240 |
860 |
642 |
1,095 |
965 |
4,802 |
4,802 |
52.9 |
4,802 |
62.4 |
1,966 |
59.0 |
40.9 |
1,821 |
60.0 |
37.9 |
| Physician
|
311,963 |
311,963 |
12.9 |
666 |
435 |
374 |
522 |
550 |
2,547 |
2,547 |
28.1 |
1,705 |
22.1 |
719 |
21.6 |
42.2 |
706 |
23.3 |
41.4 |
| Dentist
|
101,777 |
101,777 |
4.2 |
195 |
12 |
13 |
12 |
15 |
247 |
247 |
2.7 |
220 |
2.9 |
80 |
2.4 |
36.4 |
83 |
2.7 |
37.7 |
| Pharmacist
|
240,371 |
240,371 |
10.0 |
97 |
47 |
39 |
65 |
71 |
319 |
319 |
3.5 |
195 |
2.5 |
84 |
2.5 |
43.1 |
77 |
2.5 |
39.5 |
| Laboratory technician
|
66,866 |
121,079 |
5.0 |
113 |
50 |
39 |
81 |
72 |
355 |
604 |
6.7 |
463 |
6.0 |
272 |
8.2 |
58.7 |
261 |
8.6 |
56.4 |
| Radiographer
|
54,213 |
68 |
44 |
29 |
58 |
50 |
249 |
| Clinical engineer,
|
28,043 |
325,747 |
13.5 |
26 |
21 |
12 |
27 |
14 |
100 |
560 |
6.2 |
313 |
4.1 |
214 |
6.4 |
68.4 |
88 |
2.9 |
28.1 |
| Physical therapist
|
91,695 |
23 |
19 |
16 |
26 |
23 |
107 |
| Occupational therapist
|
47,852 |
8 |
8 |
4 |
11 |
8 |
39 |
| Speech therapist
|
16,639 |
n.a. |
n.a. |
n.a. |
n.a. |
n.a. |
n.a. |
| Nutritionist
|
n.a. |
11 |
8 |
10 |
13 |
14 |
56 |
| Orthoptist
|
8,889 |
10 |
6 |
3 |
9 |
5 |
33 |
| Oral hygienist
|
132,629 |
23 |
2 |
1 |
5 |
5 |
36 |
| Others
|
n.a. |
39 |
20 |
43 |
46 |
41 |
189 |
| Total or average
|
2,413,333 |
2,413,333 |
100 |
2,519 |
1,532 |
1,225 |
1,970 |
1,833 |
9,079 |
9,079 |
100 |
7,698 |
100 |
3,335 |
100 |
average: 43.3 |
3,036 |
100 |
average: 42.6 |
* Database of 2020 White Paper on Health, Labor, and Welfare. **Database of the Kyushu Regional Bureau of Health and Welfare. MPs: Medical professionals, N: Number, U: University, UOEH: University of Occupational and Environmental Health, Japan, n.a.: not available
“Research on supply and demand based on the actual work situation considering the specialty of doctors” reported that full-time male workers worked an average of 57 hours and 35 minutes per week, while female doctors worked an average of 52 hours and 16 minutes per week [3]. Average working hours are gradually decreasing but remain significantly longer than other MPs. At approximately 35 years old, 26.2% female doctors left their jobs [4], while nurses have a reported 17.7% turnover rate within 1–3 years of employment [5]. Burnout among MPs was reported to be associated with high workload a work-life imbalance [6].
Since 2007, numerous Japanese University hospitals have attempted to support health promotion in medical employees and to improve their environments. In medical settings, where a multidisciplinary approach has become mainstream, fostering good relationships between MPs and providing decent working environments can reduce medical incidents or accidents, ultimately improving patient outcomes. However, few studies have yet examined mental health, work environment and stress, and many other factors in not only nurses and physicians but also other MPs in multicenter settings.
We conducted a multicenter survey to determine the occupational differences, actual working conditions, health status, and stress in the various MPs. The purpose of this study was to clarify the current working and living conditions of various MPs and to determine whether there were differences in the physical and mental health status and work-related stress of the MPs, based on the extensive data collected by job category at five University hospitals. The findings of this study would provide basic data for analyzing factors necessary to improve the work environment, work-life balance, and physical and mental health of MPs. The hypothesis of this study was that the health status and work stress of nurses might be worse than those of other MPs. Detailed analyses and discussions on the causes of differences among different occupations will be conducted in a future study. This study will give us a basic resource for future policy advocacy and consideration on the well-being of diverse MPs working at the hospitals.
Methods
This multicenter cross-sectional study was conducted in MPs at five University hospitals in northern Kyushu, Japan, from March to November, 2020.
Facilities and Participants
Questionnaires were distributed to 7,698 MPs at five University hospitals with 9,079 MPs (Table 1). During the COVID-19 pandemic, questionnaires were limited to a portion of MPs at two University hospitals, while all MPs at the three other hospitals participated in the survey. Participants were classified into six groups based on different professions, presence of night shifts, with or without patient care, and responsibilities: nurses; physicians; dentists; pharmacists; MPs involved in disease diagnosis (MP-diagnosis), including clinical laboratory technicians and radiographers; and MPs involved in patient treatment (MP-treatment), including physical therapists, occupational therapists, speech therapists, clinical engineers, nutritionists, orthoptists, oral hygienists, and licensed psychologists.
Procedures
Web- and paper-based self-administered anonymous surveys were conducted at five universities. The web-based survey utilized the Kyushu University Questionnaire system, guaranteeing security and preventing information leakage. Due to the varying spread of COVID-19, we could not conduct the survey simultaneously at all of the University Hospitals. Thus, the survey period spanned from March to November, 2020, with the duration at each hospital ranging from 1 to 3 months.
Measurement of the variables
The self-administered questionnaire included demographic variables (e.g., age, gender, and educational level), basic working conditions and environments, experiences of minor medical and occupational accidents in the past year, and questions about emotional, informational, and actual assistance availability. The instruments used in this questionnaire were the Japanese versions of the Goldberg General Health Questionnaire 30-items (GHQ-30) for assessing mental health as well as general health, Effort–Reward Imbalance (ERI) for measuring working stress, and Quality of Working Life (QWL) questionnaire for considering stress of lifestyle in maintaining personal values and social supports.
The GHQ-30 is a proven assessment tool for identifying non-psychotic and minor psychiatric disorders through 30 self-assessment items. This questionnaire is used in psychiatric patients and in the general population within the community or in non-psychiatric clinics [7, 8]. Each item is accompanied by four responses: ‘not at all,’ ‘no more than usual,’ ‘rather more than usual,’ and ‘much more than usual.’ We used the Japanese version, which was previously validated using a scoring method by Goldberg (0–0–1–1) with a 30-point maximum score [9]. When considering the total points, the questionnaire can be viewed as a one-dimensional tool that can isolate specific items, including anxiety and depression, interpersonal relations, and general functioning. We used the total points as the overall health status in this study. The presence of health problems was defined as a GHQ score ≥ 7 in the original study [10]; however, we used another threshold, ≥ 8 points, for comparison. A GHQ score of 17 or higher was defined for situations requiring treatment or assistance in the original paper [10]. In addition to the GHQ score, we asked the respondents about subjective health perceptions as excellent, good, fair, or poor.
