Portrait of Filipino healthcare workers' discrimination experiences during the early part of the COVID-19 pandemic.

BACKGROUND: At the core of a global health crisis, healthcare workers are tasked to perform crucial and life-threatening roles. Despite the heavy-laden responsibilities amid COVID-19 pandemic, these workers are subjected to various forms of stigma and discrimination.
OBJECTIVES: The primary intent of this paper is to investigate the existence of discrimination among healthcare workers during COVID-19 pandemic in the Philippines. Further, it aims to test the following hypotheses: (1) Discrimination experiences among Filipino healthcare workers are dependent on certain demographic characteristics; and (2) Discrimination experiences vary significantly according to the type of healthcare workers.
METHODS: This exploratory study used a two-part survey questionnaire consisting of the baseline data of the respondents and an 8-point Likert-type scale to identify the different forms of discrimination experienced by Filipino healthcare workers. Data yielded by the instruments were descriptively (frequency, mean and percentage) and inferentially (Pearson R, Kendall tau, t-test, and One-Way Analysis of Variance) treated.
RESULTS: Among the Filipino healthcare workers, the Radiologic Technologists experienced the most forms of discriminatory acts, followed by Nurses and Medical Technologists. Those who work in high-risk duty assignments experienced the most discriminatory incidents such as insulting gestures and physical/social loathing, social media bashing and offensive jokes.
CONCLUSION: The discrimination experienced by Filipino healthcare workers is a valuable platform for health policy interventions at the local and global levels to safeguard the physical, social and psychological well-being of healthcare workers, especially in battling the COVID-19 pandemic.

Introduction

Healthcare workers have emerged as vulnerable populations during the COVID-19 pandemic, [1] as they protect their patients, communities, and themselves from the coronavirus [2]. Though healthcare workers remain steadfast and committed to their sworn duty, [3] vulnerability to diseases and rumors, and incorrect information still increase their anxiety levels [4]. With such circumstances, medical professionals are highly susceptible to physical and psychological vulnerabilities, [5] such as discrimination. Like other psychosocial stressors, discrimination is adversely related to a broad range of mental health outcomes, [6],[7] which can affect psychological well-being, leading to symptoms of distress [8].

Historically, healthcare workers' discrimination and mental health concerns are no different from other health crises in the past. This includes neglecting behavior and rejection [9]; and social and emotional impact [10] during the 2012 MERS-CoV and 2014 Ebola epidemic, respectively. During the early part of the COVID-19 pandemic, numerous reports of ill-treatment of individuals from the local and global settings were documented. Beddoes [11] cited the healthcare workers’ experience of getting punched in the face on a Chicago bus and a healthcare worker was doused with bleach by five men in the Philippines [12].

Further, since the outbreak of the pandemic, both cyber and physical attack among healthcare workers and their families were reported across the world [13]. The discrimination experiences of healthcare workers during the early part of the pandemic have been documented in countries like Colombia, [14] Nepal, [15] Indonesia, [16] Egypt [17]. In the Philippines, medical professionals have experienced being evicted from homes, refused rides on buses, and kicked out of restaurants by their fellow citizens due to fear of contracting the virus [18]. These medical professionals were battling mental and emotional pains caused by work-life disruptions and social stigma attached to their profession and roles in this pandemic.

Hence, the primary intent of this paper is to investigate the existence of discrimination among healthcare workers during COVID-19 pandemic in the Philippines. Further, it aims to test the following hypotheses: (1) Discrimination experiences among Filipino healthcare workers are dependent on certain demographic characteristics; and (2) Discrimination experiences vary significantly according to the type of healthcare workers.

Theoretical background

Theoretical framing

This study draws on the Gelberg-Andersen Behavioral Model for Vulnerable Populations. As an expanded version of Andersen's Behavioral Model of Health Service Use in 1968 [19]. This model is widely used for explaining health care utilization patterns by the general population and suggests that the use of health services is a function of predisposition to use services, factors that enable or impede use, and need for care, thus providing a way to conceptualize variations in utilization. The structural model assessed the impact of predisposing, enabling, and need variables on predicting the use of health services by several vulnerable populations. Demographic profile is usually pre-eminent predisposing variable for most normative populations. Education and age, for instance, are relatively important in leading to the conclusion on healthcare services utilization. Enabling variables include the source of care and barriers to health services utilization. This implies that the more obstacles experienced by an individual indicate lesser utilization of healthcare services. Moreover, the need variables include illness. This suggests that when a person is ill, he does seek medical services, regardless of his economic status.

