Correspondence to Professor Qian Yang; [email protected] ; Dr Haidong Song; [email protected]
STRENGTHS AND LIMITATIONS OF THIS STUDY
The strengths of this study are that (1) we report the prevalence and related factors of workplace violence (WPV) between COVID-19 front-line and non-front-line healthcare workers (HCWs), respectively and (2) we reveal the joint factors (educational level, professional title) associated with WPV among COVID-19 front-line and non-front-line HCWs.
It is a cross-sectional study which means we could not make the causal inference.
This survey relies on the self-report questionnaire, which may lead to recall bias.
This study was conducted in Hangzhou, China, indicating the outcome may not be generalised in other regions and countries.
Introduction
Workplace violence (WPV), no matter physically or psychologically, has emerged as one of the most salient global issues.1 2 Although WPV affects nearly all sectors and types of workers, the health sector faces more disproportionate risks.3 Because healthcare workers (HCWs) have closer interpersonal contact with their patients and their relatives versus colleagues, predisposing them to WPV exposure.4 So far, the categories of WPV that HCWs mainly suffer from are verbal violence, assault, threat, physical violence and sexual harassment.1 A report indicated 8%–38% of HCWs experienced physical violence during their careers worldwide,5 and most WPVs targeting HCWs are caused by patients and their relatives.6 Many of them are under threatened and verbal aggression.7 To make matters worse, some HCWs even faced unwanted sexual harassment and death from patients and their relatives.8 Exposure to WPV can cause serious consequences for HCWs,9 the most direct being a potential decline in medical quality and an increase in adverse events.10 Moreover, several research has also found HCWs’ physical and mental health influenced by WPV, such as cardiovascular diseases,11 depression,12 psychological stress and sleep quality.13 Thus, it is necessary to mitigate the occurrence of WPV from patients and their relative among HCWs. Although the government has legislated to ban violence to protect the interest of HCWs, annual disputes between patients and HCWs persist due to inadequate clear surveillance and enforcement.14
Currently, the public, not just the government, are urging greater attention to the HCWs,15 16 especially amidst COVID-19 pandemic. Many HCWs are facing multilevel risks, including viral infection, working overload and WPV.17 As some governments struggle to manage resources during this pandemic, the healthcare systems are overwhelmed with COVID patients.18 This may affect the treatment availability for patients with other diseases, prompting aggrieved individuals to scapegoat doctors.19 Except misplaced anger, fear, panic, misinformation about COVID-19 spread are all possible reasons, which drive patients conducting violence to HCWs.17 Although WPV has occurred and reported frequently before COVID-19 pandemic, it is true that HCWs faced more and more violence during present pandemic.20 21 Based on data released by the International Committee of the Red Cross, there were 611 incidents of violence, harassment and discrimination reported between 1 February 2020 and 31 July 2020, and of these incidents, 67% were directed towards HCWs.22 As the normalisation of prevention and control measures of the COVID-19, there will be more opportunities for HCWs to the front line. Previous studies have reported that some social-demographic23–25 (gender, age, marital status, education, etc) and work-related factors4 9 (working experience, professional title, etc) connected with WPV among HCWs. However, after sorting out the recent research on WPV among HCWs since the outbreak of the COVID-19 pandemic, it is still found that these social-demographic and work-related factors are closely related to WPV among HCWs.8 26–28 But these influencing factors did not distinguish the specific HCW groups and the correlation strength during the same period. Thus, there is a research gap of the prevalence and WPV-related factors between front-line and non-front-line HCWs’ population under the circumstance of COVID-19.
Existing research has revealed the WPV status of front-line HCWs globally during COVID-19,21 29 but there are still lacking proof focus on the difference of prevalence and related factors among both COVID-19 front-line and non-front-line HCWs exposing to WPV in the same period, especially in China. Our study aimed to explore the prevalence, the associated factors of WPV among HCWs in COVID-19 front line/non-front line. We also hope to offer policy implication for governments to implement WPV precise prevention. The hypotheses of our study are that (1) the prevalence of WPV in front-line HCWs is higher than those in non-front-line HCWs and (2) considering the health outcome of HCWs between front line and non-front line is different,30 the associated factors with WPV in COVID-19 front-line/non-front-line HCWs may also be different. Specifically, based on published literature,31–33 the WPV experienced by front-line HCWs may be related to education level, work experience, professional title and type of HCWs, while age, gender, marital status, annual income, educational level, type of HCWs, work experience, professional title and hospital levels may be correlated to the WPV of non-front-line HCWs.