The original ERI questionnaire consisted of two components: a “situation-specific component” focusing on effort and reward, and a “person-specific” component addressing overcommitment [11]. A Japanese version of the ERI questionnaire was developed and evaluated for reliability and validity [12]. In this study, we exclusively focused on the situation-specific component, considering the overall questionnaire volume. It evaluated effort using six items measuring quantitative (three items) and qualitative (one item) workloads, increase in total load over time (one item), and physical load (one item). The effort scale was scored on a 5-point scale ranging from 6 to 30, with higher scores indicating greater effort. Rewards were measured using eleven items: four affirmative items for distress (e.g., “My job security is poor”), and seven non-affirmative items (e.g., “I receive the respect I deserve from my superiors”). Participants responded to the statements on a 5-point scale. The reward scale scores ranged from 11 to 55, with higher scores indicating greater rewards. The effort-reward (ER) ratio, which evaluated the job effort-reward balance, was calculated by dividing the sum of the six effort items by the eleven reward items (effort/reward). The resulting score was multiplied by a correction factor (11/6) to adjust for unequal question numbers between the scales. An ER ratio of 1 indicated balance, while a ratio > 1 indicated a critical ER level [12].
We used a previously developed questionnaire, the QWL questionnaire, to assess stress in work environments and to evaluate working-life quality in female medical and healthcare professionals [13]. This questionnaire consisted of four key traits using the Salutogenic Model of Health, as depicted in Table 2. [14, 15]: trait (1) female-specific stress in continuing a career, trait (2) stress of lifestyle in maintaining personal values, trait (3) job satisfaction, and trait (4) social support network. Trait (1) was excluded because it was to evaluate only the female gender, and this study evaluated both genders. The other excluded trait scale was trait (3), which overlapped with other parts of this questionnaire. Thus, we used two traits: trait (2) and (4) subscales independently according to the study’s purpose, because the previous validation study indicated the independence of each trait subscale. Trait (2) included eight items measuring stress while maintaining personal values. We categorized the eight items into three groups: group (1) having no spare time for hobbies: item #1, for family and friends: item #2, and for oneself: item #3, group (2) cannot do healthy activities: item #4, cannot think of their future: item #5, and cannot maintain a balanced lifestyle: item #6, and (3) feeling difficulty in setting their schedule: item #7, and living at their desired pace: item #8. Responses were on a 5-point scale ranging from 1 (never) to 5 (frequently). Higher scores indicated higher stress levels. Trait (4) included seven items related to personal support resources, covering both practical and emotional issues. Responses were scored on a 6-point scale ranging from 1 (completely unsatisfied) to 6 (completely satisfied), with higher scores indicating greater personal support satisfaction levels. Additionally, we categorized the seven items into three groups: group (1) (four items: item #1, 2, 6, 7) including emotional support, group (2) (two items: item #4, 5) including informational support, and group (3) (one item: item #3) including actual assistance.
Table 2. Quality of working life for female medical and healthcare professional’s questionnaire
| Female-specific stress in continuing a career: FSS (5-Point Likert Scale)
|
| 1 Feel uneasy hearing “women quit their jobs easily” |
| 2 Feel uneasy hearing “women lack a certain attitude toward work” |
| 3 Feel uneasy hearing “women should quit their jobs and take care of the home” |
| 4 Feel less appreciated on the job because you are a woman |
| 5 Feel receiving slower promotion because you are a woman |
| 6 Feel not trusted to do the same job as a man because you are a woman |
| 7 Feel uneasy about having your work interrupted for a family reason |
| 8 Feel difficulty in the inability to attend training (conferences, seminars) for family reasons |
| 9 Colleagues do not help you to balance work and family |
| 10 Family members do not help you to balance work and family |
| 11 Feel uneasy about leaving the office early for family reasons |
| 12 Feel difficulty taking holidays for family reasons |
| Stress of lifestyle in maintaining personal values: SL (5-Point Likert Scale)
|
| 1 Have no time for your hobbies |
| 2 Have no time to talk with your family and friends properly |
| 3 Have no personal free time |
| 4 Cannot do personally healthy activities |
| 5 Lack of emotional capacity to consider your future |
| 6 Cannot maintain a balanced lifestyle |
| 7 Feel difficulty in setting your schedule |
| 8 Feel difficulty in living a life at your own desired pace |
| Job Satisfaction: JS (5-Point Likert Scale)
|
| 1 Wage and bonuses |
| 2 Substance and quality of your work |
| 3 Amount of your work |
| 4 Ease of taking holidays |
| 5 Length of working hours |
| 6 Position in the organization |
| 7 Speed of promotion |
| 8 Relationship with superiors |
| 9 Relationship with colleagues |
| 10 Opportunities to personally grow through your work |
| 11 Sense of achievement |
| 12 Size of responsibility and authority |
| 13 Personal welfare |
| Social support network: SSN (6-Point Likert Scale)
|
| 1 Have someone to tell your troubles to |
| 2 Have someone to consult with about your life and work |
| 3 Have someone to ask for help when you’re busy |
| 4 Have someone to give you useful advice on continuing work |
| 5 Have someone to give you the necessary information to balance your work and family |
| 6 Have someone to understand you to help you continue your career |
| 7 Have someone to help relieve your stress |
Based on Ref. 13
Statistical analyses
Categorical variables in demographic data such as the participant’s gender, education, working conditions, working positions, marriage, and presence of children were descriptively shown as numbers and percentage. We used chi-square tests to show the differences in the health status and the work stress among the 6 job categories or between genders. The health status was evaluated by GHQ, subjective health, and sleep time/satisfaction, and the work stress was evaluated by effort-reward imbalance and medical incidents/accidents. The purpose of chi-square tests was to evaluate the hypothesis that the health status and the work stress of nurses might be worse than those of other MPs. Continuous data such as age, GHQ-30 scores, ERI, and QWL were not normally distributed by the Kolmogorov-Smirnov test, so the Kruskal-Wallis equal group rank test was used to compare differences among the six groups. These data were expressed as medians and 5th percentile-95th percentiles rather than medians and quartiles, because the differences were difficult to recognize in medians and quartiles. Multiple comparisons among the non-normally distributed data were adjusted using the Bonferroni correction as the post hoc analyses. Statistical analyses were performed using STATA v.15.1 (Stata-Corp, TX, USA) with a two-tailed priori p-value < 0.05 considered statistically significant.