To the best of our knowledge, no study has empirically extended the model on discrimination experiences of healthcare workers. With the emergence of healthcare workers as a vulnerable group during the pandemic, [20] the model serves as a valuable lens to better understand the dynamics of discrimination as a system and how it is shaped by predisposing, enabling, and need variables. The predisposing factors include the healthcare workers’ demographic characteristics. The enabling factors encompass the healthcare workers’ frequency in reporting for work, means of transportation in reporting for work, place of stay during COVID-19, duty assignment, type of social media account use, and sources of information during COVID-19. Further, the perceived “need” factor of the health care workers refers to the respect and support from people in the community.

Methods

Research design

The study employed the descriptive exploratory design in order to surface the diversity on the presence of discrimination among healthcare workers. According to Brink and Wood, [21] the data collected in this type of design either contribute to the development of theory or explain phenomena from the perspective of the persons being studied.

Subjects and study site

To achieve the purpose of the study, healthcare workers from the three main geographical regions of the Philippines participated in a Google form survey questionnaire. To be included in this study, prospective health and allied professionals must be (1) currently employed in a hospital; and (2) 20 years old and above.

Corpus of data

This exploratory study employed a two-part survey questionnaire. The first part sought for the respondents’ profile (age, gender, civil status, number of children, religion, place of work and residence). It further supplied the healthcare workers' occupational profile, such as profession, type of hospital, length of service, work schedule, duty assignment, and means of transportation. The second part was an 8-point Likert-type scale ranging from “to a little extent” (1-point) to “to a great extent” (8-point). This 30-item, researcher-made scale (Cronbach reliability coefficient = 0.97) identified the forms of discrimination experienced by Filipino healthcare workers in the country. The forms of discrimination may come from the community (both face to face and virtual) and not from the patients in the hospitals. This instrument was pilot tested to a select group of health and allied professionals (n = 20) to ensure its reliability and validity (Cronbach alpha 94.6%) who were later on included in the actual pool of respondents.

Data collection process and ethical consideration

Ethical considerations such as informed consent form, confidentiality of data, and withholding of personal identifiers were advertently complied with. A two-week data gathering period was observed. Since the data were gathered at the height of the pandemic, snowballing technique was employed to recruit the possible participants who were contacted within their personal capacity. Consent was obtained from each subject through Google form, considering that movement and face-to-face interaction were prohibited at this time.

Statistical analysis

Gathered data were treated descriptively using the mean and standard deviation (SD) to show the extent and diversity of their discrimination experiences, respectively. Inferentially, Pearson r and Kendall tau were used to show relationship between healthcare workers’ discriminatory experiences and their demographic profile for continuous and discrete variables, respectively. Moreover, t-test and one-way analysis of variance were used to surface significant differences in their responses when grouped according to respondents’ profile.

Results

Participants’ characteristics

Of the 516 respondents (female = 59.9%; male = 40.1%). Most of the respondents were millennials, with age range from 28 to 38 years old (71.5%), single (67.8%), and have no child (67.8%). The majority were Catholics (78.9%), working in the government (51%), for five years or less (54.5%), were from NCR (53.3%), and worked as radiologic technologists (35.1%).

Most of them reported in their work daily (42.2%), with their cars (41.3%) in a high-risk COVID/PUI wards (Influenza like illness (ILI) tent, Severe Acute Respiratory Infections tent, Triage area, ER/ER isolation ward, OR, Delivery Room, Diagnostic centers, Morgue, Housekeeping areas, other areas where intubation CPR, NPS/OPS swabbing is done) (54.1%). Majority of the respondents stayed in their residences (70.3%) before the COVID-19 pandemic and stayed in the same as before (81.8%) during COVID-19. Most of them used social media platforms such as; Facebook, Messenger, Viber, and Instagram (66.5%), and their sources of information during COVID-19 mainly were television and social media (70.2%).

