Background
Discrimination against persons based on characteristics linked to their ethnicity or migration status is prevalent worldwide [
1]. In Germany, too, representative longitudinal studies of the population documented a general polarization of society and the emergence of extreme right-wing and nationalist tendencies [
2]. We understand discrimination to entail people being disadvantaged and belittled in relation to ascribed features, such as, e.g., gender, social background, age, disability and sexual orientation, as well as religion, language and ethnic origin [
3,
4]. Discrimination mechanisms are anchored on the individual and institutional levels and in society as a whole [
4]. Numerous systematic reviews documented the negative effects on the mental and physical health of those affected, such as increased prevalence of depression, anxiety disorders and excess weight [
5,
6].
The health system mirrors the state of society and, therefore, also reflects existing discrimination dynamics that may impact both patients and staff. Relevant studies in the hospital environment previously examined different professions and clinical specialties [
7,
8] and certain forms of discrimination, e.g., regarding the gender of affected persons [
9‐
11]. Studies focusing on systematic discrimination against healthcare staff based on features associated with their ethnicity, such as, e.g., nationality, language or religion, showed that in countries of the Global North, e.g., the USA, the UK and Canada, racial discrimination in their profession is an everyday experience for nurses [
12,
13] and physicians [
14‐
16] and that such discrimination has a continuous history. Forms of discrimination included disadvantageous treatment on both interpersonal and institutional levels, e.g., more difficult career progression [
17‐
19], more frequent disciplinary procedures [
20], more unpaid overtime and less participation in work planning [
21] and most recently in relation to the COVID-19 (coronavirus disease 2019) pandemic, indications of higher rates of mortality and exposure to infection [
22]. Studies pointed to a wide spectrum of persons, e.g., patients, colleagues and superiors, as the source of reported discrimination [
23‐
25]. A clustering of discrimination experiences linked to precarious forms of employment such as part-time work [
10,
21] and the educational level of those affected [
7,
26] was further observed. In addition to well-known effects of discrimination on individual health and well-being, additional professional and institution-related impacts of such experiences in the workplace were widespread. Discrimination and threats at the workplace were associated with lower job satisfaction [
13], poorer mental and physical health [
7,
27], higher stress levels [
8], more days lost through sickness and more frequent mental and physical withdrawal [
23,
28,
29]. Persons affected by discrimination reported diminished self-esteem and reduced productivity [
30,
31]. Studies also showed that in terms of institutional outcomes, discrimination was related to staff fluctuation of both nurses and physicians [
13,
32]. Direct and indirect effects of discrimination in clinical workplaces were further associated with negative impacts on the quality of patient care and higher costs in the healthcare system [
8,
28,
31].
Studies on discrimination of healthcare staff demonstrated the importance of taking into account the interplay and mutual reinforcement of discrimination mechanisms based on characteristics of the affected persons, such as social background, gender, professional status, age and ethnicity or experience of migration [
16,
30,
33,
34]. This so-called intersectional approach is highly relevant in analysing discrimination both on the micro level and in institutional structures, e.g., in a hospital, where hierarchical structures and a high proportion of women (particularly in nursing) prevail [
8,
17,
33]. Racism and discrimination against persons due to their nationality, ethnicity or migration status followed specific historical lines of development on the national level [
35]. It is, therefore, important to take into account the particular national situation and its distinctive features from other contexts. In Germany, the early loss of its colonies after the first World War, the extermination of ethnic minorities during the Nazi regime, and a high influx of migrant workers and their families from Turkey, the former Yugoslavia and Southern European countries such as Greece and Portugal in the 1950´s to 1970´s have led to a different formation of minorities in comparison with, e.g., the USA [
36]. Despite some qualitative studies in German-speaking countries, in comparison with the state of international research there are large gaps in quantitative research in Germany on discrimination experienced by hospital healthcare staff. Large-scale studies in Germany are thus required relating to the two numerically largest groups of staff in the healthcare field.
Accordingly, an online survey of discrimination experiences addressing hospital nurses and physicians was carried out as part of a larger multicenter mixed-methods research project. Our study was the first to focus on (a) the description of healthcare staff’s observed or personally experienced discrimination in the workplace and of identified perpetrators and ascribed reasons, and (b) the examination of interpersonal and institutional factors associated with these discrimination experiences of healthcare staff.
