Microaggressions in Medicine: A Pilot Study on Differences and Determinants Among Doctors and Nurses

Microaggressions in Medicine: A Pilot Study on Differences and Determinants Among Doctors and Nurses | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Microaggressions in Medicine: A Pilot Study on Differences and Determinants Among Doctors and Nurses TAO Lv, Wenjie Yan, longlong Li, Shuai Jiang, Min Zhang, Yasong Du This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4919288/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Introduction "Microaggression", encapsulates the notion that subtle and commonplace instances of discrimination and bias, can result in psychological and emotional distress, further entrenching inequality and cultivating a hostile social atmosphere for marginalized individuals or collectives. Studies endeavors to shed light on illuminating the impact of microaggressions on healthcare workers have found that they have consistently underscored their pervasive detrimental effects. This study aims to investigate the current status of microaggression encounters among healthcare workers, alongside an examination of the contributing risk factors associated with the occurrence of such microaggressions. Methods A total of 190 aged 18–60 years clinical healthcare practitioners were recruited from March to April 2023. Questionnaires including the Everyday Discrimination Scale-9 items (EDS). Results A total of 83 nurses [82(98.8%) female] and 107 doctors [54(50.5%) female] participated. Among the participants, 40(37.4%) doctors and 50(60.2%) nurses reported encountering microaggressions. Notably, the prevalence of microaggressions among nurses was significantly higher than that among doctors (P = 0.002). Binary logistic regression analysis provided insights into the independent factors influencing the experience of microaggressions. For doctors, the department emerged as a significant influencer (reference level = internal; OR auxiliary =6.138, P auxiliary =0.016), for nurses, age (reference level = 18 ~ 35y; OR 36 ~ 60 =3.497, P 36 ~ 60 =0.037), department (reference level = internal; OR auxiliary =0.072, P auxiliary =0.007), and family structure (reference level = nuclear family; OR big family =0.242, P big family =0.012) demonstrated significant influence of experience of microaggressions. Conclusions Healthcare professionals have encountered a significant prevalence of microaggressions, with a distinct impact observed among nurses. The encounters with microaggressions within the healthcare workforce have exhibited a robust connection with symptoms of anxiety and depression. Specifically, doctors employed in auxiliary departments have been identified as being at a heightened risk of encountering microaggressions in comparison to their peers in internal medicine. Conversely, nurses stationed in auxiliary departments face an elevated risk in contrast to their counterparts in internal medicine. Moreover, among nurses, an advanced age and living in a nuclear family (as opposed to big family) have been identified as factors contributing to an increased vulnerability to microaggressions. discrimination health care influencing factors microaggression pilot study Figures Figure 1 1. Introduction 1.1 Microaggression The term "microaggression" was originally coined by American psychiatrist Professor Chester M. Pierce in the 1970s to describe the subtle insults and demeaning actions he observed towards African Americans in everyday interactions[ 1 ]. Expanding upon this concept through a series of studies, Professor Derald Wing Sue characterized microaggressions as minor yet pervasive verbal, behavioral, and environmental indignities, rooted in either conscious or unconscious bias related to race, ethnicity, gender, sexuality, religion, disability, or other stereotypes[2; 3; 4; 5]. These acts, whether done knowingly or unknowingly, target and adversely affect individuals from marginalized communities. Sue and his colleagues further delineated microaggressions into three categories: microassaults, microinsults, and microinvalidations. Microassaults represent the most direct and apparent form of discrimination, often manifesting intentionally and akin to traditional discriminatory behaviors. Compared to other types of microaggressions, they may emerge in daily life in more covert or socially accepted manners. Microinsults refer to those behaviors or statements that are typically expressed unconsciously, conveying disdain or disrespect for an individual's race, gender, sexual orientation, or other identities. Microinvalidations are comments or actions that negate or overlook the identities and experiences of individuals. By denying the personal experiences and feelings of individuals, microinvalidations contribute to their feelings of marginalization and isolation. Research on microaggressions in China is relatively limited. Investigations within Taiwan province on the topic of homosexuality have unearthed contributing factors to microaggressions and their detrimental effects on psychological well-being[6; 7; 8]. A qualitative analysis conducted in Jinan Province regarding disabled children highlighted that microaggressions, as encountered under current educational policies, lead to significant disciplinary repercussions[ 9 ]. A study from Hong Kong pointed out that both benevolent and hostile forms of ageism serve as predictors for hostile ageist microaggressions[ 10 ]. Additionally, various other research endeavors have delved into the occurrence and adverse implications of microaggressions among diverse societal groups, exploring areas such as HIV/AIDS[ 11 ], gender[ 12 ], and professional sectors[ 13 ]. Although research on microaggressions in China has started later compared to Western countries, and there is yet to be a unified translation standard for the term "microaggression," these factors underscore the significance of conducting in-depth investigations into microaggressions within the Chinese context. The variation in translations might reflect the diverse interpretations of this concept against the backdrop of Chinese social and cultural settings. Therefore, exploring and understanding the manifestations and impacts of microaggressions among different groups in China holds particular importance for unveiling and addressing subtle forms of discrimination in society. 1.2 Microaggressions in the Healthcare Industry The healthcare industry is a pivotal area for the study of microaggressions, particularly in the interactions between healthcare professionals and patients. The inherent power imbalance between patients and healthcare professionals, stemming from differences in health status, medical knowledge, and perceived authority, typically results in greater attention towards patients[14; 15; 16]. However, in recent years, there has been an increasing awareness of microaggressions perpetrated by patients against healthcare staff. The three types of microaggressions previously mentioned are also prevalent in clinical settings. For instance, in China, some patients, dissatisfied with a doctor's title, may directly request a physician with a higher title, an act of microassault that constitutes direct and clear discriminatory behavior. Patients often mention diagnoses or opinions they have found online in front of their treating physicians, which can be seen as microinvalidations, as it questions the professional judgment of healthcare personnel. Furthermore, some patients refer to nurses by diminutive names such as "Little Zhang" or "Little Li," a form of microinsult that conveys a sense of superiority and diminishes the staff, implying their lower status or worth. Clearly, the microaggressions from patients towards healthcare personnel manifest in various forms[ 17 ]. Gender significantly influences the microaggressions experienced by healthcare professionals. For instance, female physicians report a notably higher frequency of gender-specific microaggressions compared to their male counterparts[ 18 ]. Women in training positions are more inclined to acknowledge personal encounters with gender-based bias, discrimination, and sexual harassment than men[ 19 ]. Moreover, female students aspiring to a career in surgery have faced sexual harassment and gender discrimination, which can impact their formation of professional identity and choice of specialty[ 20 ]. In addition to other personal characteristics, racial and ethnicity-based microaggressions are highly prevalent among physicians in perioperative settings. A significant 81% of surgeons and anesthesiologists from racial and ethnic minority groups reported experiencing microaggressions[ 21 ]. However, due to the predominant Han ethnicity composition in China, the concept of multiculturalism as understood in the West may not be as pronounced, potentially leading to less public discourse on racial diversity and related issues. Sexual minorities, including members of the lesbian, gay, bisexual, transgender, queer/questioning, intersex, and asexual (LGBTQIA) communities, encounter numerous challenges in society and the workplace. These challenges encompass mental health issues, discrimination, and an increased risk of burnout[ 22 ]. In China, discussions around gender and sexual orientation are considered private matters and are not widely engaged in public or mainstream media, further limiting the visibility of these groups. It is evident that due to a complex interplay of historical, cultural, social, and political factors, the sources of microaggressions experienced by healthcare professionals can vary across different countries. 1.3 Effect of Microaggressions The implications of microaggressions for mental health are particularly concerning because they often occur in medical settings where healthcares should feel safe and supported. Research has demonstrated that microaggressions are linked to anxiety and depression among those subjected to them[ 23 ]. When faced with biased behavior, which spans from patient refusal of care to explicit racist, sexist, or homophobic remarks, as well as belittling compliments or jokes, targeted physicians have reported experiencing an emotional burden characterized by exhaustion, self-doubt, and cynicism[ 17 ]. It is crucial to acknowledge that medical students and residents are already at a heightened risk of stress and burnout compared to their peers of the same age, owing to the rigorous nature of medical training and the stressful conditions of their work[ 24 ]. Experiencing microaggressions may exacerbate this risk. Environments with higher psychological safety are positively correlated with enhanced performance, knowledge sharing, and creativity[ 25 ]. Over time, microaggressions can erode the job satisfaction of healthcare professionals, lead to professional burnout, and diminish work efficiency. Notably, women physicians experience higher levels of burnout compared to their male counterparts[ 26 ]. These subtle forms of discrimination and bias degrade the work environment, fostering a culture of exclusion and disrespect that can undermine the morale of medical staff. This, in turn, can adversely affect patient care and overall healthcare outcomes. 1.4 Strategies for Addressing Microaggressions As targets of microaggressions, healthcare professionals must master effective coping strategies. Sue and colleagues propose four approaches[ 27 ]: Making the Invisible Visible, which raises awareness about subtle behaviors or comments not easily recognized as microaggressions, thereby shedding light on their often-overlooked impact. Disarming the Microaggression entails swift, non-confrontational actions to neutralize its harmful effects, aiming to halt the cycle of harm. Educating the Perpetrator involves informing them about the harm their actions or words cause, aiming for long-term behavioral change by fostering empathy and awareness. Lastly, Seeking External Reinforcement or Support becomes necessary when microaggressions are systemic or beyond immediate resolution, involving reporting to higher authorities or seeking mentorship. These strategies collectively provide a structured framework for navigating and mitigating the effects of microaggressions, fostering a more respectful and healthier working environment for healthcare professionals. Hospitals and institutions, as workplaces for healthcare professionals, can offer a range of protections and support to address microaggressions from patients towards healthcare professions. By developing clear policies, behaviors that are acceptable can be defined through signage, brochures, and digital platforms, aiming to prevent or reduce the occurrence of microaggressions[ 28 ]. Through training and education, all hospital staff can receive regular training to recognize, respond to, and report microaggressions[ 29 ]. Establishing and communicating clear mechanisms for reporting microaggressive behavior ensures that employees feel safe and supported when reporting incidents[ 30 ]. Cultivating a culture of respect and inclusion within the hospital, valuing diversity, and emphasizing the dignity of every individual are essential steps in this process. As bystanders who witness microaggressions, they play a crucial role in combating the phenomenon[ 31 ]. Understanding the harm caused by microaggressions can further empower bystanders to resist occurrences of such behavior. Encouragement can be given to patients and their families to provide feedback to medical institutions about observed microaggressions, or to show support for healthcare professionals who become targets of these attacks. Colleagues can also offer each other education and support, for instance, discussing discriminatory experiences with peers can aid in coping with patient aggression and reinforce the understanding that they are not alone[ 32 ]. 1.5 Objectives Microaggressions are subtle and often invisible, yet they are a pervasive phenomenon within the medical industry. They can significantly impact healthcare professionals' mental health, lead to professional burnout, and decrease work efficiency. Timely and accurate identification and response to microaggressions can mitigate their adverse effects on healthcare personnel. While extensive research has been conducted in this area, it predominantly focuses on Western developed countries. Given the considerable differences in the healthcare systems and cultural contexts between China and Western nations, directly applying foreign research findings may not be entirely appropriate within the Chinese context. Therefore, conducting research on microaggressions against healthcare personnel in China is crucial. Given the scarcity of studies on microaggressions faced by medical staff in the country, this study aims to undertake a preliminary exploration to understand the current situation of microaggressions encountered by healthcare workers. The objectives of this study are: To conduct a pilot study investigating the current status of microaggressions experienced by medical staff in a Chinese hospital, setting the stage for further cross-sectional research. To examine the differences in microaggressions perceived by doctors and nurses and to explore the factors (including gender, age, education level, rank, department, language, religious beliefs, family situation, lifestyle habits, and other basic characteristics) influencing these microaggressions. Our hypotheses are as follows: Doctors and nurses encounter a wide range of microaggressions, and the experiences between the two groups may vary. The microaggressions faced by both doctors and nurses are influenced by multiple factors, with the influencing factors differing between the two groups. 2. Methods 2.1 Procedure The research team disseminated the electronic survey questionnaires and filling instructions via the hospital's internal staff WeChat groups, enabling the staff to complete the surveys online on their personal electronic devices. Upon accessing the survey link, participants were first required to thoroughly read through an informed consent form. To proceed with the survey, they needed to select the option "I have read and agree to the informed consent for this study." Conversely, if they chose not to participate, by clicking "I do not agree to participate," a message "Thank you for your participation" would appear, and the session would end. For those who consented, instructions were provided, stating: This study is designed to explore the experiences of microaggressions, anxiety, and depression among medical staff in their professional settings. Respondents are encouraged to answer honestly based on their actual experiences, as there are no right or wrong answers. Completing the questionnaire is expected to take about 20 minutes. Participants could request a report of the findings from the researchers upon completion of the questionnaire. Ultimately, 199 individuals submitted their responses, with the final sample for analysis comprising 190 participants (Fig. 1 ). 