Ethical approval
Ethical approval for the study was obtained from the Kyushu University Institutional Review Board for Clinical Research and those at four other universities. The front page of each questionnaire included a written explanation of the study objectives, risks, benefits, and measures to ensure participant confidentiality. The Ethical Committees of four Universities approved that the informed consent (IC) was implied by return of a completed questionnaire. However, IC was obtained from participants at the University of Occupational and Environmental Health, Japan (UOEH) Hospital based on their ethical review board judgement. We made information about this study available to all of the universities on their websites so that participants could opt out.
Results
Collection rate and valid responses
Of 7,698 participants from five universities, 3,335 responded to the survey (43.3% response rate), as shown in Figure 1. There were 1,822 paper version responses and 1,513 web version replies. A total of 216 participants with missing items in their answers and 83 respondents (80 respondents without stated occupations and three who were cooks) were excluded. Thus, the study included 3,036 participants (42.6% valid response rate). Response rates and valid response rates by each occupation are shown in Table 1.
Basic characteristics
Table 3 demonstrates the median participant age as 35.0 years, with nurses (32.0 years) being significantly the youngest and physicians (39.0 years) the oldest among the six groups (Kruskal–Wallis: P=0.0001). The nurses were predominantly female (less than 8% males), while the physicians were over 70% male (P <0.0001). The dentists, pharmacists, and the MP-diagnosis group had equal representation of both sexes.
Table 3. Basic characteristics and working environment
|
Total
|
Nurse
|
Physician
|
Dentist
|
Pharmacist
|
MP-diagnosis
|
MP-treatment
|
P-value
|
Analyzed N
|
| Number, N(%)
|
3,036 (100) |
1,821 (60) |
706 (23) |
83 (2.7) |
77 (2.5) |
261 (8.6) |
88 (2.9) |
– |
|
| Age, yr
|
35.0 (23.0-56.0)
[36.2±10.2]
|
32.0 (23.0-55.0)
[34.6±10.2]
|
39.0 (28.0-58.0) ***
[40.1±8.9]
|
35.0 (27.0-58.0) **
[38.4±10.4]
|
35.0 (25.0-50.0) †
[36.0±8.5]
|
34.0 (23.0-58.0) ** ‡
[36.9±11.1]
|
35.0 (23.0-52.0) ‡
[35.3±9.6]
|
0.0001 |
3,036 |
| Male, N (%)
|
931 (30.7) |
140 (7.7) |
511 (72.4) |
47 (56.6) |
40 (52.0) |
137 (52.5) |
56 (63.6) |
<0.0001 |
3,036 |
| Education, N (%)
|
|
|
|
|
|
|
|
|
|
| Higher professional school (5 yr) |
51 (1.7) |
51 (2.8) |
0 (0.0) |
0 (0.0) |
0 (0.0) |
0 (0.0) |
0 (0.0) |
|
|
| Vocational school |
593 (19.6) |
513 (28.2) |
0 (0.0) |
0 (0.0) |
0 (0.0) |
52 (19.9) |
28 (31.8) |
|
|
| College |
186 (6.1) |
139 (7.6) |
0 (0.0) |
0 (0.0) |
0 (0.0) |
46 (17.6) |
1 (1.1) |
|
|
| University |
1,679 (55.4) |
1,043 (57.3) |
418 (59.4) |
26 (31.3) |
48 (62.3) |
106 (40.6) |
38 (43.2) |
|
|
| Graduate school (Master) |
163 (5.4) |
67 (3.7) |
11 (1.6) |
1 (1.2) |
20 (26.0) |
48 (18.4) |
16 (18.2) |
|
|
| Graduate school (Doctor) |
360 (11.9) |
6 (0.3) |
275 (39.1) |
56 (67.5) |
9 (11.7) |
9 (3.5) |
5 (5.7) |
<0.0001 |
3,032 |
| Employment contract, N (%)
|
|
|
|
|
|
|
|
|
|
| Full time |
2,762 (91.2) |
1,711 (94.1) |
583 (83.1) |
58 (69.9) |
72 (93.5) |
253 (96.9) |
85 (96.6) |
|
|
| Temporary part-time |
194 (6.4) |
92 (5.1) |
67 (9.5) |
21 (25.3) |
4 (5.2) |
8 (3.1) |
2 (2.3) |
|
|
| Discretionary work |
73 (2.4) |
15 (0.8) |
52 (7.4) |
4 (4.8) |
1 (1.3) |
0 (0.0) |
1 (1.1) |
<0.0001 |
3,029 |
| Working days and hours
|
|
|
|
|
|
|
|
|
|
| Working days/month, d |
20.0 (12.0-25.0)
[20.0±3.6]
|
20.0 (11.0-23.0)
[19.5±3.3]
|
21.0 (12.0-28.0) ***
[21.2±4.6]
|
20.0 (12.0-25.0) ‡
[19.2±3.9]
|
20.0 (20.0-23.0) *
[20.7±1.5]
|
20.0 (20.0-23.0) *** #
[20.7±1.4]
|
20.0 (20.0-22.0)
[20.5±2.2]
|
0.0001 |
3,014 |
| Night-time working days/month, d |
3.0 (0.0-6.0)
[2.9±2.5]
|
4.0 (0.0-6.0)
[3.6±2.3]
|
2.0 (0.0-6.0) ***
[2.2±2.7]
|
0.0 (0.0-6.0) *** ‡
[1.0±2.0]
|
1.0 (0.0-3.0) *** † ¶
[1.2±0.9]
|
1.0 (0.0-3.0) *** ‡
[1.0±1.2]
|
0.0 (0.0-4.0) *** §
[1.3±1.7]
|
0.0001 |
2,987 |
| Average working hours/week, h |
41.0 (30.0-65.0)
[44.8±11.1]
|
40.0 (31.0-55.0)
[42.6±7.7]
|
50.0 (27.0-80.0) ***
[51.4±16.0]
|
40.0 (11.0-80.0) ‡
[45.4±18.6]
|
45.0 (35.0-55.0) ‡
[45.0±6.7]
|
40.0 (36.0-52.0) ‡
[42.7±6.6]
|
40.5 (37.0-52.0) ‡
[43.3±7.4]
|
0.0001 |
3,012 |
| Wage system, N (%)
|
|
|
|
|
|
|
|
|
|
| Fixed wage |
2.551 (88.5) |
1,655 (95.2) |
493 (74.0) |
45 (57.7) |
70 (98.6) |
224 (89.6) |
64 (80.0) |
|
|
| Per hour wage |
109 (3.8) |
49 (2.8) |
37 (5.6) |
11 (14.1) |
1 (1.4) |
7 (2.8) |
4 (5.0) |
|
|
| Daily wage |
200 (6.9) |
27 (1.6) |
123 (18.5) |
21 (26.9) |
0 (0.0) |
19 (7.6) |
10 (12.5) |
|
|
| Others |
24 (0.8) |
8 (0.5) |
13 (2.0) |
1 (1.3) |
0 (0.0) |
0 (0.0) |
2 (2.5) |
<0.0001 |
2,884 |
| Position, N (%)
|
|
|
|
|
|
|
|
|
|
| Manager |
238 (7.9) |
94 (5.2) |
106 (15.1) |
12 (14.8) |
7 (9.1) |
11 (4.3) |
8 (9.3) |
|
|
| Middle-level manager |
614 (20.3) |
157 (8.6) |
351 (49.9) |
31 (38.3) |
15 (19.5) |
46 (17.8) |
14 (16.3) |
|
|
| No specific position |
2,119 (70.1) |
1,542 (84.8) |
235 (33.4) |
36 (44.4) |
53 (68.8) |
191 (73.8) |
62 (72.1) |
|
|
| Unclassified |
53 (1.8) |
25 (1.4) |
11 (1.6) |
2 (2.5) |
2 (2.6) |
11 (4.3) |
2 (2.3) |
<0.0001 |
3,024 |
MP-diagnosis: medical professionals involved in the diagnosis of patients, including radiographers and laboratory technicians; MP-treatment: medical professionals involved in the treatment of patients, including physical therapists, occupational therapists, etc. N: Number, MP: Medical professionals, yr: Year, d: Days, h: Hours.