Materials and methods
Study design and participants
We conducted this cross-sectional study in Hangzhou city from December 2020 to January 2021. Hangzhou city with 13 administrative regions (districts of Shangcheng, Gongshu, Xihu, Binjiang, Xiaoshan, Yuhang, Linping, Qiantang, Fuyang, Lin’an and County of Jiande, Tonglu, Chun’an) is the capital city of Zhejiang province in China. We adopted multistage sampling to estimate the total HCWs’ situations. First, we selected five administrative regions (Xihu District, Lin’an District, Yuhang District, Jiande County, Chun'an County) in Hangzhou city by random number method. Second, we conducted the questionnaire survey of HCWs in all health institutions in these five administrative regions. The questionnaire was distributed online by Wenjuanxing link (Wenjuanxing is an online platform providing functions including making online questionnaires, distributing questionnaires and storing the questionnaire data temporarily). Specifically, the Wenjuanxing link was shared by the administrative information systems of each hospital to the mobile phones of each HCW, respectively. The relevant informed consent information for the survey was presented to the participants prior to the survey. The participants in our survey were people who fully agreed with the content and purpose of this research. Before HCWs finish the survey, they can read the preface including that the data would be kept confidential and used only for scientific data analysis. The minimum sample size was calculated by the single population proportion formula (n=Zα/22p(1−p)/d2). According to the assumption: the prevalence of WPV among HCWs was 61.9%,27 and the marginal error was 5%. Under the circumstance of 95% CI (α=0.05), n=1.962×0.619×(1–0.619)/0.052=362.39. Considering the non-response rate of 20%, the final sample size should be at least 435. All questionnaires were finished by volunteers themselves. However, the total number of HCWs in these five regions was 31 524,34 considering the high workload of HCWs and non-obligatory of survey, not everyone made responses. Therefore, the response rate was 46.04%. To make sure the data accuracy, we did a logic error-check inference to these data and screen out invalid questionnaire with consistent answers and blank content. There were 2036 samples excluded, and the effective rate was 87.98%.
Ultimately, there were 14 909 valid subjects included in the general dataset. We divided the total samples into two parts according to the variable, namely, whether went to the front line of COVID-19 prevention and treatment in last 12 months. In our study, front-line HCWs of COVID-19 included not only those responsible for treating COVID-19 patients, but also those who conducted nucleic acid testing and offered logistical support for medical staff, patients and potential infected persons.
Measures
Demographic characteristics
In this research, we included this demographic information as follows: age, gender, marital status, educational level, annual income, types of HCWs, working experience, professional titles, hospital levels.
Assessment of WPV
We adopted the WPV questionnaire developed by Zhang et al,35 to assess the prevalence of different types of WPV among HCWs which was caused by patients and their relatives in the past 12 months. WPV was divided into seven types, including verbal violence (eg, abuse, sarcasm, insult), made difficulties (eg, picky demands, unreasonable requests, non-compliance, ridiculec), smear reputation (unfounded allegations or complaints, defamation, rude damage to reputation), mobbing behaviour (vandalism of public facilities, booing, gathering to stir up trouble, public riots, malicious camera shooting, etc), intimidation behaviour (verbal or written threats, clenched fists, threats with weapons, stalking, etc), physical violence (biting, pushing, hitting, cutting, throwing things at the body, etc) and sexual harassment (sexually suggestive verbal actions, rape or attempted rape). The original scale used a 6-point Likert scale (0=never, 1=rarely, 2=occasionally, 3=often, 4=frequently, 5=everyday) to reflect the frequency of HCWs experiencing WPV. Following prior studies using the same scale,12 13 36 during the data processing, we coded ‘never’, ‘rarely’ as ‘0’ to indicate that HCWs had not experienced any types of WPV from patients and their relatives in the last 12 months, and coded other responses as ‘1’ to demonstrate that HCWs have encountered the type of WPV. This questionnaire was confirmed to have good reliability and validity by many scholars.13 37 The Cronbach’s alpha for the scale was 0.865 in our survey (see online supplemental document for details about the complete questionnaire).