2.2 Participants Recruitment and data collection were conducted at Deyang People's Hospital from March 2023 to April 2023. Deyang People's Hospital stands as a comprehensive national level 3 hospital with staff from all over China, which is a medical and preventive technology center that holds a pivotal role in providing comprehensive medical, educational, and research services to the whole nation. Its influence spans various regions, provinces, and cities, extending its services to a vast demographic. Notably, it caters to the healthcare needs of over 4 million residents in Deyang city and surrounding counties. Inclusion criteria for participants were as follows: (1) individuals serving as healthcare professionals at Deyang People's Hospital, (2) officially employed healthcare staff, (3) minimum of one year of practical experience in healthcare work, (4) aged between 18 and 60 years, and (5) willingness to engage in the survey and provide informed consent by signing the consent form. Exclusion criteria were defined as follows: (1) refusal to engage in the survey and (2) informal healthcare workers (e.g., intern students). This study was approved by the Ethics Committee of Deyang People's Hospital(2023-04-007-K01). 2.3 Variables and measures Data collection was carried out with Wenjuanxing( https://www.wjx.cn ), which involved a self-designed demographic questionnaire, the Everyday Discrimination Scale-9 items (EDS), 2.3.1 A self-designed general demographic questionnaire was utilized to collect essential demographic information. The questionnaire encompassed a wide array of variables including gender, age, educational attainment, major field of specialization, occupation, professional title, years of work experience, and family structure, etc. 2.3.2 the Everyday Discrimination Scale-9 items (EDS) The EDS[ 33 ], originally developed by David R. Williams et al. at the University of Michigan in 1997, distinguishes itself from questionnaires specifically designed for a particular demographic. The EDS is capable of measuring unfair treatment along with more chronic, routine, and minor experiences of discrimination, making it applicable across a wide range of identities. It has been extensively used to assess experiences of discrimination across various domains. Han et al[ 34 ]. revised the Chinese version in 2019(Cronbach's α = 0.908). For the present study, a modified version consisting of 9 items was employed. Participants were prompted to rate each item on a Likert 4-point scale, where a score of 1 indicated frequent occurrences, 2 indicated occasional occurrences, 3 indicated rare occurrences, and 4 indicated no occurrence, thereby establishing a cumulative score range of 9 to 36. Lower scores corresponded to higher severity of experienced discrimination. The primary focus of this study centered on examining microaggressions encountered by healthcare professionals within their occupational milieu. In alignment with this objective, a crucial adaptation was made to the original questionnaire, wherein the term "others" was duly substituted with the more pertinent reference of "patients and their families." A diminished score indicated an elevated extent of microaggressions perceived by healthcare workers (α = 0.83, corrected item-total correlation of each item ≥ 0.3) (see Table 1 ). Table 1 Modified version of the Everyday Discriminations Scale No. Original Version Modified version Modified version in Chinese 1 You are treated with less courtesy than other people are. Compared with other medical staffs, you are treated with less courtesy than others are. 和其他医务人员相比, 受到更不礼貌的对待。 2 You are treated with less respect than other people are. Compared with other medical staff, you are treated with less respect than others are. 和其他医务人员相比, 更不被尊重。 3 People act as if they think you are not smart. Your patients or their family seem to think you are slow to respond. 患者或家属似乎认为您反应较慢。 4 People act as if they are afraid of you. Your patients or their family seem to be afraid to approach you. 患者或家属似乎害怕接近您。 5 People act as if they think you are dishonest. Your patients or their family think you are dishonest. 您常被患者或家属误解为不诚实。 6 People act as if they’re better than you are. Your patients or their family act like they are superior to you. 您的患者或家属觉得高您一等。 7 You are called names or insulted. You are called names by your patients or their family. 您被患者或家属叫名字(例如: 小张、小李等)。 8 You are threatened or harassed. Your patients or their family ignore you. 患者或家属忽视您或根本无视您的存在。 9 You receive poorer service than other people at restaurants or stores. Your patients wish or ask to make a change in their doctor. 您的患者或家属提出更换医生的要求。 2.4 Statistical Analysis Data were statistically analyzed with IBM SPSS 28.0 software. Skewed distribution measurement data were described as median, quartiles, and range of values; counting data were described as cases (%); Pearson's chi-square test and Fisher's exact test were used to compare the variability of rates across multiple groups; correlations between measures of skewed distribution were analyzed with Spearman's correlation. Variables were merged and assigned values as detailed in Table 5 . Univariate logistic regression analysis was employed to screen variable exerting an influence on the experience of microaggressions. Multivariate logistic regression analysis was undertaken to explore independent risk factors associated with experiencing microaggressions, as well as independent protective factors. 3. RESULTS 3.1 Sample Characteristics Among the 190 participants, there were 83 nurses [82(98.8%) female, 40(37.4%) aged above 35] and 107 doctors [54(50.5%) female, 34(40.1%) aged above 35]. Almost half of (47.4%) healthcare workers reported experiencing microaggressions. The incidence of microaggressions was observed to be lower among male participants compared to their female counterparts, with this difference proving to be statistically significant (p<0.05). Notably, 40(37.4%) doctors and 50(60.2%) nurses reported experiencing microaggressions, with the proportion among nurses being significantly higher than among doctors (p = 0.002). Furthermore, a statistically significant difference was observed in the incidence of microaggressions between healthcare workers with master’s or doctoral degrees and those with bachelor’s or specialist’s degrees. In addition, individuals who predominantly spoke Mandarin experienced fewer microaggressions in the workplace compared to healthcare workers whose language preference leaned towards dialects (p<0.05). see Table 2 . Table 2 Demographic Characteristics【Me (P25, P75), range/ n (%)】 Variable doctor nurse χ 2 / Mann-Whitney U P value Gender Male 53(49.5) 1(1.2) 53.663 0.000 Female 54(50.5) 82(98.8) Age 18 ~ 30 26(24.3) 23(27.7) 1.199 0.753 31 ~ 35 41(38.3) 26(31.3) 36 ~ 40 22(20.6) 17(20.5) 41 ~ 60 18(16.8) 17(20.5) Years of work 7(4,13),1 ~ 32 10 (7,16),1 ~ 40 3149.000 0.001 Educational attainment Bachelor/Specialist 0(0.0) 12(14.5) 66.843 0.000 Master 46(43.0) 68(81.9) PhD 61(57) 3(3.6) Local person No 75(70.1) 32(38.6) 18.900 0.000 Yes 32(29.9) 51(61.4) BMI 22.4(20.4,24.2),18.0 ~ 28.7 20.8(19.3,22.1),15.6 ~ 29.1 2875.500 0.000 Smoking No 102(95.3) 83(100.0) 0.069 Yes 5(4.7) 0(0.0) Drinking No 76(71.0) 73(88.0) 7.911 0.004 Yes 31(29.0) 10(12.0) Title Junior 38(35.5) 48(57.8) 17.592 0.000 Intermediate 44(41.1) 32(38.6) Senior 25(23.4) 3(3.6) Department Internal 51(47.7) 59(71.1) 13.303 0.004 surgical 34(31.8) 10(12.0) auxiliary 11(10.3) 9(8.7) A&E 11(10.3) 5(6.0) Married No 29(27.1) 19(25.3) 0.439 0.508 Yes 78(72.9) 64(74.7) Family structure Nuclear family 62(57.9) 49(59.0) 0.970 Big family 39(36.4) 29(34.9) Other structure 6(5.6) 5(6.0) Number of children 0 40(37.4) 20(24.0) 5.297 0.070 1 47(43.9) 50(60.2) ≥ 2 20(18.7) 13(15.7) Main language spoken at workplace Mandarin 40(37.4) 15(18.1) 8.475 0.004 Local dialect 67(62.6) 68(81.9) Religious beliefs No 103(96.3) 76(91.6) 0.215 Yes 4(3.7) 7(8.4) 3.2 Medical staffs experience for EDS. To facilitate analysis, we dichotomized each EDS item into two groups: ‘never’ (coded as 0) which included individuals reporting 'never' or 'less than once a year,' and 'ever' (coded as 1), encompassing those reporting 'once a month or every week' or 'once per day.' (α = 0.72). The prevalence of experiencing microaggressions was higher among nurses than among physicians (p = 0.02), with more than half (60.2%) experiencing microaggressions. see Table 3 . Table 3 Medical staffs experience for EDS Variable Me (P25, P75), range/ n (%) Mann-Whitney U/χ 2 P value doctor nurse EDS total score 32(28,34),13 ~ 36 32(29,35),18 ~ 36 4134.500 0.413 total experienced 40(37.4) 50(60.2) 3425.500 0.002 Item-1 (experienced) 15(14.0) 14(16.9) 4314.000 0.589 Item-2 (experienced) 10(9.3) 8(9.6) 4427.500 0.946 Item-3 (experienced) 6(5.6) 2(2.4) 4298.500 0.278 Item-4 (experienced) 2(1.9) 2(2.4) 4416.500 0.797 Item-5 (experienced) 5(4.7) 2(2.4) 4340.000 0.413 Item-6 (experienced) 14(13.1) 6(7.2) 4180.500 0.193 Item-7 (experienced) 17(15.9) 39(47.0) 3059.500 0.000 Item-8 (experienced) 13(12.1) 11(13.3) 4391.500 0.821 Item-9 (experienced) 3(2.8) 3(3.6) 4404.500 0.752 3.3 Factors influencing the experience of microaggressions. We dichotomized each EDS item into two groups (see 3.2) to assess whether participant experienced microaggressions or not. This cumulative sum effectively captured the overall instances experienced, with a sum equal to or greater than 1 signifying an 'ever' experience, and a sum of 0 denoting 'never' experienced situations. see Table 4 . Table 4 Variable Assignment Table Variable assigned values Gender Female = 0, Male = 1 Age 18 ~ 35 = 0, 36 ~ 60 = 1 Educational attainment Bachelor's and specialist's degree = 0, Master's and PhD degrees = 1 Local person (Deyang City) No = 0, Yes = 1 BMI <24 = 0, ≥ 24 = 1 Smoking No = 0, Yes = 1 Drinking No = 0, Yes = 1 Title Junior = 0, Intermediate = 1, Senior = 2 Department Internal = 0 surgical = 1 auxiliary = 2 A&E = 3 Married No = 0, Yes = 1 Family structure nuclear family = 0 big family = 1 other structure = 2 Number of children 0 = 0, 1 = 1, ≥ 2 = 2 Main languages spoken at work Mandarin = 0, Dialect = 1 Religious beliefs No = 0, Yes = 1 A univariate logistic regression analysis was conducted to assess the potential factors influencing the experience of microaggressions. The preliminary results indicated several potential factors associated with the experience of microaggressions. see Table 5 . Table 5 Univariate Logistic Regression Analysis of the Experience of Microaggressions doctor nurse Risk factors β SE Waldχ 2 OR 95% CI P value β SE Waldχ 2 OR 95% CI P value Gender (ref: female) male -0.452 0.403 1.257 0.636 0.289 ~ 1.402 0.262 - - - - - - Age (ref: 18 ~ 35) 36 ~ 60 -0.516 0.425 1.476 0.597 0.260 ~ 1.372 0.597 0.533 0.466 1.309 1.704 0.684 ~ 4.244 0.253 Educational attainment (ref: bachelor & specialist) master & PhD -0.293 0.403 0.529 0.746 0.339 ~ 1.643 0.467 0.288 1.246 0.053 1.333 0.116 ~ 15.325 0.817 Local person (ref: no) yes -0.588 0.458 1.650 0.555 0.226 ~ 1.362 0.199 0.059 0.460 0.016 1.061 0.430 ~ 2.614 0.898 BMI (ref: <24) ≥ 24 0.328 0.440 0.556 1.388 0.586 ~ 0.586 0.456 -0.118 0.799 0.022 0.889 0.186 ~ 4.258 0.883 Smoking (ref: no) yes 0.116 0.936 0.015 1.123 0.179 ~ 7.025 0.901 - - - - - - Drinking (ref: no) yes 0.271 0.436 0.385 1.311 0.558 ~ 3.079 0.535 0.487 0.73 0.446 1.628 0.389 ~ 6.807 0.504 Title (ref: junior) intermediate 0.357 0.453 0.621 1.429 0.588 ~ 3.468 0.431 0.174 0.468 0.139 1.19 0.476 ~ 2.979 0.709 senior -0.614 0.577 1.133 0.541 0.175 ~ 1.676 0.287 0.357 1.259 0.08 1.429 0.121 ~ 16.857 0.777 Department (ref: internal) surgical 0.396 0.468 0.716 1.486 0.594 ~ 3.718 0.398 0.793 0.836 0.9 2.211 0.429 ~ 11.379 0.343 auxiliary 1.435 0.698 4.226 4.2 1.069 ~ 16.499 0.04 -1.846 0.847 4.753 0.158 0.03 ~ 0.83 0.029 A&E 0.693 0.679 1.042 2 0.528 ~ 7.569 0.307 -0.999 0.953 1.099 0.368 0.057 ~ 2.383 0.294 Married (ref: no) yes 0.172 0.455 0.143 1.187 0.487 ~ 2.895 0.706 -0.16 0.539 0.087 0.853 0.296 ~ 2.454 0.767 Family structure (ref: nuclear family) big family 0.058 0.421 0.019 1.06 0.464 ~ 2.42 0.89 -1.026 0.485 4.469 0.358 0.138 ~ 0.928 0.035 other structure -0.165 0.905 0.033 0.848 0.144 ~ 0.144 0.855 -0.413 0.964 0.183 0.662 0.1 ~ 4.378 0.668 Number of child (ref: 0) 1 0.547 0.454 1.453 1.728 0.71 ~ 4.208 0.228 -0.164 0.538 0.093 0.848 0.296 ~ 2.436 0.760 ≥ 2 0.442 0.572 0.596 1.556 0.507 ~ 4.774 0.44 0.799 0.801 0.994 2.222 0.462 ~ 10.682 0.319 Main languages spoken at work (ref: mandarin) dialect 0.338 0.42 0.649 1.402 0.616 ~ 3.19 0.421 1.012 0.585 2.991 2.75 0.874 ~ 8.655 0.084 Religious beliefs (ref: no) yes 0.537 1.021 0.277 1.711 0.231 ~ 12.644 0.599 -0.771 0.799 0.929 0.463 0.097 ~ 2.217 0.335 That means there is no value. A binary logistic regression analysis was conducted with experience of microaggression as the dependent variable. The statistically significant factors identified through the initial univariate logistic regression served as independent variables, while gender and age served as covariates. The results showed that the independent risk factor influencing the doctors’ experience of microaggressions was department (reference level = internal; β auxiliary = 1.814, OR auxiliary =6.138, 95% CI auxiliary =1.409 ~ 26.739, P auxiliary = 0.016). The independent protective factors influencing the nurse’s experience of microaggressions was department (reference level = internal; β auxiliary =-2.634, OR auxiliary =0.072, 95% CI auxiliary =0.011 ~ 0.49, P auxiliary = 0.007;) and family structure (reference level = nuclear family; β big family =-1.417, OR big family =0.242, 95% CI big family =0.080 ~ 0.736, P big family = 0.012), and the age (reference level = 18 ~ 35 y; β 36~60 = 1.252, OR 36 ~ 60 =3.497, 95% CI 36 ~ 60 =1.078 ~ 11.35, P 36 ~ 60 = 0.037) emerged as the risk factor. see Table 6 . Table 6 − 1 Binary Logistic Regression Analysis of the Experience of Microaggressions (doctor) Risk factors β SE Waldχ 2 OR 95% CI P value Gender (ref: female) male -0.747 0.455 2.703 0.474 0.194 ~ 1.154 0.10 Age (ref: 18 ~ 35) 36 ~ 60 -0.805 0.475 2.877 0.447 0.176 ~ 1.133 0.09 Department (ref: internal) surgical 0.696 0.514 1.834 2.006 0.732 ~ 5.495 0.176 auxiliary 1.814 0.751 5.839 6.138 1.409 ~ 26.739 0.016 A&E 0.661 0.705 0.878 1.936 0.486 ~ 7.717 0.349 Constant -0.366 0.377 0.942 0.693 0.332 Table 6 − 2 Binary Logistic Regression Analysis of the Experience of Microaggressions (nurse) Risk factors β SE Waldχ 2 OR 95% CI P value Age (ref: 18 ~ 35) 36 ~ 60 1.252 0.601 4.345 3.497 1.078 ~ 11.35 0.037 Department (ref: internal) surgical 0.473 0.877 0.291 1.605 0.287 ~ 8.963 0.59 auxiliary -2.634 0.98 7.225 0.072 0.011 ~ 0.49 0.007 A&E -1.178 1.055 1.248 0.308 0.039 ~ 2.433 0.264 Family structure (ref: nuclear family) big family -1.417 0.567 6.26 0.242 0.080 ~ 0.736 0.012 other structure -0.861 1.03 0.698 0.423 0.056 ~ 3.183 0.403 Constant 0.81 0.385 4.432 2.249 0.035 4. Discussion 4.1 Gender differences in microaggression experience The term microaggression was first coined by American Harvard psychiatrist Chester Pierce in 1970[ 1 ]. Microaggressions are now used as an umbrella term for any derogatory verbal, behavioral, or visual insults directed towards a group of individuals [ 2 , 35 ]. The frequency of microaggressions is very high in healthcare settings. While the behavior of physicians is regulated by industry and hospital professional standards, patients' behavior is not. Although patients and their family often seek care when they are ill and vulnerable, but they still resistance towards healthcare providers. In healthcare settings, physicians and medical interns, including those who identify as people of color, women, and LGBTQIA individuals, are increasingly experiencing microaggressions from patients [ 36 ]. This study reveals that approximately 50% of healthcare workers have experienced microaggressions, the percentage reported stands lower than previous studies. For instance, earlier studies have documented notably higher occurrences, with figures reaching as high as ninety percent among female surgical residents[ 37 ], eighty percentage of female surgeons[ 37 ] and sixty percentage of surgical residents[ 38 ]. Gender microaggressions exist in healthcare organizations, one example is the low number on women and nurses in leadership positions [ 39 ]. Gender-based differentials have been observed within the realm of surgery, where female surgeons often receive fewer referrals, and are disproportionately tasked with nursing responsibilities. Additionally, they are more prone to being substituted by another male surgeon at a patient's request[ 26 ]. The study demonstrates that female healthcare workers are inherently more susceptible to microaggressions than their male counterparts, which is consistent with the findings of previous research by Miller SM[ 40 ]. This finding may be attributed to pervasive and deeply entrenched gender stereotypes[ 41 ] perpetuating the belief that childrearing is solely a female duty. However, it is noteworthy that the percentage of female healthcare workers who experienced microaggression in this study registers a lower percentage when contrasted with the findings of Myers AK's research[ 42 ]. This discrepancy might arise from the diverse cultural backgrounds, along with the inclusion of both doctors and nurse in the study, that reduced gender disparities. Data from the Association of American Medical Colleges (AAMC) illustrate that women constitute 43% of assistant, and 20% of full professors compared with 57%, 67%, and 80%, respectively, for men. These differences in rank are not explained by gender differences in productivity or attrition from the workforce [ 43 ] In leadership roles, women are more likely to occupy clerical positions, roles as residency or fellowship directors, whereas men are more likely to occupy more powerful positions, including department heads, department chairs, and deans [ 43 ]. Research indicates that these disparities in academic medicine are not due to a lack of female physicians, as medical schools have trained equal numbers of men and women for many years [ 44 ]. Additionally, men and women enter academia at roughly equal rates. It can be reasonably concluded that gender differences do indeed exist. Female staff members in the medical community have reported experiencing microaggressions in the form of sexism, prejudice related to pregnancy and parenting, underestimation of competence, inappropriate sexual comments, being relegated to mundane tasks, and feeling marginalized [ 45 ]. However, this study does not investigate data related to microaggressions amongst colleagues and focuses purely on microaggressions perpetrated by patients on behalf of healthcare professionals. The prevalence of these behaviors in healthcare settings has the effect of stigmatizing female and minority physicians, which in turn contributes to the creation of unhealthy workplaces and the phenomenon of physician burnout [ 46 ]. Over time, microaggressions can result in the isolation of female staff members, with the potential for them to leave their jobs and exit the healthcare system entirely. The loss of women in the healthcare system has the knock-on effect of reducing the number of women role models and mentors, which may in turn serve to exacerbate the gender gap in the future [ 47 , 48 ]. 