Methods
Data collection
Between May and November 2018, healthcare staff in 22 hospitals run by two organizations participated in a standardized online survey. The study received the approval of the relevant ethics committee. All staff members who were active as nurses or physicians in the hospitals run by the two organizations at the time of the survey were eligible to participate. The invitation to participate in the survey was sent by email via a personalized link to the work address of all physicians and nurses, and where no email account was available, by a QR (quick response) code sent by mail with the pay slip. Unipark software was used to administer the survey. The time required to answer the questionnaire was approximately 15 min. The aim was to complete a full census of both organizations with approximately 3700 physicians and 9800 nurses. No additional demographic information was available on the total population for non-responder analysis. To increase the response rate, we sent reminders and an additional attempt with written surveys was carried out among the physicians in one of the organizations.
Measures
The two dependent variables, observed and personally experienced discrimination, were formulated on the basis of the questionnaire "Discrimination experiences of migrants in Germany" developed in the context of a survey by the integration agencies of the intercultural migrant center IMAZ e.V. (Interkulturelles Migrantenzentrum e.V.) and the German Red Cross regional chapter in Düsseldorf. The survey addresses with two separate questions whether the respondent had ever been a witness to or victim of discrimination at their own wards. If the answer was yes, based on the dependent variable, further questions followed regarding information on the person being discriminated against, possible reasons for the discrimination, and the perpetrator of the observed or personally experienced discrimination. A set of possible answer categories was provided for each item, supplemented by the category “Other” with a free text field. Multiple entries from the available categories were possible. In addition, two items examined institutional responses to the discrimination, i.e., discussion of the incident or implementation of measures derived from it in the department. Further questions covered additional variables, i.e., sociodemographic characteristics of the participants, such as age, gender, and migration background. Respondents were allocated to one of three categories in relation to their migration background: no migration background, migration background on one side, or migration background on both sides, based on the country of their parents’ birth. In addition, data on employment, i.e., professional group, type of employment and working hours, were included. Institutional characteristics reflected the estimated percentage of staff and patients with a migration background in the respondents’ departments as well as the affiliation to one of the two surveyed organizations (anonymized in organisation A and B due to data protection requirements). As a relevant influencing factor in relation to discrimination, respondents’ cultural competence was also examined using a translated version of the validated Short Form Cultural Intelligence Scale [
37]. The translated items of variables used in this study are available in Additional file
1.
Statistical analyses
Variables are described with frequency distributions or with mean values and standard deviations according to the scales of measurement. In bivariate analyses, Pearson Chi-square tests and Student’s t tests were conducted to examine differences in the distribution of categorical and metrical variables, respectively, in respondents with and without any discrimination experiences. In multivariable analyses, logistic regressions were calculated in which, in two separate models, potential influencing variables were placed block- and stepwise, in relation to the two dependent variables, i.e., observed and personally experienced discrimination. In the first regression step in each model, the effect of sociodemographic and employment factors on the outcome was examined. In the second step, cultural competence and institutional features were added for a joint model. To examine potentially linked effects of sociodemographic and employment factors, an interaction term was created from the variables professional group and gender; this term was included in the regression models in addition to the relevant individual variables. In sensitivity analyses, regression models for both dependent variables were repeated with an imputed data set which was created using the MULTIPLE IMPUTATION procedure in SPSS. Range restrictions were carried out for four variables in the imputation process, including age (min = 18, max = 100), proportion of staff or patients with a migration background (min = 0, max = 100) and cultural competence (min = 1, max = 5). The statistical evaluation was carried out using version 25 of IBM SPSS Statistics.