Table 1 shows the forms of discrimination experiences rated by the health workers. The top most rated discrimination experiences are: being talked about (x̄ = 3.09; SD = 2.33), hearing offensive jokes (x̄ = 2.59; SD = 2.20), forced quarantine (x̄ = 2.26; SD = 2.01), insulting gestures (x̄ = 2.23; SD = 2.01) and hearing rants (x̄ = 2.20; SD = 1.92). Noticeably, on an 8-point scale, these low mean and high SD values indicate positive skewness and diversity in the responses, respectively, that is, most of the healthcare workers are experiencing discrimination to a lesser extent. Items such as experiencing harassment in group chats (x̄ = 1.31, SD = 0.92), in text (x̄ = 1.21, SD = 0.81), in tweets (x̄ = 1.21, SD = 0.74), being attacked physically (x̄ = 1.21, SD = 0.74) and harassment through calls (x̄ = 1.17, SD = 0.65) constitute the least rated discriminatory experiences.

Table 1

Forms of discrimination experiences rated by health workers in descending order (from top to least).

Discrimination Experiences	Mean	SD	Discrimination Experiences	Mean	SD
1. being talked about	3.09	2.33	16. social loathing	1.57	1.28
2. offensive jokes	2.59	2.20	17. physical loathing	1.54	1.24
3. forced quarantine	2.26	2.01	18. petitioned (ex. for transfer)	1.51	1.31
4. insulting gestures (covering mouth)	2.23	2.01	19. harassment of my family and close friends	1.50	1.26
5. hearing rants	2.20	1.92	20. evicted from the dormitories	1.49	1.28
6. “aloof” treatment	2.14	1.86	21. family and friends are denied of essential service	1.44	1.20
7. shunning away of people	2.00	1.77	22. denied housing	1.41	1.29
8. spreading wrong information about me	1.90	1.59	23. “barricade” my house	1.39	1.16
9. doused on my way to work	1.79	1.69	24. family and friends are denied of health services	1.34	1.01
10. blaming	1.78	1.53	25. cursing	1.32	0.93
11. social media bashing	1.74	1.59	26. being harassed through group chats	1.31	0.92
12. refused rides on buses	1.65	1.46	27. attacked physically	1.21	0.81
13. denial of access to essential services (ex.: haircut, laundry, etc.)	1.63	1.51	28. being harassed through text	1.21	0.74
14. using foul or offensive words	1.60	1.36	29. being harassed through tweets	1.21	0.74
15. creating memes in social media	1.57	1.28	30. being harassed through calls	1.17	0.65

From a pool of thirty (30) discriminatory experiences (Table 2 ), fifteen (15) and twelve (12) situations were found to be negatively correlated to age (values ranging from r = −.204, p < 0.01 to r = −.094, p < 0.05) and length of service (values ranging from r = −.164, p < 0.01 to r = −.086, p < 0.01), respectively. This shows that the more the person ages and has longer working experience in the healthcare service, the less they experience or, the less they bother about these discriminatory situations. In terms of the number of children, only the experience of being harassed through group chats was found to be positively correlated (r = .085, p < 0.05). This means that the more children they have, the more they are affected by the experience of harassment on the on-line platform. Lastly, seven (7) out of the thirty (30) discriminatory experiences were found to be positively correlated to the frequency of reporting, with values ranging from r = .080, p < 0.01 to r = .108, p < 0.05. This goes to show that the more frequent they report to work, the more discriminatory situations are bothersome for the healthcare workers.

Table 2

Correlations of Discriminatory experiences and demographic characteristics (n = 516).