4.2 Differences in microaggression experience between doctors and nurses Microaggressions can have a detrimental impact not only on the well-being of healthcare professionals but also on patients and the quality of patient care, ultimately contributing to health inequities[ 49 ]. In the medical workplace, the abilities of women are frequently undervalued, and they are often tasked with performing a multitude of menial duties, even let them exclusion from teams, activities, and opportunities. The findings of this study underscore a noteworthy discrepancy, revealing that nurses experience higher levels of microaggressions compared to their doctor counterparts. The majority of nursing staff in this study were women, which meant that they were more likely to experience microaggressions. These were not only directed at them by patients but also by colleagues, the impact of these incidents was felt by fewer women and nursing staff in leadership positions. A recurring narrative expressed by patients and their family members, "You could have been a great doctor"[ 50 ], often directed at nurses, accentuates this disparity. The contrasting perceptions of doctors and nurses play a pivotal role in this dynamic. Doctors, owing to the esteemed nature of their profession, tend to enjoy a higher level of respect and reputation among patients. In contrast, nurses are sometimes relegated to a supportive role in the patient's treatment, often performing only simple maneuvers, all of which can inadvertently increase the likelihood of nurses experiencing microaggressions. The participants of this study who were nurses predominantly identified as female. As highlighted earlier, females experienced more microaggressions than males, which could partially account for why nurse in the study also reported more microaggression than doctors. Another noteworthy finding from the study is that healthcare workers with master’s or PhD degrees experienced fewer microaggressions than those with bachelor’s or specialist’s degrees. The disparity in education levels is pronounced between doctors and nurses in this study, where doctors predominantly hold advanced degrees. This educational asymmetry might also contribute to the varying experience of microaggressions among healthcare professionals. The extant literature on the probability of experiencing microaggressions among medical staff of different titles is not consistent. The present study on the phenomenon of experiencing microaggressions among medical staff of different titles is consistent with two Iranian and American studies [ 51 , 52 ] reporting that junior residents are more likely to experience microaggressions than senior residents or attending surgeons. This finding contrasts with the results of another study [ 53 ]. It is regrettable that this paper did not differentiate between the differences in the incidence of microaggressions experienced by medical staff of different specialties. Instead, it simply differentiated between medical, surgical and ancillary departments, with medical staff of internal medicine experiencing higher rates of microaggressions than medical staff of other departments. Recent study found in surgery and surgical subspecialties experienced a higher rate (82.3%) than medical (30.1%) and dentistry (12.9%) [ 53 ]. Furthermore, the linguistic dimension emerges as a factor influencing microaggressions. Mandarin-dominant healthcare workers, owing to the prominence of Mandarin as one of the working languages of the United Nations and its widespread proficiency among over 80% of the Chinese population [ 54 ], encountered fewer instances of microaggressions. The historical context of Deyang, with its role in the Third-Front Movement, introduced a mix of migrants from various regions, many of whom lacked familiarity with local dialects. This linguistic diversity has resulted in communication challenges within clinical settings, potentially contributing to the observed pattern of microaggressions. 4.4 Factors influencing Microaggressions. This study discovered that doctors employed in auxiliary departments face an elevated risk of experiencing microaggressions when compared to their counterparts in internal departments. Doctors who consistently demonstrate greater patience and meticulous attention to detail in their work, as well as those perceived as “amicable, helpful, and supportive” tend to achieve elevated levels of patient satisfaction. However, challenges within auxiliary departments contribute to conflicts and the manifestation of microaggressions. Firstly, these departments often grapple with a high patient volume and rapid patient turnover. Secondly, their primary focus is on providing supplementary examinations, resulting in constrained time for doctors to allocate to each patient, furthermore, doctors in auxiliary departments may encounter difficulties in deciphering the intricate relationship between test results and clinical symptoms. Thirdly, there are instances where patients cannot undergo immediate examinations and need to schedule appointments. Consequently, waiting times range from a few days to several months, prolonging the anticipation period. These extended waiting periods further exacerbate conflicts between patients and doctors in auxiliary departments, leading to the emergence of microaggressions. The study revealed that older age(P 36 − 60 =0.037) was a risk factor for nursing staff when it came to experiencing microaggressions. This may, in part, be attributed to the low educational attainment of the older nurses, as evidenced by a prior study where only 14.6% of them had an undergraduate degree or higher [ 55 ]. Nurses who aged 36–60 years typically boasts a substantial work experience of at least 14 years of work experience, based on the traditional Chinese education (6 years of elementary school, 3 years of junior school, 3 years of junior/senior high school, and 4 years of undergraduate degree). These nurses often contend with burnout, a potential dearth of patience in their clinical responsibilities and staffing shortages[ 56 ]. This situation affects the quality of care provided to patients by diluting services, which increases the likelihood of patients experiencing microaggressions. In contrast, the present study demonstrated that nurses residing in big families, predominantly in the context of three-generation families where the division of labor is clear and elder members responsible for familial and child-related affairs, benefit from a protective effect against encountering microaggressions (P big family = 0.012). This living arrangement empowers nurses to strike a harmonious balance between their professional duties and familial responsibilities, subsequently fostering increased levels of patience and meticulousness in their clinical endeavors. This engenders an atmosphere of friendliness and support among healthcare staff when interacting with patients, effectively mitigating the likelihood of microaggressions transpiring Family members represent a cornerstone of emotional support for nurses, and those who residing within big families are endowed with an augmented degree of such support[ 57 ]. 5. Conclusion Despite an often-covert nature, the detrimental effects of microaggressions are tangible and far reaching, but the study found healthcare workers have faced a significant prevalence of microaggressions, with nurses being particularly affected. The experiences of microaggressions among healthcare workers have exhibited a robust association with anxiety and depression symptoms, underscoring the adverse impact of microaggressions on mental health. Physicians and nurses in different departments reported varying experiences with microaggressions. Additionally, among nurses, those elder and living in a nuclear family (as opposed to big family) were found to be at elevated risk. These findings emphasize the paramount significance of addressing and minimizing the occurrence of microaggressions in healthcare settings, particularly their implications for both healthcare professionals and patients. Implementing comprehensive policies and proactive measures aimed at addressing and minimizing the occurrence of microaggressions is crucial to mitigate their negative impact on the relationship between patients and healthcare providers and foster a healthy work environment. LIMITATIONS In healthcare setting, previous studies have focused on microaggressions experienced by patients, in this study is the first to address microaggressions experienced by healthcare workers. However, the shortcoming is that the sample in this study was confined to a single hospital and a specific geographic area. Also, the absence of before-and-after comparisons limits our ability to determine the causality between microaggressions and the symptoms of anxiety and depression. To enhance the robustness of findings, future studies should consider widening the sample size and encompassing medical professionals from diverse regions. This approach will facilitate a more comprehensive exploration of the factors influencing microaggressions. Declarations Data availability statement The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation. Ethics statement This study adhered to the Declaration of Helsinki was approved by the Ethics Committee of Deyang People's Hospital(2023-04-007-K01), the participants provided their written informed consent to participate in this study. Author contributions Datas collections were conducted by TL, LLL, MZ, and SJ. Analysis and interpretation of data were done by TL, WJY. 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Chinese Jouranl of Health Policy. 2018, 11 (12),56-61. doi: 10.3969/j.issn.1674-2982.2018.12.010 Organization WH, World health statistics 2015, 2018. Kelly EL, Fenwick KM, Brekke JS, Novaco RW. Sources of Social Support After Patient Assault as Related to Staff Well-Being. J Interpers Violence. 2021, 36 (1-2),Np1003-np1028. doi: 10.1177/0886260517738779 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4919288","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":342823970,"identity":"66982e1b-6dba-4ac5-b826-a9fd1796b8b4","order_by":0,"name":"TAO Lv","email":"","orcid":"","institution":"People’s Hospital of Deyang","correspondingAuthor":false,"prefix":"","firstName":"TAO","middleName":"","lastName":"Lv","suffix":""},{"id":342823971,"identity":"6856a91a-cd09-4bf5-aef6-a9ac5984542b","order_by":1,"name":"Wenjie Yan","email":"","orcid":"","institution":"Shanghai Jiao Tong University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Wenjie","middleName":"","lastName":"Yan","suffix":""},{"id":342823972,"identity":"5abceaae-5a10-4a68-9fe4-ce9ca799eede","order_by":2,"name":"longlong Li","email":"","orcid":"","institution":"People’s Hospital of Deyang","correspondingAuthor":false,"prefix":"","firstName":"longlong","middleName":"","lastName":"Li","suffix":""},{"id":342823973,"identity":"6454b600-51b8-4743-85a8-007f0ff433c5","order_by":3,"name":"Shuai Jiang","email":"","orcid":"","institution":"People’s Hospital of Deyang","correspondingAuthor":false,"prefix":"","firstName":"Shuai","middleName":"","lastName":"Jiang","suffix":""},{"id":342823974,"identity":"212dbf83-5c3a-4aa9-937f-b485f985365d","order_by":4,"name":"Min Zhang","email":"","orcid":"","institution":"People’s Hospital of Deyang","correspondingAuthor":false,"prefix":"","firstName":"Min","middleName":"","lastName":"Zhang","suffix":""},{"id":342823975,"identity":"d1112ff8-5f3f-4f68-8c8c-d321d14f08f7","order_by":5,"name":"Yasong Du","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAv0lEQVRIiWNgGAWjYPACZgZ+ZubDD0jTItnOlmZAmhaD8zwKEkSpNTh+/OHjgl/WiZsP8zAYMNTYRBPWcibH2HhmX3ritsO8Bx4wHEvLbSCo5UAOmzRvz2GgFr4EA8aGw0RoOf/8GVjL5mYeAwnitNxIMJPm+XE4cQMzsVokb7wxNuZtSDeecRgYyAnE+IXvfPrDxzx/rGX7+w8ffvChxoawFoUDQIKxDcpLIKQcBOTBhv4hRukoGAWjYBSMWAAACfVCQREhExEAAAAASUVORK5CYII=","orcid":"","institution":"Shanghai Jiao Tong University School of Medicine","correspondingAuthor":true,"prefix":"","firstName":"Yasong","middleName":"","lastName":"Du","suffix":""}],"badges":[],"createdAt":"2024-08-15 12:29:49","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4919288/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4919288/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":66551320,"identity":"371f21e6-7e77-469f-8cd1-83d77096c6ff","added_by":"auto","created_at":"2024-10-14 09:06:49","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":188500,"visible":true,"origin":"","legend":"\u003cp\u003eResearch Flowchart\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-4919288/v1/db737c016217326819a8bc05.png"},{"id":67156773,"identity":"d4d92bb3-a6a6-44e7-ba3a-40604f0106c4","added_by":"auto","created_at":"2024-10-21 18:16:52","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1358099,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4919288/v1/ad78a244-dbe9-486a-89d4-93a62623ded4.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Microaggressions in Medicine: A Pilot Study on Differences and Determinants Among Doctors and Nurses","fulltext":[{"header":"1. Introduction","content":"\u003cdiv id=\"Sec2\" class=\"Section2\"\u003e \u003ch2\u003e1.1 Microaggression\u003c/h2\u003e \u003cp\u003eThe term \"microaggression\" was originally coined by American psychiatrist Professor Chester M. Pierce in the 1970s to describe the subtle insults and demeaning actions he observed towards African Americans in everyday interactions[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Expanding upon this concept through a series of studies, Professor Derald Wing Sue characterized microaggressions as minor yet pervasive verbal, behavioral, and environmental indignities, rooted in either conscious or unconscious bias related to race, ethnicity, gender, sexuality, religion, disability, or other stereotypes[2; 3; 4; 5]. These acts, whether done knowingly or unknowingly, target and adversely affect individuals from marginalized communities.\u003c/p\u003e \u003cp\u003eSue and his colleagues further delineated microaggressions into three categories: microassaults, microinsults, and microinvalidations. Microassaults represent the most direct and apparent form of discrimination, often manifesting intentionally and akin to traditional discriminatory behaviors. Compared to other types of microaggressions, they may emerge in daily life in more covert or socially accepted manners. Microinsults refer to those behaviors or statements that are typically expressed unconsciously, conveying disdain or disrespect for an individual's race, gender, sexual orientation, or other identities. Microinvalidations are comments or actions that negate or overlook the identities and experiences of individuals. By denying the personal experiences and feelings of individuals, microinvalidations contribute to their feelings of marginalization and isolation.