For the continuous data, in each column, upper and lower value means median (5 percentile-95 percentile) and mean ± standard deviation, respectively.
vs Nurse, *: P<0.05; **: P<0.01; ***: P<0.001, vs Physician, §: P<0.05; †: P<0.01; ‡: P<0.001, vs Dentist, #: P<0.05; ¶: P<0.01
The physicians, dentists, and pharmacists had 6-year degrees, with dentists having the highest percentage of doctoral degrees (68%), followed by physicians (about 40%) and pharmacists (about 12%). Approximately 60% of the nurses had 4-year college or University degrees, which was significantly higher than the 40% range for the MP-diagnosis and -treatment groups (P<0.0001). Approximately 30% of the MP-treatment group were vocational school graduates.
Over 90% of the nurses, pharmacists, MP-diagnosis, and MP-treatment were full-time employees, while physicians (83.1%) and dentists (69.9%) comprised significantly fewer full-time employees than nurses (P<0.0001). Furthermore, a quarter of dentists worked fewer hours on a part-time basis. Among the full-time employees, the physicians significantly had the highest number of days worked per month (21.0 days/month) and hours worked per week (40.0 hour/week) (Kruskal–Wallis: P=0.0001, both). The Nurses had the highest proportion of night-shift work (4.0 days/month), followed by physicians (2.0 days/month) (Kruskal–Wallis: P=0.0001). The dentists, who often worked part-time, were the most likely to have a salary structure primarily based on daily and hourly salaries.
Approximately ≥ 70% of the nurses, pharmacists, those in the MP-diagnosis and -treatment groups were employed without specific positions. Approximately 50% of the physicians and 40% of the dentists held middle-level management positions, while 15% were in management positions, indicating a higher management personnel prevalence in these occupations than in others.
Table 4 reveals that the physicians significantly had the highest married individual proportion (approximately 80%), while nurses had the lowest (36%) (P<0.0001). The other occupational groups had an almost 50% proportion of married individuals. When considering the results by sex for each occupation, males were significantly more likely to be married than females (P<0.0001 or P=0.007). Male physicians had the highest percentage of married individuals, approximately 90%, while other occupations had around 70%. Among the female respondents, approximately 60% of the physicians were married, while 30% of the other MPs were married. Furthermore, approximately 70% of the physicians had children, while the overall respondent proportion was around 40%. Nurses had the lowest rate of having children, at 30%. Among men, approximately 80% of the physicians had children, while the percentage was lower for other male medical professions, ranging around 50% (P<0.0001). Among women, approximately 50% of the physicians had children, which was significantly lower than that for male physicians but higher than women in the other medical occupations (20–30%) (P<0.0001).
Table 4. Marriage status and presence of children
|
Total
|
Nurse
|
Physician
|
Dentist
|
Pharmacist
|
MP-diagnosis
|
MP-treatment
|
P-value
|
Analyzed N
|
| Marriage, N (%)
|
|
|
|
|
|
|
|
|
|
| Total
|
3,036 |
1,821 |
706 |
83 |
77 |
261 |
88 |
<0.0001 |
3030 |
| Married |
1,500 (49.5) |
661 (36.4) |
575 (81.7) |
46 (55.4) |
41 (53.3) |
131 (50.4) |
46 (52.3) |
| Single |
1,451 (47.9) |
1,102 (60.6) |
117 (16.6) |
37 (44.6) |
120 (45.5) |
120 (46.2) |
40 (45.5) |
| Widowed/Divorced |
79 (2.6) |
55 (3.0) |
12 (1.7) |
0 (0.0) |
1 (1.3) |
9 (3.5) |
2 (2.3) |
|
|
|
|
|
|
|
|
|
|
| Male
|
931 |
140 |
511 |
47 |
40 |
137 |
56 |
<0.0001 |
929 |
| Married |
740 (79.7) |
96 (68.6) |
453 (88.8) |
34 (72.3) |
28 (70.0) |
92 (67.7) |
37 (66.1) |
| Single |
179 (19.3) |
41 (29.3) |
52 (10.2) |
13 (27.7) |
12 (30.0) |
43 (31.6) |
18 (32.1) |
| Widowed/Divorced |
10 (1.1) |
3 (2.1) |
5 (1.0) |
0 (0.0) |
0 (0.0) |
1 (0.7) |
1 (1.8) |
|
|
|
|
|
|
|
|
|
|
| Female
|
2,105 |
1,681 |
195 |
36 |
37 |
124 |
32 |
<0.0001 |
2101 |
| Married |
760 (36.2) |
565 (33.7) |
122 (62.9) |
12 (33.3) |
13 (35.1) |
39 (31.5) |
9 (28.1) |
| Single |
1,272 (60.5) |
1,061 (63.2) |
65 (33.5) |
24 (66.7) |
23 (62.2) |
77 (62.1) |
22 (68.8) |
| Widowed/Divorced |
69 (3.3) |
52 (3.1) |
7 (3.6) |
0 (0.0) |
1 (2.7) |
8 (6.5) |
1 (3.1) |
| Comparison M vs. F, P-value
|
<0.0001
|
<0.0001
|
<0.0001
|
<0.0001
|
0.007
|
<0.0001
|
0.003
|
|
|
|
|
|
|
|
|
|
|
|
|
| Presence of children, N (%)
|
|
|
|
|
|
|
|
|
|
| Total
|
3,036 |
1,821 |
706 |
83 |
77 |
261 |
88 |
<0.0001 |
3036 |
| yes |
1,268 (41.8) |
558 (30.6) |
488 (69.1) |
33 (39.8) |
32 (41.6) |
117 (44.8) |
40 (45.5) |
|
|
|
|
|
|
|
|
|
|
| Male
|
931 |
140 |
511 |
47 |
40 |
137 |
56 |
<0.0001 |
931 |
| yes |
639 (68.6) |
87 (62.1) |
395 (77.3) |
25 (53.2) |
22 (55.0) |
77 (56.2) |
33 (58.9) |
|
|
|
|
|
|
|
|
|
|
| Female
|
2,105 |
1,681 |
195 |
36 |
37 |
124 |
32 |
<0.0001 |
2,105 |
| yes |
629 (29.9) |
471 (28.0) |
93 (47.7) |
8 (22.2) |
10 (27.0) |
40 (32.3) |
7 (21.9) |
| Comparison M vs. F, P-value
|
<0.0001
|
<0.0001
|
<0.0001
|
0.004
|
0.01
|
<0.0001
|
0.001
|
|
|
MP-diagnosis: medical professionals involved in the diagnosis of patients, including radiographers and laboratory technicians; MP-treatment: medical professionals involved in the treatments of patients, physical therapists, occupational therapists, etc. N: Number, M: Male; F: Female, MP: Medical professional. Statistical analyses between male and female in each occupation were shown in italics.