Results
The final sample comprised
N = 800 staff members of the participating institutions (response rate: 5.9%). Respondents’ characteristics are shown in Table
1. Altogether, 305 respondents (38.1%) stated that they had witnessed and 108 respondents (13.5%) stated that they had personally experienced discrimination in their department. For observed events, affected persons were mostly colleagues (
n = 257), and patients (
n = 211; see Table
2). Stated reasons were the ethnicity of the discriminated person (
n = 196), their appearance (
n = 175) or language (
n = 145). Meanwhile, of those participants who had experienced discrimination themselves, the majority reported gender (
n = 62) as the reason for the event, followed by ethnicity (
n = 28) and appearance (
n = 27; see Table
2). Witnesses to and victims of discrimination both reported that the discriminating actors most frequently were patients (81.3% and 67.6%, respectively), although colleagues (55.4% and 36.1%) and superiors (20.3% and 33.3%) were also named. Out of all cases of observed or personally experienced discrimination (
n = 320), 39.1% of respondents (
n = 125) stated that antidiscrimination measures were subsequently discussed in their department. However, only 27.8% of respondents (
n = 89) stated that relevant measures were implemented.
Table 1
Description of respondents (all and categorized by status of discrimination experiences) and institutional characteristics
Institution | | | | |
Organization A | 271 (33.9%) | 113 (41.7%) | 51 (18.8%) | 151 (55.7%) |
Organization B | 529 (66.1%) | 192 (36.3%) | 57 (10.8%)* | 329 (62.2%) |
Gender | | | | |
Male | 241 (31.0%) | 94 (39.0%) | 35 (14.5%) | 141 (58.5%) |
Female | 536 (69.0%) | 201 (37.5%) | 69 (12.9%) | 326 (60.8%) |
Professional group |
Physicians | 243 (30.4%) | 101 (41.6%) | 45 (18.5%) | 137 (56.4%) |
Nursing staff | 557 (69.6%) | 204 (36.6%) | 63 (11.3%)* | 343 (61.6%) |
Migration background |
No migration background | 621 (78.2%) | 235 (37.8%) | 78 (12.6%) | 375 (60.4%) |
Migration background on one side | 56 (7.1%) | 24 (42.9%) | 11 (19.6%) | 30 (53.6%) |
Migration background on both sides | 117 (14.7%) | 45 (38.5%) | 19 (16.2%) | 70 (59.8%) |
Employment contract |
Fixed-term | 207 (26.4%) | 93 (44.9%) | 34 (16.4%) | 111 (53.6%) |
Permanent | 578 (73.6%) | 207 (35.8%)* | 74 (12.8%) | 359 (62.1%)* |
Work time model |
Full time | 492 (62.2%) | 192 (39.0%) | 69 (14.0%) | 290 (58.9%) |
Part time | 299 (37.8%) | 110 (36.8%) | 38 (12.7%) | 184 (61.5%) |
Age (in years) | 41.3 (11.1) | 40.3 (10.4)* | 39.4 (10.2) | 41.9 (11.4)* |
Cultural competence | 3.5 (0.6) | 3.6 (0.6)* | 3.8 (0.5)* | 3.4 (0.6)* |
Estimated proportion of staff with migration background in respondent’s own department (in %) | 22.1 (17.5) | 22.5 (16.8) | 22.4 (16.9) | 21.8 (17.8) |
Estimated proportion of patients with migration background in respondent’s own department (in %) | 30.5 (20.5) | 32.2 (20.3) | 33.0 (22.2) | 29.1 (20.4)* |
Table 2
Frequency of observed and personally experienced discrimination and characteristics associated with the discrimination event
Target of discrimination |
Colleague | 257 (84.3) | – |
Patient | 211 (69.2) | – |
Superior | 21 (6.9) | – |
Other | 5 (1.6) | – |
Reason for discrimination |
Ethnicity | 196 (64.3) | 28 (25.9) |
Appearance | 175 (57.4) | 27 (25.0) |
Language | 145 (47.5) | 11 (10.2) |
Religion | 125 (41.0) | 15 (13.9) |
Gender | 109 (35.7) | 62 (57.4) |
Age | 35 (11.5) | 24 (22.2) |
Other | 23 (7.5) | 20 (18.5) |
Source of discrimination |
Patient | 248 (81.3) | 73 (67.6) |
Colleague | 169 (55.4) | 39 (36.1) |
Superior | 62 (20.3) | 36 (33.3) |
Other | 16 (5.2) | 3 (2.8) |
Bivariate analyses revealed that witnesses in comparison with non-witnesses of discrimination were statistically more likely to be fixed-term employees, to be younger, and to have higher cultural competence. Victims of discrimination in comparison with non-victims were more often employed in organization A, were more often physicians, and had higher cultural competence. Those participants who reported no discrimination experiences at their wards were statistically more likely to be permanent employees, to be older, to have lower cultural competence and to work in wards with fewer patients with migration background than participants who reported any discrimination experience (see Table
1).