\u003c/p\u003e \u003cp\u003eResearch on microaggressions in China is relatively limited. Investigations within Taiwan province on the topic of homosexuality have unearthed contributing factors to microaggressions and their detrimental effects on psychological well-being[6; 7; 8]. A qualitative analysis conducted in Jinan Province regarding disabled children highlighted that microaggressions, as encountered under current educational policies, lead to significant disciplinary repercussions[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. A study from Hong Kong pointed out that both benevolent and hostile forms of ageism serve as predictors for hostile ageist microaggressions[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Additionally, various other research endeavors have delved into the occurrence and adverse implications of microaggressions among diverse societal groups, exploring areas such as HIV/AIDS[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], gender[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], and professional sectors[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAlthough research on microaggressions in China has started later compared to Western countries, and there is yet to be a unified translation standard for the term \"microaggression,\" these factors underscore the significance of conducting in-depth investigations into microaggressions within the Chinese context. The variation in translations might reflect the diverse interpretations of this concept against the backdrop of Chinese social and cultural settings. Therefore, exploring and understanding the manifestations and impacts of microaggressions among different groups in China holds particular importance for unveiling and addressing subtle forms of discrimination in society.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e1.2 Microaggressions in the Healthcare Industry\u003c/h2\u003e \u003cp\u003eThe healthcare industry is a pivotal area for the study of microaggressions, particularly in the interactions between healthcare professionals and patients. The inherent power imbalance between patients and healthcare professionals, stemming from differences in health status, medical knowledge, and perceived authority, typically results in greater attention towards patients[14; 15; 16]. However, in recent years, there has been an increasing awareness of microaggressions perpetrated by patients against healthcare staff. The three types of microaggressions previously mentioned are also prevalent in clinical settings. For instance, in China, some patients, dissatisfied with a doctor's title, may directly request a physician with a higher title, an act of microassault that constitutes direct and clear discriminatory behavior. Patients often mention diagnoses or opinions they have found online in front of their treating physicians, which can be seen as microinvalidations, as it questions the professional judgment of healthcare personnel. Furthermore, some patients refer to nurses by diminutive names such as \"Little Zhang\" or \"Little Li,\" a form of microinsult that conveys a sense of superiority and diminishes the staff, implying their lower status or worth. Clearly, the microaggressions from patients towards healthcare personnel manifest in various forms[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eGender significantly influences the microaggressions experienced by healthcare professionals. For instance, female physicians report a notably higher frequency of gender-specific microaggressions compared to their male counterparts[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Women in training positions are more inclined to acknowledge personal encounters with gender-based bias, discrimination, and sexual harassment than men[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Moreover, female students aspiring to a career in surgery have faced sexual harassment and gender discrimination, which can impact their formation of professional identity and choice of specialty[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn addition to other personal characteristics, racial and ethnicity-based microaggressions are highly prevalent among physicians in perioperative settings. A significant 81% of surgeons and anesthesiologists from racial and ethnic minority groups reported experiencing microaggressions[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. However, due to the predominant Han ethnicity composition in China, the concept of multiculturalism as understood in the West may not be as pronounced, potentially leading to less public discourse on racial diversity and related issues. Sexual minorities, including members of the lesbian, gay, bisexual, transgender, queer/questioning, intersex, and asexual (LGBTQIA) communities, encounter numerous challenges in society and the workplace. These challenges encompass mental health issues, discrimination, and an increased risk of burnout[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. In China, discussions around gender and sexual orientation are considered private matters and are not widely engaged in public or mainstream media, further limiting the visibility of these groups. It is evident that due to a complex interplay of historical, cultural, social, and political factors, the sources of microaggressions experienced by healthcare professionals can vary across different countries.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e1.3 Effect of Microaggressions\u003c/h2\u003e \u003cp\u003eThe implications of microaggressions for mental health are particularly concerning because they often occur in medical settings where healthcares should feel safe and supported. Research has demonstrated that microaggressions are linked to anxiety and depression among those subjected to them[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. When faced with biased behavior, which spans from patient refusal of care to explicit racist, sexist, or homophobic remarks, as well as belittling compliments or jokes, targeted physicians have reported experiencing an emotional burden characterized by exhaustion, self-doubt, and cynicism[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. It is crucial to acknowledge that medical students and residents are already at a heightened risk of stress and burnout compared to their peers of the same age, owing to the rigorous nature of medical training and the stressful conditions of their work[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Experiencing microaggressions may exacerbate this risk.\u003c/p\u003e \u003cp\u003eEnvironments with higher psychological safety are positively correlated with enhanced performance, knowledge sharing, and creativity[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Over time, microaggressions can erode the job satisfaction of healthcare professionals, lead to professional burnout, and diminish work efficiency. Notably, women physicians experience higher levels of burnout compared to their male counterparts[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. These subtle forms of discrimination and bias degrade the work environment, fostering a culture of exclusion and disrespect that can undermine the morale of medical staff. This, in turn, can adversely affect patient care and overall healthcare outcomes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e1.4 Strategies for Addressing Microaggressions\u003c/h2\u003e \u003cp\u003eAs targets of microaggressions, healthcare professionals must master effective coping strategies. Sue and colleagues propose four approaches[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]: Making the Invisible Visible, which raises awareness about subtle behaviors or comments not easily recognized as microaggressions, thereby shedding light on their often-overlooked impact. Disarming the Microaggression entails swift, non-confrontational actions to neutralize its harmful effects, aiming to halt the cycle of harm. Educating the Perpetrator involves informing them about the harm their actions or words cause, aiming for long-term behavioral change by fostering empathy and awareness. Lastly, Seeking External Reinforcement or Support becomes necessary when microaggressions are systemic or beyond immediate resolution, involving reporting to higher authorities or seeking mentorship. These strategies collectively provide a structured framework for navigating and mitigating the effects of microaggressions, fostering a more respectful and healthier working environment for healthcare professionals.\u003c/p\u003e \u003cp\u003eHospitals and institutions, as workplaces for healthcare professionals, can offer a range of protections and support to address microaggressions from patients towards healthcare professions. By developing clear policies, behaviors that are acceptable can be defined through signage, brochures, and digital platforms, aiming to prevent or reduce the occurrence of microaggressions[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Through training and education, all hospital staff can receive regular training to recognize, respond to, and report microaggressions[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Establishing and communicating clear mechanisms for reporting microaggressive behavior ensures that employees feel safe and supported when reporting incidents[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Cultivating a culture of respect and inclusion within the hospital, valuing diversity, and emphasizing the dignity of every individual are essential steps in this process.\u003c/p\u003e \u003cp\u003eAs bystanders who witness microaggressions, they play a crucial role in combating the phenomenon[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Understanding the harm caused by microaggressions can further empower bystanders to resist occurrences of such behavior. Encouragement can be given to patients and their families to provide feedback to medical institutions about observed microaggressions, or to show support for healthcare professionals who become targets of these attacks. Colleagues can also offer each other education and support, for instance, discussing discriminatory experiences with peers can aid in coping with patient aggression and reinforce the understanding that they are not alone[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e1.5 Objectives\u003c/h2\u003e \u003cp\u003eMicroaggressions are subtle and often invisible, yet they are a pervasive phenomenon within the medical industry. They can significantly impact healthcare professionals' mental health, lead to professional burnout, and decrease work efficiency. Timely and accurate identification and response to microaggressions can mitigate their adverse effects on healthcare personnel. While extensive research has been conducted in this area, it predominantly focuses on Western developed countries. Given the considerable differences in the healthcare systems and cultural contexts between China and Western nations, directly applying foreign research findings may not be entirely appropriate within the Chinese context. Therefore, conducting research on microaggressions against healthcare personnel in China is crucial. Given the scarcity of studies on microaggressions faced by medical staff in the country, this study aims to undertake a preliminary exploration to understand the current situation of microaggressions encountered by healthcare workers. The objectives of this study are:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eTo conduct a pilot study investigating the current status of microaggressions experienced by medical staff in a Chinese hospital, setting the stage for further cross-sectional research.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eTo examine the differences in microaggressions perceived by doctors and nurses and to explore the factors (including gender, age, education level, rank, department, language, religious beliefs, family situation, lifestyle habits, and other basic characteristics) influencing these microaggressions.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eOur hypotheses are as follows:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eDoctors and nurses encounter a wide range of microaggressions, and the experiences between the two groups may vary.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eThe microaggressions faced by both doctors and nurses are influenced by multiple factors, with the influencing factors differing between the two groups.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Procedure\u003c/h2\u003e \u003cp\u003eThe research team disseminated the electronic survey questionnaires and filling instructions via the hospital's internal staff WeChat groups, enabling the staff to complete the surveys online on their personal electronic devices. Upon accessing the survey link, participants were first required to thoroughly read through an informed consent form. To proceed with the survey, they needed to select the option \"I have read and agree to the informed consent for this study.\" Conversely, if they chose not to participate, by clicking \"I do not agree to participate,\" a message \"Thank you for your participation\" would appear, and the session would end. For those who consented, instructions were provided, stating: This study is designed to explore the experiences of microaggressions, anxiety, and depression among medical staff in their professional settings. Respondents are encouraged to answer honestly based on their actual experiences, as there are no right or wrong answers. Completing the questionnaire is expected to take about 20 minutes. Participants could request a report of the findings from the researchers upon completion of the questionnaire. Ultimately, 199 individuals submitted their responses, with the final sample for analysis comprising 190 participants (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Participants\u003c/h2\u003e \u003cp\u003eRecruitment and data collection were conducted at Deyang People's Hospital from March 2023 to April 2023. Deyang People's Hospital stands as a comprehensive national level 3 hospital with staff from all over China, which is a medical and preventive technology center that holds a pivotal role in providing comprehensive medical, educational, and research services to the whole nation. Its influence spans various regions, provinces, and cities, extending its services to a vast demographic. Notably, it caters to the healthcare needs of over 4\u0026nbsp;million residents in Deyang city and surrounding counties.\u003c/p\u003e \u003cp\u003eInclusion criteria for participants were as follows: (1) individuals serving as healthcare professionals at Deyang People's Hospital, (2) officially employed healthcare staff, (3) minimum of one year of practical experience in healthcare work, (4) aged between 18 and 60 years, and (5) willingness to engage in the survey and provide informed consent by signing the consent form. Exclusion criteria were defined as follows: (1) refusal to engage in the survey and (2) informal healthcare workers (e.g., intern students). This study was approved by the Ethics Committee of Deyang People's Hospital(2023-04-007-K01).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Variables and measures\u003c/h2\u003e \u003cp\u003eData collection was carried out with Wenjuanxing(\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.wjx.cn\u003c/span\u003e\u003cspan address=\"https://www.wjx.cn\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), which involved a self-designed demographic questionnaire, the Everyday Discrimination Scale-9 items (EDS),\u003c/p\u003e \u003cp\u003e \u003cb\u003e2.3.1\u003c/b\u003e A self-designed general demographic questionnaire was utilized to collect essential demographic information. The questionnaire encompassed a wide array of variables including gender, age, educational attainment, major field of specialization, occupation, professional title, years of work experience, and family structure, etc.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e2.3.2 the Everyday Discrimination Scale-9 items (EDS)\u003c/h2\u003e \u003cp\u003eThe EDS[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e], originally developed by David R. Williams et al. at the University of Michigan in 1997, distinguishes itself from questionnaires specifically designed for a particular demographic. The EDS is capable of measuring unfair treatment along with more chronic, routine, and minor experiences of discrimination, making it applicable across a wide range of identities. It has been extensively used to assess experiences of discrimination across various domains. Han et al[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. revised the Chinese version in 2019(Cronbach's α\u0026thinsp;=\u0026thinsp;0.908). For the present study, a modified version consisting of 9 items was employed. Participants were prompted to rate each item on a Likert 4-point scale, where a score of 1 indicated frequent occurrences, 2 indicated occasional occurrences, 3 indicated rare occurrences, and 4 indicated no occurrence, thereby establishing a cumulative score range of 9 to 36. Lower scores corresponded to higher severity of experienced discrimination. The primary focus of this study centered on examining microaggressions encountered by healthcare professionals within their occupational milieu. In alignment with this objective, a crucial adaptation was made to the original questionnaire, wherein the term \"others\" was duly substituted with the more pertinent reference of \"patients and their families.\" A diminished score indicated an elevated extent of microaggressions perceived by healthcare workers (α\u0026thinsp;=\u0026thinsp;0.83, corrected item-total correlation of each item\u0026thinsp;\u0026ge;\u0026thinsp;0.3) (see Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eModified version of the Everyday Discriminations Scale\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOriginal Version\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModified version\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModified version in Chinese\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYou are treated with less courtesy than other people are.