General Health
Table 5 summarizes the evaluation of participant physical and mental health using the GHQ-30, subjective health perspective, sleep state, occupational stress (Effort- Reward Imbalance, ERI), experiences of medical incidents or accidents, and Quality of Working Life (QWL).
Table 5. Results of GHQ, sleep time, ERI, medical incidences, stress of lifestyle, and social support network
|
Total
|
Nurse
|
Physician
|
Dentist
|
Pharmacist
|
MP-diagnosis
|
MP-treatment
|
P-value
|
Analyzed N
|
| Number
|
3,036 |
1,821 |
706 |
83 |
77 |
261 |
88 |
|
|
| Health status
|
|
|
|
|
|
|
|
|
|
| GHQ
|
|
|
|
|
|
|
|
|
|
| Total GHQ, points
|
8.0 (0.0-21.0)
[8.6±6.4]
|
9.0 (1.0-22.0)
[9.8±6.4]
|
5.0 (0.0-18.0) ***
[6.3±5.8]
|
5.0 (0.0-17.5) ***
[6.7±5.9]
|
6.0 (0.0-16.0) **
[7.2±5.3]
|
6.0 (0.0-21.0) *** §
[7.9±6.8]
|
7.0 (0.0-19.0)
[8.0±6.4]
|
0.0001 |
2,969 |
| Male GHQ, points
|
5.0 (0.0-19.0)
[6.7±6.2]
|
7.0 (0.0-23.0)
[8.3±6.9]
|
5.0 (0.0-18.0) *
[6.1±5.9]
|
6.0 (0.0-28.0)
[7.3±6.6]
|
5.0 (0.0-14.0)
[5.9±4.7]
|
5.0 (0.0-21.0)
[7.4±6.5]
|
5.5 (0.0-19.0)
[6.8±6.5]
|
0.008 |
911 |
| Female GHQ, points
|
9.0 (0.0-21.0)
[9.5±6.4]
|
9.0 (1.0-21.0)
[9.9±6.4]
|
6.0 (0.0-17.0) ***
[7.0±5.4]
|
4.0 (0.0-14.0) *
[6.0±4.9]
|
9.0 (0.0-17.0)
[8.6±5.6]
|
6.5 (0.0-22.0)
[8.3±7.1]
|
10.5 (1.0-19.0)
[10.0±5.7]
|
0.0001 |
2,058 |
| Presence of health problems |
|
|
|
|
|
|
|
|
|
| GHQ ≥7 points, N (%) |
1,674 (56.4) |
1,165 (65.6) |
275 (39.6) |
32 (40.0) |
35 (48.6) |
121 (46.4) |
46 (54.8) |
<0.0001 |
2,969 |
| GHQ ≥8 points, N (%) |
1,486 (50.1) |
1,042 (58.6) |
240 (34.5) |
28 (35.0) |
32 (44.4) |
105 (40.2) |
39 (46.4) |
<0.0001 |
| GHQ ≥17 points, N (%) |
385 (13.0) |
287 (16.2) |
49 (7.1) |
5 (6.3) |
2 (2.8) |
34 (13.0) |
8 (9.5) |
<0.0001 |
| Subjective health, N (%)
|
| Excellent |
353 (11.6) |
136 (7.5) |
143 (20.3) |
14 (16.9) |
13 (16.9) |
32 (12.3) |
15 (17.1) |
<0.0001 |
3,035 |
| Good |
679 (22.4) |
361 (19.8) |
202 (28.6) |
17 (20.5) |
16 (20.8) |
63 (24.1) |
20 (22.7) |
|
|
| Fair |
1,522 (50.2) |
966 (54.7) |
277 (39.2) |
44 (53.0) |
40 (52.0) |
123 (47.1) |
42 (47.7) |
|
|
| Not fair |
429 (14.1) |
295 (16.2) |
75 (10.6) |
7 (8.4) |
8 (10.4) |
34 (13.0) |
10 (11.4) |
|
|
| Poor |
52 (1.7) |
32 (1.8) |
9 (1.3) |
1 (1.2) |
0 (0.0) |
9 (3.5) |
1 (1.1) |
|
|
| Sleep time, N (%)
|
| <5.0 hour |
298 (9.8) |
180 (9.9) |
67 (9.5) |
10 (12.1) |
4 (5.2) |
29 (11.1) |
8 (9.1) |
0.21 |
3,034 |
| 5.0-5.9 hour |
1,329 (43.8) |
780 (42.9) |
312 (44.3) |
45 (54.2) |
37 (48.1) |
122 (46.7) |
33 (37.5) |
| 6.0-6.9 hour |
1,054 (34.7) |
629 (34.6) |
254 (36.0) |
18 (21.7) |
29 (37.7) |
84 (32.2) |
40 (45.5) |
| 7.0-7.9 hour |
303 (10.0) |
191 (10.5) |
63 (8.9) |
9 (10.8) |
7 (9.1) |
26 (10.0) |
7 (8.0) |
| 8.0-8.9 hour |
40 (1.3) |
30 (1.7) |
9 (1.3) |
1 (1.2) |
0 (0.0) |
0 (0.0) |
0 (0.0) |
| ≥9.0 hour |
10 (0.3) |
10 (0.6) |
0 (0.0) |
0 (0.0) |
0 (0.0) |
0 (0.0) |
0 (0.0) |
| Sleep satisfaction for rest, N (%)
|
| Enough sleep |
379 (12.5) |
210 (11.6) |
101 (14.3) |
10 (12.2) |
12 (15.6) |
32 (12.3) |
14 (15.9) |
0.16 |
3,001 |
| Not but for rest |
1,746 (57.6) |
1,083 (59.6) |
395 (56.0) |
41 (50.0) |
42 (54.6) |
138 (52.9) |
47 (53.4) |
| Not much time for sleep |
837 (27.6) |
491 (27.0) |
188 (26.7) |
26 (31.7) |
21 (27.3) |
86 (33.0) |
25 (28.4) |
| No time to sleep |
69 (2.3) |
34 (1.9) |
21 (3.0) |
5 (6.1) |
2 (2.6) |
5 (1.9) |
2 (2.3) |
| Effort-reward imbalance
|
| Effort, points
|
16.0 (8.0-25.0)
[16.1±5.0]
|
17.0 (7.0-23.0)
[17.3±4.7]
|
14.0 (7.0-23.0) ***