The findings of the multivariable stepwise logistic regression analyses showed that the relative probability of observing a discrimination event was significantly associated only with respondents’ cultural competence (see Table
3). According to this, respondents with greater cultural competence showed a higher relative probability of observing discrimination in their department (
β = 1.583;
p = 0.002). All other sociodemographic, employment and institutional features were not significantly linked to this dependent variable. The statistical model was significant (
χ2(12) = 23.11;
p = 0.027;
N = 734). Concerning features associated with the dependent variable of personally experienced discrimination, three variables retained stable effects throughout the stepwise structure (see Table
3): first, the relative probability of experiencing discrimination in the respondent’s own department was significantly lower in one of the two organizations (
β = 0.575;
p = 0.037). In addition, the interaction of the effects of gender and professional group was statistically significant (
β = 0.280;
p = 0.010), so that male nurses and female physicians had a higher probability of experiencing discrimination than female nurses and male physicians. Greater cultural competence of the respondents also increased the relative probability of reporting personal experience of discrimination (
β = 2.838;
p = < 0.001). The statistical model for the second outcome was also significant (
χ2(12) = 51.32;
p < 0.001;
N = 734).
Table 3
Results of stepwise logistic regressions for the dependent variables ‘witness of discrimination’ and ‘victim of discrimination’
Gender: Female | 1.233 | .709; 2.143 | .458 | 1.128 | .645; 1.975 | .673 | 2.009 | .947; 4.262 | .069 | 1.702 | .786; 3.683 | .177 |
Age | .991 | .976; 1.006 | .219 | .994 | .979; 1.009 | .431 | .979 | .957; 1.001 | .060 | .983 | .961; 1.006 | .148 |
Migration background: on one side | 1.245 | .682; 2.271 | .475 | 1.168 | .636; 2.144 | .617 | 1.387 | .640; 3.006 | .407 | 1.275 | .577; 2.816 | .548 |
Migration background: on both sides | .900 | .584; 1.388 | .635 | .781 | .497; 1.227 | .283 | 1.185 | .657; 2.136 | .573 | .897 | .486; 1.655 | .727 |
Institution: organization B | .820 | .578; 1.162 | .265 | .916 | .633; 1.327 | .644 | .532 | .328; .861 | .010 | .575 | .343; .967 | .037 |
Professional group: nurses | 1.360 | .761; 2.429 | .299 | 1.275 | .709; 2.290 | .417 | 1.532 | .671; 3.496 | .311 | 1.346 | .582; 3.110 | .487 |
Interaction: professional group*gender | .603 | .304; 1.193 | .146 | .644 | .322; 1.287 | .213 | .236 | .092; .602 | .003 | .280 | .107; .734 | .010 |
Employment contract: permanent | .791 | .519; 1.204 | .274 | .804 | .526; 1.230 | .315 | 1.534 | .826; 2.852 | .176 | 1.640 | .874; 3.076 | .123 |
Work time model: part time | 1.084 | .778; 1.511 | .632 | 1.089 | .779; 1.523 | .619 | 1.252 | .782; 2.005 | .349 | 1.242 | .767; 2.012 | .379 |
Proportion of staff with MB in department | – | – | – | .997 | .987; 1.007 | .556 | – | – | – | 1.000 | .986; 1.014 | .962 |
Proportion of patients with MB in department | – | – | – | 1.006 | .997; 1.015 | .183 | – | – | – | 1.001 | .989; 1.014 | .851 |
Cultural competence | – | – | – | 1.583 | 1.184; 2.118 | .002 | – | – | – | 2.838 | 1.825; 4.413 | < .001 |
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