Discriminatory Experiences	Age	Number of children	Length of service	Frequency of reporting
1. evicted from the dormitories	−0.104*	0.015	−0.092*	0.085*
2. petitioned (ex. For transfer)	−0.113*	−0.002	−0.100*	0.091*
3. doused on my way to work	−0.165**	−0.004	−0.137⁎⁎	0.080*
4. refused rides on buses	−0.102*	−0.026	−0.074	0.095*
5. attacked physically	−0.002	0.026	−0.057	−0.015
6. being talked about	−0.201⁎⁎	0.007	−0.161⁎⁎	0.099⁎⁎
7. offensive jokes	−0.204⁎⁎	0.000	−0.164⁎⁎	0.067
8. denial of access to essential services (ex.: haircut, laundry, etc.)	−0.096*	−0.010	−0.072	0.006
9. social media bashing	−0.115⁎⁎	0.005	−0.086*	0.034
10. forced quarantine	−0.049	0.014	−0.046	−0.027
11. “aloof” treatment	−0.039	0.027	−0.032	0.031
12. creating memes in social media	−0.094*	0.021	−0.089*	0.012
13. spreading wrong information about me	−0.073	0.015	−0.056	0.060
14. “barricade” my house	−0.08	−0.018	−0.109*	0.071
15. being harassed through text	−0.028	0.041	−0.031	−0.003
16. being harassed through calls	0.027	0.032	0.017	0.011
17. being harassed through group chats	−0.023	0.085*	−0.017	0.026
18. harassment of my family and close friends	−0.083	0.002	−0.074	0.035
19. blaming	−0.116⁎⁎	0.007	−0.100*	0.039
20. insulting gestures (covering mouth)	−0.137⁎⁎	0.000	−0.116⁎⁎	0.071
21. shunning away of people	−0.092*	0.055	−0.083	0.074
22. hearing rants	−0.184⁎⁎	0.000	−0.157⁎⁎	0.088*
23. family and friends are denied of health services	−0.038	0.069	−0.027	0.065
24. family and friends are denied of essential service	−0.048	0.027	−0.044	.085*
25. social loathing	−.095*	0.032	−.094*	0.036
26. physical loathing	−.089*	0.039	−0.083	0.066
27. using foul or offensive words	−0.049	0.031	−0.036	.097*
28. denied housing	−0.084	0.010	−0.075	.108⁎⁎
29. cursing	−0.072	0.041	−0.073	0.010
30. being harassed through tweets	−0.074	0.000	−0.064	0.051

Correlation is significant at the 0.05 level (2-tailed).

Correlation is significant at the 0.01 level (2-tailed).

Significant differences were noted from the 30 identified discriminatory acts (Table 3 ) when grouped according to selected demographic characteristics. With gender as the criterion, marked difference was evident in “insulting gestures such as covering mouth has a significant difference” (t-value = −2.03), with the female healthcare workers posting a higher mean (x̄ = 2.19). As regards civil status, a significant difference was noted in “denied housing” (F-ratio = 6.015) which is highly felt by healthcare workers who are neither single nor married (x̄ = 2.29). Despite the spread of wrong information about them having the highest mean difference (0.28) between those working in public (x̄ = 1.93; SD = 1.86) and private (x̄ = 1.65; SD = 1.49) hospitals, no significant difference in their experiences were noted (t-value = 1.88, p-value = 0.06) nor in any of the discriminatory acts.

Table 3

Significant differences in Discriminatory experiences when grouped according to some demographic characteristics (n = 516).