[14.7±4.9]
|
13.5 (7.0-20.5) ***
[13.8±4.3]
|
14.0 (8.0-22.0) ***
[14.3±4.4]
|
13.0 (7.0-2.0) *** †
[13.4±4.7]
|
13.5 (8.0-22.0) **
[14.3±4.8]
|
0.0001 |
2,974 |
| Reward, points
|
49.0 (33.0-55.0)
[46.9±7.4]
|
49.0 (33.0-55.0)
[46.8±7.4]
|
49.0 (33.0-55.0)
[47.1±7.3]
|
49.0 (36.0-55.0)
[48.0±6.2]
|
48.0 (36.0-54.0)
[46.8±6.5]
|
49.0 (33.0-55.0)
[47.6±7.0]
|
46.0 (19.0-54.0) *** ‡ || p ccc
[43.2±9.9]
|
0.0008 |
2,961 |
| Effort-reward imbalance |
0.6 (0.3-1.3)
[0.7±0.4]
|
0.6 (0.4-1.3)
[0.7±0.4]
|
0.5 (0.2-1.2) ***
[0.6±0.3]
|
0.5 (0.3-1.1) ***
[0.6±0.2]
|
0.6 (0.3-1.2) *
[0.6±0.3]
|
0.5 (0.2-1.0) ***
[0.5±0.3]
|
0.6 (0.3-1.7) # cc
[0.7±0.6]
|
0.0001 |
2,961 |
| Effort-reward imbalance >1.0, N(%) |
352 (11.9) |
258 (14.5) |
58 (8.4) |
5 (6.3) |
5 (6.8) |
15 (5.9) |
11 (12.9) |
<0.0001 |
| Medical incidents & accidents, N (%)
|
| Never felt |
152 (5.1) |
70 (3.9) |
49 (7.0) |
9 (11.3) |
3 (4.0) |
14 (5.4) |
7 (8.0) |
<0.0001 |
2,998 |
| Be worried |
855 (28.5) |
477 (26.6) |
218 (31.2) |
24 (30.0) |
19 (25.3) |
94 (36.2) |
23 (26.1) |
| Occasionally |
1,425 (47.5) |
863 (48.1) |
326 (46.6) |
42 (52.5) |
34 (45.3) |
118 (45.4) |
42 (47.7) |
| Often felt |
422 (14.1) |
295 (16.4) |
72 (10.3) |
3 (3.8) |
16 (21.3) |
23 (8.9) |
13 (14.8) |
| Met actual risk |
144 (4.8) |
91 (5.1) |
34 (4.9) |
2 (2.5) |
3 (4.0) |
11 (4.2) |
3 (3.4) |
| Stress of lifestyle in maintaining personal values in QWL
|
| Having no spare time for hobbies, family, and self. points
|
9.0 (3.0-15.0)
[9.5±3.4]
|
9.0 (3.0-15.0)
[9.4±3.4]
|
10.5 (3.0-15.0) ***
[10.2±3.5]
|
10.5 (3.0-15.0)
[9.6±3.3]
|
9.0 (4.5-15.0) §
[9.0±3.3]
|
9.0 (3.0-15.0) ‡
[8.9±3.4]
|
10.5 (3.0-15.0)
[9.3±3.3]
|
0.0001 |
3,027 |
| Cannot do healthy activities, think about own future. points
|
10.0 (4.0-15.0)
[9.6±3.1]
|
10.0 (4.0-14.0)
[9.5±3.1]
|
10.0 (4.0-15.0) *
[10.0±3.1]
|
9.0 (4.0-14.0)
[9.4±3.1]
|
8.0 (5.0-13.0) §
[8.7±2.9]
|
9.0 (4.0-14.0) †
[9.2±3.0]
|
8.0 (3.0-13.0) §
[8.9±3.1]
|
0.0001 |
3,028 |
| Feeling difficulty setting own pace and schedule. points
|
10.0 (5.0-15.0)
[10.0±3.3]
|
10.0 (4.0-15.0)
[9.8±3.2]
|
12.0 (6.0-15.0) ***
[10.9±3.1]
|
11.5 (4.0-15.0)
[10.3±3.4]
|
10.0 (6.0-15.0)
[10.0±3.2]
|
10.0 (5.0-15.0) ‡
[9.9±3.2]
|
9.0 (4.0-15.0) ‡
[9.1±3.1]
|
0.0001 |
3,032 |
| Social support network in QWL
|
| Emotional, points
|
17.0 (11.0-24.0)
[17.6 ± 3.8]
|
18.0 (12.0-24.0)
[17.8 ± 3.6]
|
17.0 (10.0-24.0)
[17.5±4.2]
|
17.0 (10.0-24.0)
[17.7±4.2]
|
16.0 (12.0-24.0)
[16.9±3.4]
|
16.0 (10.0-24.0) ***§
[16.7±3.9]
|
16.0 (5.0-24.0) ***§
[16.6±4.9]
|
0.0001 |
3,029 |
| Informational, points x2
|
16.0 (10.0-24.0)
[16.8±4.1]
|
16.0 (10.0-24.0)
[16.8±3.9]
|
16.0 (8.0-24.0)
[16.8±4.5]
|
18.0 (10.0-24.0)
[17.5±4.2]
|
16.0 (12.0-20.0)
[16.2±3.2]
|
16.0 (10.0-24.0)
[16.6±3.9]
|
16.0 (6.0-24.0)
[16.1±5.0]
|
0.23 |
3,026 |
| Actual assistance, points x4
|
16.0 (8.0-24.0)
[17.3±4.2]
|
16.0 (12.0-24.0)
[17.4±4.0]
|
16.0 (8.0-24.0)
[17.4±4.6]
|
20.0 (8.0-24.0)
[17.6±4.9]
|
16.0 (8.0-24.0)
[16.4±4.0]
|
16.0 (8.0-24.0)
[16.8±4.2]
|
16.0 (8.0-24.0)
[16.4±5.4]
|
0.04 |
3,033 |
MP-diagnosis: medical professionals involved in the diagnosis of patients, including radiographers and laboratory technicians; MP-treatment: medical professionals involved in the treatments of patients, physical therapists, occupational therapists, etc. N: Number, M: Male; F: Female, MP: Medical professional,
GHQ: General health questionnaire, ERI: Effort-reward imbalance, QWL: Quality of Working Life, N: Number, MP: Medical Professional.