Statistical analysis
In this study, the general dataset was split into front-line HCWs and non-front-line HCWs subdatasets, respectively. Data analysis was conducted in four steps. First, χ2 tests were employed to explore the bivariate relationships between demographic information and front-line status. Second, we also used the χ2 test to examine the association between seven types of WPV and front-line status. Third, the frequency of the number of WPV types was calculated separately for front-line/non-front-line HCWs. Finally, binary logistic regression model was established to identify the associated factors with front-line/non-front-line HCWs experiencing WPV. SPSS software, V.22.0 (SPSS), was used to conduct all data analyses, with a significance level of 0.05 (two tailed).
Results
Table 1 shows the demographic characteristics of HCWs. Of the all participants, 40.39% of HCWs aged between 30 and 39 years old, and 76.09% were female. Over half of the participants were married, and 67.74% received undergraduate education. Most HCWs (61.86%) earned between ¥60 000 and ¥120 000 annually. Of the participants, 53.38% were doctors, 41.92% worked less than 10 years, 53.05% had a primary professional title and 89.12% worked in the primary hospital. There were statistically significant differences in age (χ2=222.168, p<0.001), gender (χ2=222.568, p<0.001), marital status (χ2=104.777, p<0.001), educational level (χ2=190.192, p<0.001), annual income (χ2=275.356, p<0.001), types of HCWs (χ2=85.264, p<0.001), working experience (χ2=204.196, p<0.001), professional titles (χ2=307.995, p<0.001) and hospital level (χ2=282.148, p<0.001) between front-line and non-front-line HCWs. Specifically, in our study samples, compared with non-front-line HCWs, most front-line HCWs were between 30 and 39 years old, males, married, with higher educational level, annual income, work experience and professional title, doctor type and coming from primary hospitals.
Table 1Descriptive data of study variables
| Characteristic | Total (n=14 909) | Front line (n=3748) | Non-front line (n=11 161) | χ2 | P value |
| Age, n (%) | |||||
| 20–29 | 3812 (25.57) | 638 (17.14) | 3174 (28.72) | 222.168 | <0.001 |
| 30–39 | 6022 (40.39) | 1581 (42.48) | 4441 (40.19) | ||
| 40–49 | 3449 (23.13) | 1069 (28.72) | 2380 (21.54) | ||
| ≥50 | 1489 (10.11) | 434 (11.66) | 1055 (9.55) | ||
| Gender, n (%) | |||||
| Male | 3564 (23.91) | 1233 (32.90) | 2331 (20.89) | 222.568 | <0.001 |
| Female | 11 345 (76.09) | 2515 (67.10) | 8830 (79.11) | ||
| Marital status, n (%) | |||||
| Single | 3787 (25.40) | 716 (19.10) | 3071 (27.52) | 104.777 | <0.001 |
| Married | 11 122 (74.60) | 3032 (80.90) | 8090 (72.48) | ||
| Educational level, n (%) | |||||
| ≦Junior college | 4022 (26.96) | 691 (18.44) | 3331 (29.84) | 190.192 | <0.001 |
| Undergraduate | 10 099 (67.74) | 2810 (74.97) | 7289 (65.31) | ||
| ≥Graduate | 788 (5.29) | 247 (6.59) | 541 (4.85) | ||
| Annual income, n (%) | |||||
| ≦60 000 | 2461 (16.51) | 372 (9.93) | 2089 (18.72) | 275.356 | <0.001 |
| 60 000–120 000 | 9223 (61.86) | 2271 (60.59) | 6952 (62.29) | ||
| >120 000 | 3225 (21.63) | 1105 (29.48) | 2120 (18.99) | ||
| Types of HCWs, n (%) | |||||
| Doctors | 7959 (53.38) | 2201 (58.72) | 5758 (51.59) | 85.264 | <0.001 |
| Nurses | 5790 (38.84) | 1360 (36.29) | 4430 (39.69) | ||
| Administrator | 1160 (7.78) | 187 (8.72) | 973 (8.72) | ||
| Working experience, n (%) | |||||
| 0–9 | 5672 (41.92) | 1064 (31.56) | 4608 (45.36) | 204.196 | <0.001 |
| 10–19 | 3941 (29.13) | 1119 (33.19) | 2822 (27.78) | ||
| 20–29 | 2520 (18.63) | 784 (23.26) | 1736 (17.09) | ||
| ≥30 | 1396 (10.32) | 404 (11.98) | 992 (9.77) | ||
| Professional titles, n (%) | |||||
| Primary | 7909 (53.05) | 1545 (41.22) | 6364 (57.02) | 307.995 | <0.001 |
| Intermediate | 5050 (33.87) | 1505 (40.15) | 3545 (31.76) | ||
| Advanced | 1950 (13.08) | 698 (18.62) | 1252 (11.22) | ||
| Hospital level, n (%) | |||||
| Primary hospital | 5426 (36.39) | 1595 (42.56) | 3831 (34.32) | 282.148 | <0.001 |
| Secondary hospital | 5131 (34.42) | 1250 (33.35) | 3881 (34.77) | ||
| Tertiary hospital | 2730 (18.31) | 754 (20.12) | 1976 (17.70) | ||
| Non-graded hospital | 1622 (10.88) | 149 (3.98) | 1473 (13.20) | ||
Single, includes individuals who are unmarried, separated, widowed and divorced.