Discriminatory Experiences	Gender	Type of hospital	Civil Status	Type of Health Profession	Duty assign	Means of transportation	Place of stay
1. evicted from the dormitories	0.51	0.35	2.24	3.01*	1.72	0.44	5.15*
2. petitioned (ex. for transfer)	1.38	−1.05	0.05	2.75*	0.85	1.00	4.68*
3. doused on my way to work	−0.61	−0.94	0.37	5.06*	1.50	4.63*	1.74
4. refused rides on buses	0.76	−0.80	1.02	2.84*	0.07	5.75*	1.54
5. attacked physically	0.65	0.21	0.78	0.82	0.76	1.39	1.26
6. being talked about	1.55	−0.11	2.16	4.83*	7.52*	1.49	2.38
7. offensive jokes	0.71	−1.02	0.68	7.72*	5.13*	2.60*	2.49*
8. denial of access to essential services (ex.: haircut, laundry, etc.)	−0.21	−0.29	0.81	2.51*	2.92	1.06	3.24*
9. social media bashing	0.32	0.32	2.12	1.38	1.45	2.60*	3.35*
10. forced quarantine	0.15	0.54	0.86	1.11	3.36*	0.30	2.62*
11. “aloof” treatment	−1.89	−0.65	1.30	3.61*	0.82	0.73	1.72
12. creating memes in social media	1.53	−1.33	0.28	0.89	0.27	1.01	3.41*
13. spreading wrong information about me	1.04	1.88	0.98	2.62*	0.49	2.65*	1.39
14. “barricade” my house	0.85	0.59	2.25	0.49	0.70	1.26	2.61*
15. being harassed through text	−0.38	1.53	0.17	1.60	0.21	0.66	1.39
16. being harassed through calls	−0.73	1.37	2.28	1.23	0.29	0.19	0.62
17. being harassed through group chats	−0.76	1.82	2.11	1.84	0.13	1.61	2.49*
18. harassment of my family and close friends	1.06	0.84	0.40	1.17	0.36	1.35	1.44
19. blaming	−0.60	0.15	0.22	1.18	1.79	1.01	2.60*
20. insulting gestures (covering mouth)	−2.03*	−0.43	0.11	3.36*	2.90	1.30	2.49*
21. shunning away of people	−1.64	1.12	0.08	3.29*	2.70	0.68	2.39*
22. hearing rants	−0.15	0.16	2.33	2.37*	2.61	2.97*	2.74*
23. family and friends are denied of health services	−0.36	−0.72	1.39	1.21	0.39	0.77	1.97
24. family and friends are denied of essential service	0.07	0.54	0.25	1.90	0.31	1.61	1.18
25. social loathing	−0.25	−0.37	0.83	1.92	1.33	0.62	2.34
26. physical loathing	−0.38	0.02	0.70	2.18	1.24	0.53	1.16
27. using foul or offensive words	−0.89	1.15	0.01	2.66*	0.98	1.56	3.16*
28. denied housing	−0.68	1.09	6.02*	1.83	1.82	1.47	5.62*
29. cursing	−0.36	−0.07	0.03	0.77	0.34	1.34	1.57
30. being harassed through tweets	0.49	0.28	0.13	0.31	0.22	0.72	2.45*

significant at p<0.05

When compared according to the type of health professions, receiving offensive jokes (F-ratio = 7.72) was strongly experienced by Radiologic Technologists (x̄ = 3.07), followed by doused on the way to work (F-ratio = 5.06) as most experienced by Nurses (x̄ = 2.13) and insulting gestures (F-ratio = 3.36) by Midwives (x̄ = 2.54). Further, for duty assignment, the most discriminated healthcare workers are those assigned to high-risk assignments, with marked differences in being talked about (F-ratio = 7.52, x̄ = 3.45), offensive jokes (F-ratio = 5.13, x̄ = 2.87) and forced quarantine (F-ratio = 3.36, x̄ = 2.44). For means of transportation, health workers who were refused rides on buses (F-ratio = 5.75, x̄ = 2.78) and doused on the way to work (F-ratio = 4.63, x̄ = 2.83) used bicycles while hearing rants (F-ratio = 2.97, x̄ = 2.67), spreading wrong information (F-ratio = 2.65, x̄ = 2.41) are experienced most by healthcare workers using their motorcycles.

Finally, in the place of stay, significant differences were noted among those who are not staying in specially designated healthcare facilities who experienced being denied of housing (F-ratio = 5.62, x̄ = 2.26), evicted from the dormitories (F-ratio = 5.15, x̄ = 2.33), petitioned (F-ratio = 4.68, x̄ = 2.03), while those healthcare workers who experienced being created memes in social media (F-ratio = 3.41, x̄ = 2.13), and social media bashing (F-ratio = 3.35, x̄ = 2.29) are those who are stay-in residents in a hospital dorms.

Discussion

Our first hypothesis, which states that discrimination experiences among Filipino healthcare workers are dependent on certain demographic characteristics was supported. Specifically, results showed a negative correlation between healthcare workers’ age and length of service discriminatory experiences. Notably, as health practitioners age and spend more years in service, they become more resilient to discrimination. Similarly, Gooding et al. [22] found that the older the individuals are, the more resilient they become with respect to problem-solving and emotion regulation as compared to the younger generations. Other studies also suggested that resilient personality may counter the negative effects of ill health [23] and predicts mental health in older adults [24]. Thus, hospital administrators must initiate programs that promote resiliency in the workplace, such as stress management techniques, positive mind framing, and finding meaning and value in life.