For the continuous data, in each column, upper and lower value means median (5 percentile-95 percentile) and mean ± standard deviation, respectively.
vs Nurse, *: P<0.05; **: P <0.01; ***: P <0.001, vs Physician, §: P <0.05; †: P <0.01; ‡: P <0.001, vs Dentist, #: P <0.05; ||: P <0.001, vs Pharmacist, P: P <0.05, vs MP - diagnosis, cc: P <0.01; ccc: P <0.001
GHQ-30
The median value of total GHQ score was 8.0 points, with nurses having significantly higher scores (9.0 points) and physicians scoring the lowest (5.0 point) (Kruskal–Wallis: P=0.0001). Pharmacists, as well as the MP-diagnosis and -treatment groups, had scores ranging from 6.0 to 7.0 points. Nurses had a significantly higher proportion (65.6%) of GHQ scores ≥ 7 points, indicating a higher prevalence of medical problems (P<0.0001). Physicians and dentists were least likely to have health problems. The scores on GHQ in male physicians and pharmacists were the lowest (5.0 points, both), while the score on GHQ in male nurses was the highest (7.0 points) (P=0.008). Female nurses and those in the MP-treatment group had the poorest performance in the GHQ-30 questionnaire (10.5 points and 9.0 points, respectively) (P=0.0001). Regarding sex differences, female MPs in each occupation, except for dentists, generally had worse scores than males.
Subjective health status
Overall, 50% of the respondents answered “fair” and 34% “good” to “excellent.” The percentage of respondents who answered “fair” was almost 50% for all occupations. However, nurses had significantly the lowest percentage (7.5%) answering, “excellent,” while physicians had the highest percentage (approximately 20%) (P<0.0001).
Sleeping conditions
Regarding sleep duration, approximately 80% of the respondents slept for 5 to 6 hours, with no significant difference between occupations. Additionally, there was no significant difference in subjective sleep perception, with 50% of the MPs reporting that they were getting a fair amount of sleep and approximately 30% reporting that they were not getting enough sleep.
Occupational stress: Effort-Reward Imbalance (ERI)
Effort and reward elements were measured and scored according to the ERI model, and the balance (the ratio of effort and reward) was examined. The median effort score was approximately 16.0 points, while the reward component was 49.0 points, resulting in a 0.6 balance. Nurses had significantly higher effort scores than other occupations, while the MP-diagnosis group had the lowest reward scores (Kruskal–Wallis: P=0.0001). Therefore, the nurses and MP-treatment group had the highest effort-to-reward ratio imbalances (i.e., effort/reward >1.0; 14.5%) (P<0.0001).
Experience with medical accidents
Dentists, the MP-treatment group, and doctors were the occupations most likely to say that they had never had accidents, at 11%, 8%, and 7%, respectively. Nurses and pharmacists had a significantly lower likelihood of reporting no accidents (approximately 4%) (P<0.0001). The combined percentage of nurses and pharmacists who often felt at risk of accidents or had accidents exceeded 20%.
Quality of Working Life (QWL)
The results of the evaluation of lifestyle stress in maintaining personal values were consolidated into three categories (Table 5), with physicians having significantly the highest in all categories (Kruskal–Wallis: P=0.0001 for all). Pharmacists, the MP-diagnosis, and the MP-treatment groups had lower scores. The MP-diagnosis and -treatment groups had the lowest emotional support, compared to nurses and physicians in the use of social support network. Pharmacists were the least likely to receive actual assistance. Informational support was similar among the professions.
Discussion
We surveyed MPs from five University hospitals, which are special functioning hospitals, and compared working conditions, family status, health status, workplace stress, and evaluation of their own lives among six occupation groups. The health status was assessed using the GHQ-30. Nurses had the worst subjective sense of health, while physicians had the best. The MP-diagnosis group had the best effort-reward balance, while nurses and the MP-treatment group had the worst. Physicians had the highest evaluation of lifestyle stress. Thus, different occupations have different health conditions and occupational stresses, and different supports are needed for each occupation.
Basic characteristics of the subjects
The five University Hospitals were special functioning hospitals, where not only advanced medical care for critically ill patients but also student-to-resident education were provided. Thus, the MPs working at these facilities may have more burdens on their personal life and health than those in general hospitals and clinics. The coronavirus pandemic also influenced the working environment, stress in MP, and the questionnaire response rate.
Distribution methods, distribution periods, collection rates, and valid responses
According to the Kyushu Regional Bureau of Health and Welfare database, the total number of MPs at the five universities in 2020 was 9,079. Special functioning hospitals, including the five participating hospitals, were mandated to report annually the number of employees by job category (Table 1) [16]. We could not distribute questionnaires to all employees because of their heavy workloads and difficulties in cooperation. Owing to differences in internet environments and staff accessibility at the five universities, it was not possible to unify the survey distribution method of the web or paper versions; this may have affected collection rates. Additionally, the COVID-19 pandemic had not yet begun when the study was planned but began just after each facility ethics committee approved the study. Thus, the study started when the first wave of the pandemic subsided. The distribution periods were also spread over the five Universities when the waves of COVID-19 had settled, requiring a long survey period between March and November 2020.
Basic background differences across the six occupational groups
Reflecting educational system characteristics for each occupation in Japan, graduates from vocational schools were the greatest in the MP-treatment group, followed by nurses and the MP-diagnosis group. Four-year learning institutions for nursing education have increased, with more nursing college graduates than in the MP-diagnosis and MP-treatment groups. Regarding employment contracts, the dentists had the largest number of part-time and short-term employees because many had to maintain their specialist certification by working at University Hospitals as part-time workers. Regarding job position, physicians and dentists were in middle management and had concerns about medical personnel workstyle reform, especially among female physicians and dentists. In terms of employment stability, that for physicians and dentists was less stable compared to other occupations.
The total marriage rates, including men and women, were the lowest among nurses. However, female marriage rates were not significantly different from other MPs, except for female physicians. Many female physicians responding to the survey were married, while unmarried male MPs were uninterested. Both male and female physicians with children had higher response rates. However, there were no significant differences between non-physician occupations. The presence of marriage and child-rearing increased survey interest among male MPs.
Health status (GHQ, subjective health status, sleep status)
The GHQ-30 was used to examine the mental and physical health of medical and healthcare professionals. A direct mean score comparison with other GHQs, including the GHQ-12, -60, and -28, was difficult, but the psychologically distressed groups were defined in each GHQ form as a cutoff point of ≥ 4 on the GHQ-12, ≥ 6 on GHQ-28, and ≥ 7 on GHQ-30. We compared our results with the ratios of the distressed groups in previous studies using different GHQ questionnaires. A previous study on Japanese female physicians conducted in 2007 reported a median score of 6, with 41.6% participants having a GHQ above the threshold: ≥ 8 [17]. In the present study, including 72.4% male physicians, the median GHQ-30 score was lower. Additionally, the individual proportion with a GHQ-30 score ≥ 8 was smaller. In our study, the GHQ-30 score ≥ 8 ratio in all physicians was also lower than in the previous study, owing to better scores among male physicians.