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCompared with other medical staffs, you are treated with less courtesy than others are.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e和其他医务人员相比, 受到更不礼貌的对待。\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYou are treated with less respect than other people are.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCompared with other medical staff, you are treated with less respect than others are.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e和其他医务人员相比, 更不被尊重。\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePeople act as if they think you are not smart.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYour patients or their family seem to think you are slow to respond.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e患者或家属似乎认为您反应较慢。\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePeople act as if they are afraid of you.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYour patients or their family seem to be afraid to approach you.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e患者或家属似乎害怕接近您。\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePeople act as if they think you are dishonest.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYour patients or their family think you are dishonest.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e您常被患者或家属误解为不诚实。\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePeople act as if they\u0026rsquo;re better than you are.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYour patients or their family act like they are superior to you.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e您的患者或家属觉得高您一等。\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYou are called names or insulted.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYou are called names by your patients or their family.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e您被患者或家属叫名字(例如: 小张、小李等)。\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYou are threatened or harassed.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYour patients or their family ignore you.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e患者或家属忽视您或根本无视您的存在。\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYou receive poorer service than other people at restaurants or stores.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYour patients wish or ask to make a change in their doctor.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e您的患者或家属提出更换医生的要求。\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Statistical Analysis\u003c/h2\u003e \u003cp\u003eData were statistically analyzed with IBM SPSS 28.0 software. Skewed distribution measurement data were described as median, quartiles, and range of values; counting data were described as cases (%); Pearson's chi-square test and Fisher's exact test were used to compare the variability of rates across multiple groups; correlations between measures of skewed distribution were analyzed with Spearman's correlation. Variables were merged and assigned values as detailed in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. Univariate logistic regression analysis was employed to screen variable exerting an influence on the experience of microaggressions. Multivariate logistic regression analysis was undertaken to explore independent risk factors associated with experiencing microaggressions, as well as independent protective factors.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. RESULTS","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Sample Characteristics\u003c/h2\u003e \u003cp\u003eAmong the 190 participants, there were 83 nurses [82(98.8%) female, 40(37.4%) aged above 35] and 107 doctors [54(50.5%) female, 34(40.1%) aged above 35]. Almost half of (47.4%) healthcare workers reported experiencing microaggressions. The incidence of microaggressions was observed to be lower among male participants compared to their female counterparts, with this difference proving to be statistically significant (p\u0026lt;0.05). Notably, 40(37.4%) doctors and 50(60.2%) nurses reported experiencing microaggressions, with the proportion among nurses being significantly higher than among doctors (p\u0026thinsp;=\u0026thinsp;0.002). Furthermore, a statistically significant difference was observed in the incidence of microaggressions between healthcare workers with master\u0026rsquo;s or doctoral degrees and those with bachelor\u0026rsquo;s or specialist\u0026rsquo;s degrees. In addition, individuals who predominantly spoke Mandarin experienced fewer microaggressions in the workplace compared to healthcare workers whose language preference leaned towards dialects (p\u0026lt;0.05). see Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e Demographic Characteristics【Me (P25, P75), range/ n (%)】\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003edoctor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003enurse\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eχ\u003csup\u003e2\u003c/sup\u003e/ Mann-Whitney U\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP value\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e53(49.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1(1.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e53.663\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e54(50.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e82(98.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18\u0026thinsp;~\u0026thinsp;30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26(24.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23(27.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.199\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.753\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e31\u0026thinsp;~\u0026thinsp;35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e41(38.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26(31.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e36\u0026thinsp;~\u0026thinsp;40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22(20.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17(20.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e41\u0026thinsp;~\u0026thinsp;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18(16.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17(20.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eYears of work\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7(4,13),1\u0026thinsp;~\u0026thinsp;32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10 (7,16),1\u0026thinsp;~\u0026thinsp;40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3149.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEducational attainment\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBachelor/Specialist\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0(0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12(14.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e66.843\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaster\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e46(43.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e68(81.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e61(57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3(3.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLocal person\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e75(70.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32(38.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18.900\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32(29.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e51(61.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBMI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22.4(20.4,24.2),18.0\u0026thinsp;~\u0026thinsp;28.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20.8(19.3,22.1),15.6\u0026thinsp;~\u0026thinsp;29.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2875.500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSmoking\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e102(95.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e83(100.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.069\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5(4.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0(0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDrinking\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e76(71.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e73(88.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.911\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31(29.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10(12.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTitle\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJunior\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38(35.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e48(57.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17.592\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntermediate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e44(41.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32(38.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSenior\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25(23.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3(3.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDepartment\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInternal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e51(47.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e59(71.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13.303\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003esurgical\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34(31.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10(12.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eauxiliary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11(10.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9(8.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA\u0026amp;E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11(10.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5(6.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMarried\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29(27.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19(25.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.439\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.508\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e78(72.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e64(74.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFamily structure\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNuclear family\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e62(57.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e49(59.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.970\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBig family\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e39(36.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29(34.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther structure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6(5.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5(6.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNumber of children\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40(37.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20(24.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.297\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.070\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e47(43.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50(60.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20(18.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13(15.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMain language spoken at workplace\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMandarin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40(37.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15(18.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.475\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLocal dialect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e67(62.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e68(81.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eReligious beliefs\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e103(96.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e76(91.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.215\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4(3.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7(8.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Medical staffs experience for EDS.\u003c/h2\u003e \u003cp\u003eTo facilitate analysis, we dichotomized each EDS item into two groups: \u0026lsquo;never\u0026rsquo; (coded as 0) which included individuals reporting 'never' or 'less than once a year,' and 'ever' (coded as 1), encompassing those reporting 'once a month or every week' or 'once per day.' (α\u0026thinsp;=\u0026thinsp;0.72). The prevalence of experiencing microaggressions was higher among nurses than among physicians (p\u0026thinsp;=\u0026thinsp;0.02), with more than half (60.2%) experiencing microaggressions. see Table \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMedical staffs experience for EDS\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eMe (P25, P75), range/ n (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMann-Whitney U/χ\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eP value\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003edoctor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003enurse\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEDS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003etotal score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32(28,34),13\u0026thinsp;~\u0026thinsp;36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32(29,35),18\u0026thinsp;~\u0026thinsp;36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4134.500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.413\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003etotal experienced\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40(37.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50(60.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3425.500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eItem-1 (experienced)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15(14.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14(16.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4314.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.589\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eItem-2 (experienced)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10(9.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8(9.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4427.500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.946\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eItem-3 (experienced)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6(5.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2(2.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4298.500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.278\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eItem-4 (experienced)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2(1.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2(2.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4416.500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.797\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eItem-5 (experienced)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5(4.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2(2.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4340.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.413\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eItem-6 (experienced)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14(13.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6(7.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4180.500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.193\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eItem-7 (experienced)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17(15.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39(47.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3059.500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eItem-8 (experienced)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13(12.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11(13.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4391.500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.821\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eItem-9 (experienced)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3(2.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3(3.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4404.500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.752\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Factors influencing the experience of microaggressions.