HCWs, healthcare workers.
As shown in table 2, the types of WPV experienced by HCWs, ranked by prevalence were made difficulties (24.43%), verbal violence (22.49%), smear reputation (11.51%), intimidation behaviours (7.43%), mobbing behaviours (5.99%), physical violence (4.98%) and sexual harassment (3.90%), respectively. There were significant differences between front-line and non-front-line HCWs in verbal violence (χ2=109.179, p<0.001), made difficulties (χ2=109.754, p<0.001), smear reputation (χ2=60.614, p<0.001), mobbing behaviours (χ2=17.450, p<0.001), intimidation behaviours (χ2=24.478, p<0.001). More specifically, in our study samples, front-line HCWs experienced more verbal violence, made difficulties, mobbing behaviours and intimidation behaviours than non-front-line HCWs. In contrast, non-front-line HCWs suffered from more smear reputation than front-line HCWs.
Table 2The incidence rate and rank ordering of WPV against health workers within last 12 months
| Violence styles | Total (N=14 909) | Front line (N=3748) | Non-front line (N=11 161) | χ2 | P value | Ranking orders (front line vs non-front line) |
| Verbal violence, N (%) | ||||||
| No | 11 556 (77.51) | 2674 (71.34) | 8882 (79.58) | 109.179 | <0.001 | 2–2 |
| Yes | 3353 (22.49) | 1074 (28.66) | 2279 (20.42) | |||
| Made difficulties, N (%) | ||||||
| No | 11 267 (75.57) | 2594 (69.21) | 8673 (77.71) | 109.754 | <0.001 | 1–1 |
| Yes | 3642 (24.43) | 1154 (30.79) | 2488 (22.29) | |||
| Smear reputation, N (%) | ||||||
| No | 13 193 (88.49) | 3185 (89.67) | 10 008 (84.98) | 60.614 | <0.001 | 3–3 |
| Yes | 1716 (11.51) | 563 (10.33) | 1153 (15.02) | |||
| Mobbing behaviours, N (%) | ||||||
| No | 14 016 (94.01) | 3471 (92.61) | 10 545 (94.48) | 17.450 | <0.001 | 5–5 |
| Yes | 893 (5.99) | 277 (7.39) | 616 (5.52) | |||
| Intimidation behaviours, N (%) | ||||||
| No | 13 802 (92.57) | 3401 (90.74) | 10 401 (93.19) | 24.478 | <0.001 | 4–4 |
| Yes | 1107 (7.43) | 347 (9.26) | 760 (6.81) | |||
| Physical violence, N (%) | ||||||
| No | 14 167 (95.02) | 3543 (94.53) | 10 624 (95.19) | 2.570 | 0.109 | 6–6 |
| Yes | 742 (4.98) | 205 (5.47) | 537 (4.81) | |||
| Sexual harassment, N (%) | ||||||
| No | 14 327 (96.10) | 3595 (95.52) | 10 732 (96.16) | 0.425 | 0.514 | 7–7 |
| Yes | 582 (3.90) | 153 (4.08) | 429 (3.84) | |||
WPV, workplace violence.
The total prevalence of WPV of HCWs in this study was 30.09%. While the prevalence in front-line HCWs was 37.25%, and 27.69% in non-front-line HCWs. Additionally, the numbers of WPV were statistically different between front-line and non-front-line HCWs (χ2=177.374, p<0.001). Most front-line HCWs (11.50%) suffered from 2 types of WPV, while most non-front-line HCWs (9.45%) experienced only one type of WPV (table 3).