In previous studies on nurses, the ratios of the psychologically distressed groups over the GHQ threshold were between 40.1% and 68.8%, respectively [18–20]. A Japanese male nurse study reported the lowest value of 40.1% [20], while the highest value (68.8%) was observed among female nurses working at University Hospitals in a metropolitan area during 2003. This percentage is comparable to the findings from the nurses in this study [18]. Male nurses generally experienced lower distress than females [20, 21].
A survey of hospital-employed pharmacists reported that 54.7% of the participants scored above the GHQ threshold [22]. Similarly, the present study observed a relatively high psychological distress prevalence among pharmacists. Among the six occupations studied, the average score and proportion of individuals classified into the distressed group were second highest among the MP-treatment group. The MP-treatment group was associated with worse GHQ scores than the MP-diagnosis group. The worse GHQ scores among the MP-treatment group may be because this group had more contact with patients and more risk of medical accidents.
During the same period, a small number of MP participants were surveyed at a single municipal University Hospital in Tokyo during the 2020 COVID-19 epidemic [23]. More people were infected with COVID-19 in Tokyo than in Fukuoka and Saga prefectures, where this study was conducted. Additionally, this study was conducted between COVID-19 infection waves when the prevalence had subsided. This phenomenon may have influenced the relatively high ratio of the distressed group among the nurses (66.4%), but it was less than that in the Tokyo area.
Stress related to occupation: Effort-Reward Imbalance model
Work stress may influence mental stress, and has been evaluated from two perspectives: effort and rewards. The usefulness of this tool has been studied in Japan, and surveys have been conducted in various occupations [24–27]. Healthcare workers were reported to have higher workplace stress levels, and the nurses in this study had similar levels compared to previous studies of 20,000 general workers in Japan [24]. Additionally, the MP-treatment group, including occupational therapists and physical therapists, had a high effort-reward score of ≥1. The ratio of nurses was higher, owing to higher effort scores (the numerator), while that of the MP-treatment was high owing to lower reward scores (the denominator). Unlike nurses, the MP-treatment group played a minor role in patient healing, making involvement in the complete recovery process difficult. Compared to nurses, the MP-treatment group had lower salaries, which could have been a reason for the lower reward score. The effort-reward ratios were reported to be higher among general workers who worked more than 50 h per week [24]. Furthermore, the average working hours of the nurses and individuals in the MP-treatment group were 42–43, shorter than that of the physicians, who worked more than 50. Therefore, long working hours may not necessarily be a contributing factor. Moreover, previous reports have indicated higher effort scores for shift-working jobs [24]. Thus, the higher effort scores of nurses may have been attributable to them working the most night shifts. However, the low remuneration scores of medical personnel not working many night shifts and working fewer hours than physicians are thought to be linked to lower work motivation. Thus, their work efforts were not as high as that of nurses, but their rewards were the lowest.
Experience with medical incidents and accidents
Regarding medical accidents, dentists had the lowest rate of near-miss experiences, while nurses and physicians had the highest. A survey conducted in 2018 among 2,480 medical facilities affiliated with the Japan Hospital Association reported an average of 278 near-misses/month at each hospital [28]. In the 40th Medical Accident Information Collection Project Report, it was reported that the occupations with the most medical accidents and incidents were nurses, physicians, and pharmacists, in that order [29]. Although the present study did not specifically examine near-miss events, the fact that they were more common among nurses, physicians, and pharmacists suggests a similar trend.
Lifestyle stress in maintaining personal values and social support network
Workplace quality of life is crucial for all workers. Diversification of working styles has been recommended, and new employment and labor policies have been designed to suit these types of work styles in the current and severe working environment of the medical field. MPs often experience high distress levels due to the nature of their work and its environment. We previously developed a questionnaire comprising four trait scales and used two traits in this study [14]. The physicians had less time to devote to their hobbies, health, and future, and they experienced difficulty in setting their own pace and schedule. This finding may be partially related to the fact that physician work time was the longest, and they had to work at a University Hospital and multiple facilities, including private clinics. Compared with nurses and physicians, emotional support was lower in the MP-diagnosis and -treatment groups, which may be related to the few employees in each group; thus, they may be isolated. These themes and postulates should be examined in future sub-analyses.
Limitations
The survey targeted the MPs working at 5 University Hospitals that provided not only medical care but also education and research, so the results might not be applicable to those working at general hospitals that provide community medical care, making it difficult to generalize the questionnaire results. We were also aware of some selection bias due to the mixed distribution methods of web and paper surveys, the long distribution periods varying from one to three months, the low collection rate and valid responses, and the limitation of the survey field to northern Kyushu. Especially, the valid response rate of the MP-treatment group was low, at 28%, compared with other professions, and further study of a larger number of subjects is needed. The average ages of the physicians and dentists were higher than those of the other professions because the response rate from young physicians and dentists, such as residents, was low. Thus, the survey might not reflect the opinions of younger physicians and dentists, who tended to work excessive hours. The COVID-19 pandemic had not occurred at the time of study planning, and we did not intend to examine its impact in this survey since a cross-sectional study like this could not assess the influence of the outbreak. However, the outbreak during the distribution of questionnaires may have affected the survey methodology and contributed to selection bias.
Conclusions
In this study, we investigated and simultaneously examined GHQ and occupation-related stress concerning the health status of MPs, including the effort-reward imbalance model, assessment of their current lives, and future concerns. Mental health status was worst among nurses and best among physicians. Effort-Reward Imbalance was worse in the nurses and the MP-treatment group due to their high effort and low reward, respectively. Lifestyle stress in maintaining personal values was the worst among physicians. We plan to conduct a sub-analysis to carefully examine factors contributing to the variation among the different occupations. Differences in basic characteristics and working stress levels must be considered when assisting healthcare professionals.
Acknowledgements
We wish to thank all participants who cooperated with the survey during the COVID-19 pandemic. We would like to thank Editage (www.editage.com) for English language editing.
Conflict of Interest
There are no conflicts of interest to declare regarding this paper.
Financial Disclosure
This study was supported by two Japan Society for the Promotion of Science KAKENHI Grants-in-Aid for Challenging Exploratory Research (JP19K21724 and JP21K18462).
Data Sharing and Data Accessibility
Data supporting the findings of this study are available from the corresponding author upon request. The data are not publicly available due to privacy or ethical restrictions.
Author Contributions
A.C., M.N., K.T., and Y. F. conceived the ideas; R.I., MA.K., F.M., S.K., T.N., S.Y., K.N., K.K., and N.N. collected the data; A.C., H.S. and M.N. analyzed the data; and A.C. and H.C. led the writing.
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