\u003c/h2\u003e \u003cp\u003eWe dichotomized each EDS item into two groups (see 3.2) to assess whether participant experienced microaggressions or not. This cumulative sum effectively captured the overall instances experienced, with a sum equal to or greater than 1 signifying an 'ever' experience, and a sum of 0 denoting 'never' experienced situations. see Table \u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eVariable Assignment Table\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eassigned values\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u0026thinsp;=\u0026thinsp;0, Male\u0026thinsp;=\u0026thinsp;1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18\u0026thinsp;~\u0026thinsp;35\u0026thinsp;=\u0026thinsp;0, 36\u0026thinsp;~\u0026thinsp;60\u0026thinsp;=\u0026thinsp;1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducational attainment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBachelor's and specialist's degree\u0026thinsp;=\u0026thinsp;0,\u003c/p\u003e \u003cp\u003eMaster's and PhD degrees\u0026thinsp;=\u0026thinsp;1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLocal person (Deyang City)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u0026thinsp;=\u0026thinsp;0, Yes\u0026thinsp;=\u0026thinsp;1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;24\u0026thinsp;=\u0026thinsp;0, \u0026ge;\u0026thinsp;24\u0026thinsp;=\u0026thinsp;1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u0026thinsp;=\u0026thinsp;0, Yes\u0026thinsp;=\u0026thinsp;1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDrinking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u0026thinsp;=\u0026thinsp;0, Yes\u0026thinsp;=\u0026thinsp;1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTitle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJunior\u0026thinsp;=\u0026thinsp;0, Intermediate\u0026thinsp;=\u0026thinsp;1, Senior\u0026thinsp;=\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDepartment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInternal\u0026thinsp;=\u0026thinsp;0\u003c/p\u003e \u003cp\u003esurgical\u0026thinsp;=\u0026thinsp;1\u003c/p\u003e \u003cp\u003eauxiliary\u0026thinsp;=\u0026thinsp;2\u003c/p\u003e \u003cp\u003eA\u0026amp;E\u0026thinsp;=\u0026thinsp;3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u0026thinsp;=\u0026thinsp;0, Yes\u0026thinsp;=\u0026thinsp;1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFamily structure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003enuclear family\u0026thinsp;=\u0026thinsp;0\u003c/p\u003e \u003cp\u003ebig family\u0026thinsp;=\u0026thinsp;1\u003c/p\u003e \u003cp\u003eother structure\u0026thinsp;=\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of children\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u0026thinsp;=\u0026thinsp;0, 1\u0026thinsp;=\u0026thinsp;1, \u0026ge;\u0026thinsp;2\u0026thinsp;=\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMain languages spoken at work\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMandarin\u0026thinsp;=\u0026thinsp;0, Dialect\u0026thinsp;=\u0026thinsp;1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReligious beliefs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u0026thinsp;=\u0026thinsp;0, Yes\u0026thinsp;=\u0026thinsp;1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eA univariate logistic regression analysis was conducted to assess the potential factors influencing the experience of microaggressions. The preliminary results indicated several potential factors associated with the experience of microaggressions. see Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eUnivariate Logistic Regression Analysis of the Experience of Microaggressions\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"13\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c7\" namest=\"c2\"\u003e \u003cp\u003edoctor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c13\" namest=\"c8\"\u003e \u003cp\u003enurse\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRisk factors\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eβ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eSE\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWaldχ\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eOR\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e95%\u003cem\u003eCI\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eβ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003eSE\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eWaldχ\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cem\u003eOR\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003e95%\u003cem\u003eCI\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"13\" nameend=\"c13\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGender\u003c/b\u003e (ref: female)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.452\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.403\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.257\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.636\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.289\u0026thinsp;~\u0026thinsp;1.402\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.262\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"13\" nameend=\"c13\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge\u003c/b\u003e (ref: 18\u0026thinsp;~\u0026thinsp;35)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e36\u0026thinsp;~\u0026thinsp;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.516\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.425\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.476\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.597\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.260\u0026thinsp;~\u0026thinsp;1.372\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.597\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.533\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.466\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.309\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.704\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.684\u0026thinsp;~\u0026thinsp;4.244\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.253\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"13\" nameend=\"c13\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEducational attainment\u003c/b\u003e (ref: bachelor \u0026amp; specialist)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emaster \u0026amp; PhD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.293\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.403\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.529\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.746\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.339\u0026thinsp;~\u0026thinsp;1.643\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.467\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.288\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.246\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.333\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.116\u0026thinsp;~\u0026thinsp;15.325\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.817\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"13\" nameend=\"c13\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLocal person\u003c/b\u003e (ref: no)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.588\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.458\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.650\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.555\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.226\u0026thinsp;~\u0026thinsp;1.362\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.199\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.059\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.460\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.061\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.430\u0026thinsp;~\u0026thinsp;2.614\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.898\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"13\" nameend=\"c13\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBMI\u003c/b\u003e (ref: \u0026lt;24)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.328\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.440\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.556\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.388\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.586\u0026thinsp;~\u0026thinsp;0.586\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.456\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.118\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.799\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.889\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.186\u0026thinsp;~\u0026thinsp;4.258\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.883\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"13\" nameend=\"c13\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSmoking\u003c/b\u003e (ref: no)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.936\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.179\u0026thinsp;~\u0026thinsp;7.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.901\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"13\" nameend=\"c13\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDrinking\u003c/b\u003e (ref: no)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.271\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.436\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.385\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.311\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.558\u0026thinsp;~\u0026thinsp;3.079\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.535\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.487\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.446\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.628\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.389\u0026thinsp;~\u0026thinsp;6.807\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.504\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"13\" nameend=\"c13\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTitle\u003c/b\u003e (ref: junior)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eintermediate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.357\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.453\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.621\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.429\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.588\u0026thinsp;~\u0026thinsp;3.468\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.431\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.174\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.468\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.139\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.476\u0026thinsp;~\u0026thinsp;2.979\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.709\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003esenior\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.614\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.577\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.133\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.541\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.175\u0026thinsp;~\u0026thinsp;1.676\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.287\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.357\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.259\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.429\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.121\u0026thinsp;~\u0026thinsp;16.857\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.777\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"13\" nameend=\"c13\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDepartment\u003c/b\u003e (ref: internal)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003esurgical\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.396\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.468\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.716\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.486\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.594\u0026thinsp;~\u0026thinsp;3.718\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.398\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.793\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.836\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e2.211\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.429\u0026thinsp;~\u0026thinsp;11.379\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.343\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eauxiliary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.435\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.698\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.226\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.069\u0026thinsp;~\u0026thinsp;16.499\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-1.846\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.847\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e4.753\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.158\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.03\u0026thinsp;~\u0026thinsp;0.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.029\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA\u0026amp;E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.693\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.679\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.528\u0026thinsp;~\u0026thinsp;7.569\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.307\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.953\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.099\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.368\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.057\u0026thinsp;~\u0026thinsp;2.383\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.294\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"13\" nameend=\"c13\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMarried\u003c/b\u003e (ref: no)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.172\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.455\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.187\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.487\u0026thinsp;~\u0026thinsp;2.895\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.706\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.539\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.087\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.853\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.296\u0026thinsp;~\u0026thinsp;2.454\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.767\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"13\" nameend=\"c13\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFamily structure\u003c/b\u003e (ref: nuclear family)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ebig family\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.058\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.421\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.464\u0026thinsp;~\u0026thinsp;2.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-1.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.485\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e4.469\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.358\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.138\u0026thinsp;~\u0026thinsp;0.928\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.035\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eother structure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.165\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.905\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.848\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.144\u0026thinsp;~\u0026thinsp;0.144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.855\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.413\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.964\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.183\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.662\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.1\u0026thinsp;~\u0026thinsp;4.378\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.668\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"13\" nameend=\"c13\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNumber of child\u003c/b\u003e (ref: 0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.547\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.454\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.453\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.728\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.71\u0026thinsp;~\u0026thinsp;4.208\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.228\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.164\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.538\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.093\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.848\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.296\u0026thinsp;~\u0026thinsp;2.436\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.760\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.442\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.572\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.596\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.556\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.507\u0026thinsp;~\u0026thinsp;4.774\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.799\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.801\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.994\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e2.222\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.462\u0026thinsp;~\u0026thinsp;10.682\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.319\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"13\" nameend=\"c13\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMain languages spoken at work\u003c/b\u003e (ref: mandarin)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003edialect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.338\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.649\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.402\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.616\u0026thinsp;~\u0026thinsp;3.