Table 3Prevalence and number of WPV against HCWs within the last 12 months
| No of WPV | Total (N=14 909, 30.09%) | Front line (N=3748, 37.25%) | Non-front line (N=11 161, 27.69%) | χ2 | P value |
| 0, N (%) | 10 423 (69.91) | 2352 (62.75) | 8071 (72.31) | 177.3747 | <0.001 |
| 1, N (%) | 1447 (9.71) | 392 (10.46) | 1055 (9.45) | ||
| 2, N (%) | 1332 (8.93) | 431 (11.50) | 901 (8.07) | ||
| 3, N (%) | 665 (4.46) | 237 (6.32) | 428 (3.83) | ||
| 4, N (%) | 340 (2.28) | 135 (3.60) | 205 (1.84) | ||
| 5, N (%) | 138 (0.93) | 58 (1.55) | 80 (0.72) | ||
| 6, N (%) | 69 (0.46) | 23 (0.61) | 46 (0.41) | ||
| 7, N (%) | 495 (3.32) | 120 (3.20) | 375 (3.36) |
HCW, healthcare worker; WPV, workplace violence.
The ORs and 95% CIs obtained from the bivariable logistic regression models are listed in table 4. In front-line HCWs subdataset, female HCWs (OR 0.837, 95% CI 0.710 to 0.988) had a lower likelihood of experiencing WPV compared with male HCWs. HCWs with undergraduate education (OR 1.251, 95% CI 1.061 to 1.541) were more likely to suffer from WPV than those with a lower educational level. HCWs with intermediate professional title (OR 1.475, 95% CI 1.227 to 1.772) or advanced professional title (OR 1.693, 95% CI 1.294 to 2.216) were also more likely to experience WPV. In non-front-line HCWs subdataset, participants aged over 50 years old (OR 0.721, 95% CI 0.536 to 0.969) were less prone to experience WPV compared with those aged between 20 and 29 years old. HCWs who worked between 10 and 19 years (OR 0.847, 95% CI 0.749 to 0.958), and those working in non-graded hospitals (OR 0.714, 95% CI 0.614 to 0.832) may also have less probabilities to suffer from WPV. Compared with those with lower education, HCWs with undergraduate education (OR 1.323, 95% CI 1.179 to 1.484) or graduate education (OR 1.519, 95% CI 1.271 to 1.895) were more likely to experience WCV. Nurses (OR 1.142, 95% CI 1.031 to 1.265) had a higher likelihood of experiencing WPV than doctors. HCWs with intermediate (OR 1.458, 95% CI 1.297 to 1.638) or advanced (OR 1.928, 95% CI 1.607 to 2.313) professional titles were also more likely to suffer from WPV compared with those with primary professional titles.
Table 4Associations between demographic characteristics and workplace violence among health workers
| Variables | Front line | Non-front line | ||||
| OR | 95% CI | P value | OR | 95% CI | P value | |
| Age (Ref. 20–29) | ||||||
| 30–39 | 1.246 | 0.976 to 1.592 | 0.078 | 1.054 | 0.918 to 1.211 | 0.456 |
| 40–49 | 1.230 | 0.889 to 1.701 | 0.211 | 0.926 | 0.758 to 1.132 | 0.452 |
| ≥50 | 0.844 | 0.534 to 1.335 | 0.468 | 0.721 | 0.536 to 0.969 | 0.030 |
| Gender (Ref. male) | ||||||
| Female | 0.837 | 0.710 to 0.988 | 0.035 | 0.935 | 0.835 to 1.046 | 0.241 |
| Marital status (Ref. single) | ||||||
| Married | 1.018 | 0.828 to 1.253 | 0.864 | 0.920 | 0.816 to 1.037 | 0.172 |
| Educational level (Ref. ≦Junior college) | ||||||
| Undergraduate | 1.251 | 1.061 to 1.541 | 0.035 | 1.323 | 1.179 to 1.484 | <0.001 |
| ≥Graduate | 1.310 | 0.930 to 1.845 | 0.123 | 1.519 | 1.217 to 1.895 | <0.001 |
| Annual income (Ref. ≦60 000) | ||||||
| 60 000–120 000 | 0.916 | 0.717 to 1.170 | 0.480 | 1.051 | 0.925 to 1.194 | 0.448 |
| >120 000 | 0.833 | 0.631 to 1.099 | 0.196 | 0.982 | 0.833 to 1.158 | 0.830 |
| Types of HCWs (Ref. doctors) | ||||||
| Nurses | 1.141 | 0.960 to 1.356 | 0.135 | 1.142 | 1.031 to 1.265 | 0.011 |
| Administrators | 0.889 | 0.643 to 1.230 | 0.478 | 0.960 | 0.813 to 1.133 | 0.628 |
| Working experience (Ref. 0–9) | ||||||
| 10–19 | 0.920 | 0.760 to 1.115 | 0.397 | 0.847 | 0.749 to 0.958 | 0.008 |
| 20–29 | 0.780 | 0.587 to 1.036 | 0.087 | 0.966 | 0.797 to 1.170 | 0.721 |
| ≥30 | 0.733 | 0.482 to 1.116 | 0.147 | 0.836 | 0.633 to 1.105 | 0.208 |
| Professional titles (Ref. primary) | ||||||