Regarding the number of children, only the experience of being harassed through group chats was positively correlated. According to Piquero et al. [25], workers in healthcare are more prone to experience verbal harassment and bullying. Many health care workers, who are victims of verbal abuse online, feel that their complaints will not be taken seriously by hospital administrators because of the platform used. Because of this, medical organizations may adopt improved policies that promote psychologically safe interactions among workplace stakeholders and safeguard the psychological well-being of healthcare workers.

Of the thirty (30) discriminatory experiences, seven (7) were positively correlated to reporting frequency. These discrimination scenarios tend to hit the working parents more vulnerable, as they endure sacrifices at work to be able to provide for the needs of their children. The work-family balance remains critical for employed parents and employers alike [26]. Healthcare workers are forced to stay in safe facilities to prevent the risks to their health and their loved ones, making them obliged to have more frequent and longer hours of hospital duty, changing protocols, and potential medical supply shortages [27]. The unprecedented demand for healthcare services during the COVID-19 pandemic has left family-oriented and overworked health professionals vulnerable to discrimination experiences [28]. The job demands more hours at the healthcare facilities and less time with the family. Although the shortage of medical practitioners at the time of this pandemic is recognized, [29] hospital administrators are encouraged to design a work scheme that enables healthcare workers to have a justifiable and humane number of working hours [30] to minimize the discriminatory experiences.

The second hypothesis, which states that discrimination experiences vary significantly according to type of healthcare workers was supported. Specifically, significant differences were noted when they were grouped according to gender, civil status, type of health profession, duty assignment, means of transportation, and place of stay. The finding that there is a significant difference in the gender criterion is similar to what González-Sanguino et al. [31] redounded about the female gender having more symptomatology of psychological impact. The female healthcare workers in this study experienced more incidents of insulting gestures, such as people covering their mouths when they pass by or in the same room. Experiencing this kind of discrimination, female healthcare workers are more susceptible to stress, anxiety, and depression. In terms of civil status, healthcare workers who are either single parents, separated, or widowed had higher experiences of discriminatory acts, specifically in being denied of housing. This runs counter to Wang et al., [32] where married respondents reported being more affected by distressing events related to COVID-19. Discriminatory acts did not differ among healthcare workers who either worked in public or private hospitals. No marked difference was noted in their discrimination experiences when viewed according to the type of hospital. This is in concordance with the existing literature about work issues in public and private hospitals where healthcare workers experienced discrimination [see [33]].

Alarmingly, the Radiologic Technologists were highly discriminated against in terms of offensive jokes, followed by Nurses and Midwives. The Radiologic technologists are among the largest group of professionals, [34] and their work responsibilities entail stress, [35] which need intervention [36] to avoid being faced with mental and emotional disturbances during this pandemic. Further, healthcare workers with high-risk assignments experience being talked about, receiving offensive jokes and being forced quarantined. This concurs with previous studies [i.e. [37,38]], which looked into healthcare discrimination. Healthcare workers who use their bikes and those who do not stay in designated healthcare facilities were the most discriminated. These people are driven by inherently negative thoughts, [39] which revolve around their fears of being exposed to the virus, being blamed, or displacing their felt helplessness with the present condition.

Conclusion and recommendation

This empirical study yielded a portrait of structural and individual discrimination experienced by Filipino healthcare workers. As COVID-19 frontliners, relevant psychological support programs are needed to promote their well-being. Social media use should be maximized to educate and re-educate the people of the role and contributions of healthcare professionals. The knowledge base on discrimination as a social stressor invites dialogic space where both policymakers and practitioners could enact protective measures and safety nets that could support and ensure the overall well-being of the health workforce. Ultimately, the burdens brought about by any global crisis become bearable if any act of discrimination is addressed promptly and holistically. Such expectation is best facilitated by society's adherence to democratic principles and respect for the dignity of the human person.

Admittedly, there are limitations in the present study. Considering that the data were primarily gathered through an online questionnaire due to face-to-face restrictions in the country, a follow-up study may be conducted through a mixed-method approach. Capturing the narratives of the health professionals may shed light to the nature and the dynamics of discrimination indicators found in the tool. Additionally, this study was limited to testing how discrimination experiences compare and relate to the health professionals’ profile. Hence, model development initiative may be conducted through multi-variate analysis, such as, structural equation modeling, canonical correlation and multinomial regression.