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.421\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.585\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2.991\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e2.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.874\u0026thinsp;~\u0026thinsp;8.655\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.084\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"13\" nameend=\"c13\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eReligious beliefs\u003c/b\u003e (ref: no)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.537\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.277\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.711\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.231\u0026thinsp;~\u0026thinsp;12.644\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.599\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.771\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.799\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.929\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.463\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.097\u0026thinsp;~\u0026thinsp;2.217\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.335\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cem\u003eThat means there is no value.\u003c/em\u003e \u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eA binary logistic regression analysis was conducted with experience of microaggression as the dependent variable. The statistically significant factors identified through the initial univariate logistic regression served as independent variables, while gender and age served as covariates. The results showed that the independent risk factor influencing the doctors\u0026rsquo; experience of microaggressions was department (reference level\u0026thinsp;=\u0026thinsp;internal; β\u003csub\u003eauxiliary\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;1.814, \u003cem\u003eOR\u003c/em\u003e\u003csub\u003eauxiliary\u003c/sub\u003e=6.138, 95%\u003cem\u003eCI\u003c/em\u003e\u003csub\u003eauxiliary\u003c/sub\u003e =1.409\u0026thinsp;~\u0026thinsp;26.739, \u003cem\u003eP\u003c/em\u003e\u003csub\u003eauxiliary\u003c/sub\u003e\u003cem\u003e=\u003c/em\u003e0.016). The independent protective factors influencing the nurse\u0026rsquo;s experience of microaggressions was department (reference level\u0026thinsp;=\u0026thinsp;internal; β\u003csub\u003eauxiliary\u003c/sub\u003e=-2.634, \u003cem\u003eOR\u003c/em\u003e\u003csub\u003eauxiliary\u003c/sub\u003e=0.072, 95%\u003cem\u003eCI\u003c/em\u003e\u003csub\u003eauxiliary\u003c/sub\u003e =0.011\u0026thinsp;~\u0026thinsp;0.49, \u003cem\u003eP\u003c/em\u003e\u003csub\u003eauxiliary\u003c/sub\u003e\u003cem\u003e=\u003c/em\u003e0.007;) and family structure (reference level\u0026thinsp;=\u0026thinsp;nuclear family; β\u003csub\u003ebig family\u003c/sub\u003e=-1.417, \u003cem\u003eOR\u003c/em\u003e \u003csub\u003ebig family\u003c/sub\u003e=0.242, 95%\u003cem\u003eCI\u003c/em\u003e\u003csub\u003ebig family\u003c/sub\u003e =0.080\u0026thinsp;~\u0026thinsp;0.736, \u003cem\u003eP\u003c/em\u003e\u003csub\u003ebig family\u003c/sub\u003e \u003cem\u003e=\u003c/em\u003e0.012), and the age (reference level\u0026thinsp;=\u0026thinsp;18\u0026thinsp;~\u0026thinsp;35 y; β\u003csub\u003e36~60\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;1.252, \u003cem\u003eOR\u003c/em\u003e\u003csub\u003e36\u0026thinsp;~\u0026thinsp;60\u003c/sub\u003e=3.497, 95%\u003cem\u003eCI\u003c/em\u003e\u003csub\u003e36\u0026thinsp;~\u0026thinsp;60\u003c/sub\u003e=1.078\u0026thinsp;~\u0026thinsp;11.35, \u003cem\u003eP\u003c/em\u003e\u003csub\u003e36\u0026thinsp;~\u0026thinsp;60\u003c/sub\u003e\u003cem\u003e=\u003c/em\u003e0.037) emerged as the risk factor. see Table \u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e6\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u0026thinsp;\u0026minus;\u0026thinsp;1 Binary Logistic Regression Analysis of the Experience of Microaggressions (doctor)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRisk factors\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eβ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eSE\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWaldχ\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eOR\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e95%\u003cem\u003eCI\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eGender (ref: female)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.747\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.455\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.703\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.474\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.194\u0026thinsp;~\u0026thinsp;1.154\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge (ref: 18\u0026thinsp;~\u0026thinsp;35)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e36\u0026thinsp;~\u0026thinsp;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.805\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.475\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.877\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.447\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.176\u0026thinsp;~\u0026thinsp;1.133\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDepartment (ref: internal)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003esurgical\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.696\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.514\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.834\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.732\u0026thinsp;~\u0026thinsp;5.495\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.176\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eauxiliary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.814\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.751\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.839\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.138\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.409\u0026thinsp;~\u0026thinsp;26.739\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA\u0026amp;E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.661\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.705\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.878\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.936\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.486\u0026thinsp;~\u0026thinsp;7.717\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.349\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eConstant\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.366\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.377\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.942\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.693\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.332\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u0026thinsp;\u0026minus;\u0026thinsp;2 Binary Logistic Regression Analysis of the Experience of Microaggressions (nurse)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRisk factors\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eβ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eSE\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWaldχ\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eOR\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e95%\u003cem\u003eCI\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eAge (ref: 18\u0026thinsp;~\u0026thinsp;35)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e36\u0026thinsp;~\u0026thinsp;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.252\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.601\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.345\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.497\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.078\u0026thinsp;~\u0026thinsp;11.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.037\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDepartment (ref: internal)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003esurgical\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.473\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.877\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.291\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.605\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.287\u0026thinsp;~\u0026thinsp;8.963\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eauxiliary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-2.634\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.225\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.072\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.011\u0026thinsp;~\u0026thinsp;0.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA\u0026amp;E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.178\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.248\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.308\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.039\u0026thinsp;~\u0026thinsp;2.433\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.264\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFamily structure\u003c/b\u003e (ref: nuclear family)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ebig family\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.417\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.567\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.242\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.080\u0026thinsp;~\u0026thinsp;0.736\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eother structure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.861\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.698\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.423\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.056\u0026thinsp;~\u0026thinsp;3.183\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.403\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eConstant\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.385\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.432\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.249\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.035\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Gender differences in microaggression experience\u003c/h2\u003e \u003cp\u003eThe term microaggression was first coined by American Harvard psychiatrist Chester Pierce in 1970[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Microaggressions are now used as an umbrella term for any derogatory verbal, behavioral, or visual insults directed towards a group of individuals [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. The frequency of microaggressions is very high in healthcare settings. While the behavior of physicians is regulated by industry and hospital professional standards, patients' behavior is not. Although patients and their family often seek care when they are ill and vulnerable, but they still resistance towards healthcare providers. In healthcare settings, physicians and medical interns, including those who identify as people of color, women, and LGBTQIA individuals, are increasingly experiencing microaggressions from patients [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. This study reveals that approximately 50% of healthcare workers have experienced microaggressions, the percentage reported stands lower than previous studies. For instance, earlier studies have documented notably higher occurrences, with figures reaching as high as ninety percent among female surgical residents[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e], eighty percentage of female surgeons[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e] and sixty percentage of surgical residents[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Gender microaggressions exist in healthcare organizations, one example is the low number on women and nurses in leadership positions [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Gender-based differentials have been observed within the realm of surgery, where female surgeons often receive fewer referrals, and are disproportionately tasked with nursing responsibilities. Additionally, they are more prone to being substituted by another male surgeon at a patient's request[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. The study demonstrates that female healthcare workers are inherently more susceptible to microaggressions than their male counterparts, which is consistent with the findings of previous research by Miller SM[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. This finding may be attributed to pervasive and deeply entrenched gender stereotypes[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e41\u003c/span\u003e] perpetuating the belief that childrearing is solely a female duty. However, it is noteworthy that the percentage of female healthcare workers who experienced microaggression in this study registers a lower percentage when contrasted with the findings of Myers AK's research[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. This discrepancy might arise from the diverse cultural backgrounds, along with the inclusion of both doctors and nurse in the study, that reduced gender disparities.\u003c/p\u003e \u003cp\u003eData from the Association of American Medical Colleges (AAMC) illustrate that women constitute 43% of assistant, and 20% of full professors compared with 57%, 67%, and 80%, respectively, for men. These differences in rank are not explained by gender differences in productivity or attrition from the workforce [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e43\u003c/span\u003e] In leadership roles, women are more likely to occupy clerical positions, roles as residency or fellowship directors, whereas men are more likely to occupy more powerful positions, including department heads, department chairs, and deans [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Research indicates that these disparities in academic medicine are not due to a lack of female physicians, as medical schools have trained equal numbers of men and women for many years [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Additionally, men and women enter academia at roughly equal rates. It can be reasonably concluded that gender differences do indeed exist. Female staff members in the medical community have reported experiencing microaggressions in the form of sexism, prejudice related to pregnancy and parenting, underestimation of competence, inappropriate sexual comments, being relegated to mundane tasks, and feeling marginalized [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. However, this study does not investigate data related to microaggressions amongst colleagues and focuses purely on microaggressions perpetrated by patients on behalf of healthcare professionals. The prevalence of these behaviors in healthcare settings has the effect of stigmatizing female and minority physicians, which in turn contributes to the creation of unhealthy workplaces and the phenomenon of physician burnout [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Over time, microaggressions can result in the isolation of female staff members, with the potential for them to leave their jobs and exit the healthcare system entirely. The loss of women in the healthcare system has the knock-on effect of reducing the number of women role models and mentors, which may in turn serve to exacerbate the gender gap in the future [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e48\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Differences in microaggression experience between doctors and nurses\u003c/h2\u003e \u003cp\u003eMicroaggressions can have a detrimental impact not only on the well-being of healthcare professionals but also on patients and the quality of patient care, ultimately contributing to health inequities[\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. In the medical workplace, the abilities of women are frequently undervalued, and they are often tasked with performing a multitude of menial duties, even let them exclusion from teams, activities, and opportunities. The findings of this study underscore a noteworthy discrepancy, revealing that nurses experience higher levels of microaggressions compared to their doctor counterparts. The majority of nursing staff in this study were women, which meant that they were more likely to experience microaggressions. These were not only directed at them by patients but also by colleagues, the impact of these incidents was felt by fewer women and nursing staff in leadership positions. A recurring narrative expressed by patients and their family members, \"You could have been a great doctor\"[\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e50\u003c/span\u003e], often directed at nurses, accentuates this disparity. The contrasting perceptions of doctors and nurses play a pivotal role in this dynamic. Doctors, owing to the esteemed nature of their profession, tend to enjoy a higher level of respect and reputation among patients. In contrast, nurses are sometimes relegated to a supportive role in the patient's treatment, often performing only simple maneuvers, all of which can inadvertently increase the likelihood of nurses experiencing microaggressions. The participants of this study who were nurses predominantly identified as female. As highlighted earlier, females experienced more microaggressions than males, which could partially account for why nurse in the study also reported more microaggression than doctors.