| Intermediate | 1.475 | 1.227 to 1.772 | <0.001 | 1.458 | 1.297 to 1.638 | <0.001 |
| Advanced | 1.693 | 1.294 to 2.216 | <0.001 | 1.928 | 1.607 to 2.313 | <0.001 |
| Hospital level (Ref. primary hospital) | ||||||
| Secondary hospital | 1.073 | 0.916 to 1.257 | 0.384 | 1.029 | 0.928 to 1.141 | 0.588 |
| Tertiary hospital | 0.855 | 0.704 to 1.038 | 0.114 | 1.010 | 0.889 to 1.147 | 0.882 |
| Non-graded hospital | 0.852 | 0.593 to 1.222 | 0.384 | 0.714 | 0.614 to 0.832 | <0.001 |
Single, includes individuals who are unmarried, separated, widowed and divorced; non-graded hospital, includes non-governmental hospitals and private clinics.
HCW, healthcare worker; Ref, reference group.
Discussion
Experiencing the WPV can directly and profoundly affect the HCWs’ health and well-being.38 In this study, we revealed the prevalence and related factors associated with WPV between COVID-19 front-line and non-front-line HCWs, respectively. Our study found that high professional title and high educational level were the common factors that affected both front-line and non-front-line HCWs among all the related factors.
Our findings reported a lower prevalence (30.09%) of total WPV among HCWs compared with previous reports of WPV among HCWs in Turkey (44.7%) and in Shandong Province, China (47.8%).29 33 However, the prevalence of WPV among HCWs in our study was still higher than that reported in some areas of China, such as Heilongjiang Province (12.6%) and Zhejiang Province (22.05%).39 40 This may be caused by the China’s medical reform in recent years and the care for HCWs. For example, ‘Yi Nao’, an extreme behaviour of WPV which patients, patients’ relatives and even hiring gangs attack HCWs or damage hospital facilities, has been sentenced to prison.8 In our study, the prevalence of WPV was higher among front-line HCWs (37.25%) compared with non-front-line HCWs (27.73%). It is in line with previous studies,8 41 that the proportion of front-line HCWs suffering from one or more types of WPV exceeds that of non-front-line HCWs. It is understandable that front-line HCWs faced more hazardous situations. For example, they may be stigmatised as virus carriers by some patients42; they also lacked of adequate protective measures which may directly encounter the violence of attackers.43 Moreover, according to the scapegoating theory,44 patients in lockdown zones may be more likely to blame and aggress against HCWs, lacking a sense of control and emotional outlet due to prolonged lockdown. In our study, difficulties, verbal violence, smear reputation are the top three types of WPV suffered by both front-line/non-front-line HCWs, which is similar to previous studies.26 40 45 There are many factors related to these types of WPV, for example, diagnosed disease severity, dissatisfaction with medical services, education level of patients and their relatives, and poor doctor–patient relationships, etc.33 45 Previous studies have found that workplace quarantine measures can exacerbate the WPV,32 46 because both COVID-19 patients and front-line HCWs were locked in fixed locations, which lead patients have more chances and convenience to conducted WPV to HCWs,42 especially verbal violence, made difficulties, smear reputation, mobbing behaviours and intimidation behaviours. For non-front-line HCWs, they can get more protection by hospitals’ security measures, despite WPV may still be inevitable. Existing studies also have demonstrated that experiencing WPV can inflict tremendous psychological and mental trauma.13 16 Therefore, it is suggested that efforts should be made to help HCWs to identify the risks, provide more protection (eg, security guard, legal assistance, counselling), and comfort those who had experienced WPV, especially for front-line HCWs.
In our study, inconsistent with previous studies,2 8 we found that both front-line/non-front-line HCWs with higher professional titles were more likely to suffer from WPV. The association between professional titles and WPV may be triggered by the differences in the patients they treated. HCWs with higher professional titles have a higher probability of treating more severe patients, who also have higher risk of obtaining negative health outcomes, potentially spurring WPV to HCWs from patients or their guardians.33 Therefore, it means that hospitals should strengthen the protection of HCWs with high professional titles, such as establishing an early warning mechanism for them to prevent WPV. Consistent with previous research, educational level was also associated with WPV among front-line/non-front-line HCWs.31 47 It is understandable, as HCWs with higher educational levels will serve patients with more serious diseases. Generally, patients with more serious conditions are prone to worse doctor–patient relationship because of patients’ pain and irritable temperament.48 The worse doctor–patient relationship and negative health outcome may cause WPV to HCWs by the patients and their relatives. Therefore, HCWs should be trained to learn the doctor–patient communication skills and WPV handling skills.
For front-line HCWs, this study reported that female HCWs were less likely to experience WPV, which is consistent with other Chinese studies.33 49 Because bullying the weak (such as women and children) is considered immoral in Chinese traditional culture.45 Compared with males, females are less inclined to resist and are physically weaker, which makes them less regarded as the target of WPV relatively. Thus, health administrators and hospitals should focus more on male HCWs. For non-front-line HCWs, our study revealed HCWs who aged over 50 years had less chance to undergo WPV, which is in line with previous studies.29 50 Studies have found that HCWs have more clinical expertise and higher professional knowledge as their age grows, which may enable them to handle and resolve conflicts and disputes with patients and their families, thereby reducing the risk of WPV.27 41 Additionally, patients may be more likely to respect the advice and instructions of older HCWs, decreasing non-compliance and dissatisfaction.51 So the hospitals and health colleges should provide special training against exacting patients and their relatives for young HCWs. Our study showed that non-front-line nurses were more vulnerable to encountering WPV, which is accorded with previous research.29 52 One of the reasons may be that nurses are the group that accompany patients the most compared with doctors and administrators. A study conducted in China found that HCWs directly contacting with patients were 2.98 times more liable to be exposed to WPV than those with indirect contact.53 The other reason may be attributed to that, compared with front-line nurses, the work environment of non-front-line nurses is relatively stable and safe, which may lead to higher expectations from patients.54 Patients may become dissatisfied and hostile toward nurses who cannot meet these expectations. Thus, non-front-line nurses should also give enough attention because more research has focused on doctors currently. We also found that non-front-line HCWs with working experience between 10 and 19 years were less likely to suffer from WPV. We assume that it may be because HCWs with over 10 years of experience have treated many patients, equipping them with greater expertise to assess, deal with and avoid potential WPV. In addition, we found an interesting outcome that non-front-line HCWs in non-governmental hospitals and private clinics have less possibility of sustaining WPV. It is because most patients prefer public hospitals over non-governmental hospitals and private clinics in China,55 because public hospitals were dominant in the Chinese medical system. According to the data released in,China Health Statistics Yearbook 2021 non-governmental hospitals and private clinics accounted for 66.54% of the total number of hospitals in the country, while the number of visits to non-governmental hospitals and private clinics only accounted for 15.98% of the total number of visits to hospitals in the country.56 Therefore, it is advisable to encourage mild patients to be diverted to non-governmental hospitals, which could alleviate pressures and long wait times in public hospitals.
The findings of our study can give some implications for manager to make policies and researchers to conduct intervention of WPV for HCWs. More importantly, these implications can also offer experience for those countries still under the pandemic of COVID-19 pandemic. First, the government should further strengthen the legislation to protect the rights of HCWs. Social media (eg, news, short video, official account) should promote harmony between doctors and patient. Second, hospitals should offer adequate security measures and safeguarding,57 especially for HCWs with higher professional titles and educational levels. Third, psychological counselling services focusing on HCWs, particularly front-line HCWs, should be established. Fourth, it is urgent to improve the patients’ literacy,58 conduct health education59 and implement pre-examination procedures to enhance the efficiency of doctor–patient communication.
In this study, there are also several limitations that should be noted. First, it is a cross-sectional study which means we could not make the causal inference. Therefore, we are intended to use longitudinal design to further verification. Second, this survey relies on the self-report questionnaire, which may lead to recall bias among participants. Finally, as the study was conducted solely in Hangzhou, China, the generalisability of findings to other geographical regions and countries is unclear.