\u003c/p\u003e \u003cp\u003eAnother noteworthy finding from the study is that healthcare workers with master\u0026rsquo;s or PhD degrees experienced fewer microaggressions than those with bachelor\u0026rsquo;s or specialist\u0026rsquo;s degrees. The disparity in education levels is pronounced between doctors and nurses in this study, where doctors predominantly hold advanced degrees. This educational asymmetry might also contribute to the varying experience of microaggressions among healthcare professionals. The extant literature on the probability of experiencing microaggressions among medical staff of different titles is not consistent. The present study on the phenomenon of experiencing microaggressions among medical staff of different titles is consistent with two Iranian and American studies [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e51\u003c/span\u003e, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e52\u003c/span\u003e] reporting that junior residents are more likely to experience microaggressions than senior residents or attending surgeons. This finding contrasts with the results of another study [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. It is regrettable that this paper did not differentiate between the differences in the incidence of microaggressions experienced by medical staff of different specialties. Instead, it simply differentiated between medical, surgical and ancillary departments, with medical staff of internal medicine experiencing higher rates of microaggressions than medical staff of other departments. Recent study found in surgery and surgical subspecialties experienced a higher rate (82.3%) than medical (30.1%) and dentistry (12.9%) [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e53\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFurthermore, the linguistic dimension emerges as a factor influencing microaggressions. Mandarin-dominant healthcare workers, owing to the prominence of Mandarin as one of the working languages of the United Nations and its widespread proficiency among over 80% of the Chinese population [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e54\u003c/span\u003e], encountered fewer instances of microaggressions. The historical context of Deyang, with its role in the Third-Front Movement, introduced a mix of migrants from various regions, many of whom lacked familiarity with local dialects. This linguistic diversity has resulted in communication challenges within clinical settings, potentially contributing to the observed pattern of microaggressions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e4.4 Factors influencing Microaggressions.\u003c/h2\u003e \u003cp\u003eThis study discovered that doctors employed in auxiliary departments face an elevated risk of experiencing microaggressions when compared to their counterparts in internal departments. Doctors who consistently demonstrate greater patience and meticulous attention to detail in their work, as well as those perceived as \u0026ldquo;amicable, helpful, and supportive\u0026rdquo; tend to achieve elevated levels of patient satisfaction. However, challenges within auxiliary departments contribute to conflicts and the manifestation of microaggressions. Firstly, these departments often grapple with a high patient volume and rapid patient turnover. Secondly, their primary focus is on providing supplementary examinations, resulting in constrained time for doctors to allocate to each patient, furthermore, doctors in auxiliary departments may encounter difficulties in deciphering the intricate relationship between test results and clinical symptoms. Thirdly, there are instances where patients cannot undergo immediate examinations and need to schedule appointments. Consequently, waiting times range from a few days to several months, prolonging the anticipation period. These extended waiting periods further exacerbate conflicts between patients and doctors in auxiliary departments, leading to the emergence of microaggressions.\u003c/p\u003e \u003cp\u003eThe study revealed that older age(P\u003csub\u003e36\u0026thinsp;\u0026minus;\u0026thinsp;60\u003c/sub\u003e=0.037) was a risk factor for nursing staff when it came to experiencing microaggressions. This may, in part, be attributed to the low educational attainment of the older nurses, as evidenced by a prior study where only 14.6% of them had an undergraduate degree or higher [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. Nurses who aged 36\u0026ndash;60 years typically boasts a substantial work experience of at least 14 years of work experience, based on the traditional Chinese education (6 years of elementary school, 3 years of junior school, 3 years of junior/senior high school, and 4 years of undergraduate degree). These nurses often contend with burnout, a potential dearth of patience in their clinical responsibilities and staffing shortages[\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. This situation affects the quality of care provided to patients by diluting services, which increases the likelihood of patients experiencing microaggressions.\u003c/p\u003e \u003cp\u003eIn contrast, the present study demonstrated that nurses residing in big families, predominantly in the context of three-generation families where the division of labor is clear and elder members responsible for familial and child-related affairs, benefit from a protective effect against encountering microaggressions (P\u003csub\u003ebig family\u003c/sub\u003e = 0.012). This living arrangement empowers nurses to strike a harmonious balance between their professional duties and familial responsibilities, subsequently fostering increased levels of patience and meticulousness in their clinical endeavors. This engenders an atmosphere of friendliness and support among healthcare staff when interacting with patients, effectively mitigating the likelihood of microaggressions transpiring Family members represent a cornerstone of emotional support for nurses, and those who residing within big families are endowed with an augmented degree of such support[\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e57\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eDespite an often-covert nature, the detrimental effects of microaggressions are tangible and far reaching, but the study found healthcare workers have faced a significant prevalence of microaggressions, with nurses being particularly affected. The experiences of microaggressions among healthcare workers have exhibited a robust association with anxiety and depression symptoms, underscoring the adverse impact of microaggressions on mental health. Physicians and nurses in different departments reported varying experiences with microaggressions. Additionally, among nurses, those elder and living in a nuclear family (as opposed to big family) were found to be at elevated risk.\u003c/p\u003e \u003cp\u003eThese findings emphasize the paramount significance of addressing and minimizing the occurrence of microaggressions in healthcare settings, particularly their implications for both healthcare professionals and patients. Implementing comprehensive policies and proactive measures aimed at addressing and minimizing the occurrence of microaggressions is crucial to mitigate their negative impact on the relationship between patients and healthcare providers and foster a healthy work environment.\u003c/p\u003e \u003cp\u003e \u003cb\u003eLIMITATIONS\u003c/b\u003e \u003c/p\u003e \u003cp\u003eIn healthcare setting, previous studies have focused on microaggressions experienced by patients, in this study is the first to address microaggressions experienced by healthcare workers. However, the shortcoming is that the sample in this study was confined to a single hospital and a specific geographic area. Also, the absence of before-and-after comparisons limits our ability to determine the causality between microaggressions and the symptoms of anxiety and depression. To enhance the robustness of findings, future studies should consider widening the sample size and encompassing medical professionals from diverse regions. This approach will facilitate a more comprehensive exploration of the factors influencing microaggressions.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eData availability statement\u003c/p\u003e\n\u003cp\u003eThe raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.\u003c/p\u003e\n\u003cp\u003eEthics statement\u003c/p\u003e\n\u003cp\u003eThis study adhered to the Declaration of Helsinki was approved by the Ethics Committee of Deyang People\u0026apos;s Hospital(2023-04-007-K01), the participants provided their written informed consent to participate in this study.\u003c/p\u003e\n\u003cp\u003eAuthor contributions\u003c/p\u003e\n\u003cp\u003eDatas collections were conducted by TL, LLL, MZ, and SJ. Analysis and interpretation of data were done by TL, WJY. Drafting of the paper was performed by TL, WJY and LLL. Statistical analysis was carried out by TL, WJY and LLL. Critical revision of the paper was executed by YSD. All authors contributed to the article and approved the submitted version.\u003c/p\u003e\n\u003cp\u003eConflict of interest\u003c/p\u003e\n\u003cp\u003eThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential confict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding: \u003c/strong\u003eScience and Technology Plan Project of Deyang City: 2022SCZ131\u003c/p\u003e\n\u003cp\u003eThe funders had no role in the design or decision to publish the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003ePierce CM. Black psychiatry one year after Miami. 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JAMA Intern Med. 2016; 176:1294\u0026ndash;1304.\u003c/li\u003e\n\u003cli\u003eO\u0026apos;Malley CB, Levy A, Chase A, Prasad S, Cases on Diversity, Equity, and Inclusion for the Health Professions Educator, 2023.\u003c/li\u003e\n\u003cli\u003eMontcrieff C. \u0026quot;You could have been a great doctor:\u0026quot; Microaggressions toward nurse practitioners. J Am Assoc Nurse Pract. 2023, 35 (7),402-403. doi: 10.1097/jxx.0000000000000863 \u003c/li\u003e\n\u003cli\u003eSadrabad AZ, Bidarizerehpoosh F, Farahmand Rad R, Kariman H, Hatamabadi H, Alimohammadi H. Residents\u0026rsquo; experiences of abuse and harassment in emergency departments. J Interpers Violence. (2019) 34(3):642\u0026ndash;52. 10.1177/0886260516645575.\u003c/li\u003e\n\u003cli\u003eBarnes KL, Dunivan G, Sussman AL, McGuire L, McKee R. Behind the mask: an exploratory assessment of female surgeons\u0026rsquo; experiences of gender bias. Acad Med. (2020) 95(10):1529\u0026ndash;38. 10.1097/ACM.0000000000003569\u003c/li\u003e\n\u003cli\u003eMicroaggressions: Prevalence and Perspectives of Residents and Fellows in Post-Graduate Medical Education in Kuwait 2022 Jun 15:9:907544. doi: 10.3389/fsurg.2022.907544. eCollection 2022.\u003c/li\u003e\n\u003cli\u003eImplementation Plan for Universalizing the State Common Language and Written Language Project: Circular by the Ministry of Education and State Language Commission. 2017\u003c/li\u003e\n\u003cli\u003eSu BB, Du J, Jia JZ, Wang YY, Jing ZW, Zhang C, Wang ZF. Study on the Current Situation and Allocation Equity of China\u0026apos;s Nursing Human Resources. Chinese Jouranl of Health Policy. 2018, 11 (12),56-61. doi: 10.3969/j.issn.1674-2982.2018.12.010 \u003c/li\u003e\n\u003cli\u003eOrganization WH, World health statistics 2015, 2018.\u003c/li\u003e\n\u003cli\u003eKelly EL, Fenwick KM, Brekke JS, Novaco RW. Sources of Social Support After Patient Assault as Related to Staff Well-Being. J Interpers Violence. 2021, 36 (1-2),Np1003-np1028. doi: 10.1177/0886260517738779 \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"discrimination, health care, influencing factors, microaggression, pilot study","lastPublishedDoi":"10.21203/rs.3.rs-4919288/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4919288/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eIntroduction\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\"Microaggression\", encapsulates the notion that subtle and commonplace instances of discrimination and bias, can result in psychological and emotional distress, further entrenching inequality and cultivating a hostile social atmosphere for marginalized individuals or collectives. Studies endeavors to shed light on illuminating the impact of microaggressions on healthcare workers have found that they have consistently underscored their pervasive detrimental effects. This study aims to investigate the current status of microaggression encounters among healthcare workers, alongside an examination of the contributing risk factors associated with the occurrence of such microaggressions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 190 aged 18–60 years clinical healthcare practitioners were recruited from March to April 2023. Questionnaires including the Everyday Discrimination Scale-9 items (EDS).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 83 nurses [82(98.8%) female] and 107 doctors [54(50.5%) female] participated. Among the participants, 40(37.4%) doctors and 50(60.2%) nurses reported encountering microaggressions. Notably, the prevalence of microaggressions among nurses was significantly higher than that among doctors (P = 0.002). Binary logistic regression analysis provided insights into the independent factors influencing the experience of microaggressions. For doctors, the department emerged as a significant influencer (reference level = internal; OR\u003csub\u003eauxiliary\u003c/sub\u003e=6.138, P\u003csub\u003eauxiliary\u003c/sub\u003e=0.016), for nurses, age (reference level = 18 ~ 35y; OR\u003csub\u003e36 ~ 60\u003c/sub\u003e=3.497, P\u003csub\u003e36 ~ 60\u003c/sub\u003e=0.037), department (reference level = internal; OR\u003csub\u003eauxiliary\u003c/sub\u003e=0.072, P\u003csub\u003eauxiliary\u003c/sub\u003e=0.007), and family structure (reference level = nuclear family; OR\u003csub\u003ebig family\u003c/sub\u003e=0.242, P\u003csub\u003ebig family\u003c/sub\u003e=0.012) demonstrated significant influence of experience of microaggressions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHealthcare professionals have encountered a significant prevalence of microaggressions, with a distinct impact observed among nurses. The encounters with microaggressions within the healthcare workforce have exhibited a robust connection with symptoms of anxiety and depression. Specifically, doctors employed in auxiliary departments have been identified as being at a heightened risk of encountering microaggressions in comparison to their peers in internal medicine. Conversely, nurses stationed in auxiliary departments face an elevated risk in contrast to their counterparts in internal medicine. Moreover, among nurses, an advanced age and living in a nuclear family (as opposed to big family) have been identified as factors contributing to an increased vulnerability to microaggressions.\u003c/p\u003e","manuscriptTitle":"Microaggressions in Medicine: A Pilot Study on Differences and Determinants Among Doctors and Nurses","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-10-14 09:06:44","doi":"10.21203/rs.3.rs-4919288/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"76c5061e-8200-46b8-858f-ed7484a3c8fb","owner":[],"postedDate":"October 14th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-10-21T18:08:43+00:00","versionOfRecord":[],"versionCreatedAt":"2024-10-14 09:06:44","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4919288","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4919288","identity":"rs-4919288","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}