Conclusion
This study illustrates the potential factors associated with WPV among front-line and non-front-line HCWs in China. Policy-makers may formulate strategies based on these findings to help HCWs to obtain enough support and care, especially for HCW with high professional title and high educational level. Our study suggests that research in the future can focus more on the prevention and intervention strategies of WPV in HCWs from multiple perspectives, such as intervening with patients rather than just doctors; qualitative and quantitative analysis of WPV reported by the media; empirical research on guiding public opinion towards WPV.
The authors would like to thank all the participants in this study for their time and help.
Data availability statement
Data are available on reasonable request.
Ethics statements
Patient consent for publication
Not applicable.
Ethics approval
This study involves human participants and this study was implemented in accordance with the Declaration of Helsinki. Hangzhou seventh people’s hospital approved this study (2020035). Participants gave informed consent to participate in the study before taking part.
Contributors DJ: concept and design, analysis, drafting of the initial manuscript and revision. QW: concept and design, analysis, drafting of the initial manuscript and revision. XX: data collection and revision of the manuscript. JZ: revision of the manuscript. YX: critical feedback and revision of the manuscript. YZ, SL and LB: data collection and critical feedback and revision of the manuscript. HS: supervision, validation, interpretation of data and critical revision of the manuscript. QY: concept and design, supervision, validation, interpretation of data and critical revision of the manuscript. HS and QY is the guarantor of this article.
Funding This study was funded by the National Natural Science Foundation of China (71974170), Leading Innovative and Entrepreneur Team Introduction Program of Zhejiang (2019R01007) and Public Projects of Science and Technology Department of Zhejiang Province (LGF21H090006).
Competing interests None declared.
Patient and public involvement Patients and/or the public were not involved in the design, or conduct, or reporting, or dissemination plans of this research.
Provenance and peer review Not commissioned; externally peer reviewed.
Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.
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Abstract
Objectives
Workplace violence (WPV) against healthcare workers (HCWs) is a global issue. Our research aimed to elucidate the status and associated factors of WPV among front-line/non-front-line HCWs during the COVID-19 pandemic.
Design
This cross-sectional study was conducted among HCWs in Hangzhou City through multistage sampling from December 2020 to January 2021.
Participants
This study included 14 909 valid samples (N=3748 front-line HCWs and N=11 161 non-front-line HCWs).
Primary and secondary outcome measures
We assessed the WPV status by Chinese version of WPV questionnaire. Binary logistic regression model was established to examine the associated factors of front-line/non-front-line HCWs experiencing WPV.
Results
The total WPV prevalence equalled 37.25% for front-line HCWs and 27.73% for non-front-line HCWs. Among front-line HCWs, females were less likely to experience WPV (OR 0.837, 95% CI 0.710 to 0.988), while individuals who were undergraduate (OR 1.251, 95% CI 1.061 to 1.541) and had higher professional title (intermediate: OR 1.475, 95% CI 1.227 to 1.772; advanced: OR 1.693, 95% CI 1.294 to 2.216) were more likely to suffer from WPV; for non-front-line HCWs, individuals who aged over 50 years old (OR 0.721, 95% CI 0.563 to 0.969), had worked between 10 and 19 years (OR 0.847, 95% CI 0.749 to 0.958) and worked in the non-graded hospital (OR 0.714, 95% CI 0.614 to 0.832) had less chance to experience WPV, while individuals who had higher educational level (undergraduate: OR 1.323, 95% CI 1.179 to 1.484; ≥graduate: OR 1.519, 95% CI 1.217 to 1.895), were nurse (OR 1.142, 95% CI 1.031 to 1.265), and had higher professional title (intermediate: OR 1.458, 95% CI 1.297 to 638; advanced: OR 1.928, 95% CI 1.607 to 2.313) were more inclined to suffer from WPV (p all<0.05).
Conclusions
This study indicates that the prevalence of WPV among front-line HCWs is significantly higher than among non-front-line HCWs. Policy-makers should prioritise COVID-19 front-line HCWs, especially those with high educational levels and professional titles.
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Details
1 School of Public Health, and Department of Geriatrics of the Fourth Affliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China
2 School of Health Policy and Management, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
3 Department of Community Service, Hangzhou Seventh People's Hospital, Hangzhou, Zhejiang, China