Owona Manga Léon Jules^1*, Eyoum Christian¹, Bita Fouda André Arsène¹, Ndjiengwe François Errero², Djietcheu Tan-Ho Myriam¹, Mor Ndiaye^3
1Faculty of Medicine and Pharmaceutical Sciences, The University of Douala, Douala, Cameroon.
²Laquintinie Hospital, Douala, Cameroon.
³Cheick Anta Diop University, Dakar, Senegal.
DOI: 10.4236/odem.2024.124027   PDF    HTML   XML   89 Downloads   572 Views   Citations

Abstract

Introduction: Psychological distress can affect every worker’s mental health or working ability, and specifically the healthcare workers. It is essential to prevent and treat it in order to anticipate predictable consequences. The aim of this study was to investigate the epidemiological characteristics of psychological distress among the healthcare workers in the city of Douala. Material and Methods: This cross-sectional study was carried out during the 1^st semester of 2023 in two reference hospitals of the city of Douala. All volunteer staff from the targeted hospitals were involved. We collected data using a mixed questionnaire that included the working conditions, as of the socioprofessional and psychological characteristics of the healthcare workers. We measured the level of psychological distress using the Kessler scale. Analysis was performed using the Chi-2 test and multivariate analysis. Threshold value was & = 5%, p < 5%. Results: The participation rate was 86.3%. Women (81.7%) and nurses (77.6%) predominated in the sample. The mean age was 35.38 ± 8.9 years, and individuals in their thirties accounted for 46.79% of the sample. The prevalence of psychological distress was 19.5%. The psychological distress was categorized as mild (48.72%), moderate (18.46%) or severe (32.82%). Risk factors for psychological distress included being of the catholic faith (OR = 3.6, p = 0.04), poor sleep quality (OR = 3.9, p = 0.001) and long working hours (OR = 2.2, p = 0.002). Overtime was the only protective factor identified (OR = 0.6, p = 0.04). Conclusion: Nearly 1 out of 5 healthcare workers suffered from psychological distress exacerbated by poor working and living conditions. There is an urgent need to improve their working conditions to prevent the development of more severe consequences.

Keywords

Psychological Distress, Healthcare Workers, Hospitals, Douala

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1. Introduction

Psychological distress (PD) is a state of emotional suffering in response to stressful situations that can affect physical or psychological health [1]. PD combines signs of depression and in some cases, psychosomatic symptoms such as headaches, poor sleeping quality or severe fatigue [2]-[4]. In the absence of adequate treatment, or in front of persisting triggering factors, complications can arise in the form of dementia, even depression which is the ultimate complication [4]-[6]. In 2004, depression was considered as the 1^st cause of disability in people aged 15 to 59 years in developing countries (77.6%), and the 3^rd cause (98.7%) of disability in all ages worldwide [7]. PD can affect anyone, regardless of individual characteristics, living environment or occupation. In the general population, the overall prevalence varies from 5% to 27% [8]-[10]. In Canada, its prevalence was 21% in the general population [11]. In Spain, it was 65.1% among the non-healthcare workers during the COVID-19 pandemic [12].

PD also affects healthcare workers particularly during major epidemics such as COVID-19, Ebola or SARS [13]. PD affected 10.1% to 29.9% of the healthcare workers, during and after the 1^st wave of COVID-19 in Canada [14]. Its prevalence was 41% among nurses in Jordan [15] and 72. 9% among those working in Saudi Arabia [16]. In Europe, the prevalence of PD was lower among nursing staff in France (12.3%) [17] and Italy (26.5%) [18]. In South Africa, PD was 57.4% among healthcare workers at the start of the COVID-19 pandemic [19].

The risk of suicide is higher in individuals suffering from PD [5]. It must be actively prevented in our context, as the prevalence of depression was high (43.5%) among Cameroonian caregivers during the COVID-19 pandemic [20]. Hence, the aim of this research is to investigate the epidemiological characteristics of psychological distress among healthcare workers (HCW) in the city of Douala in the post-COVID-19 period.

2. Material and Methods

• Type, settings and duration of study.

This cross-sectional study was designed and conducted during the 1^st semester of 2023 to assess PD in Douala based reference hospitals. Laquintinie hospital and the Douala military hospital were randomly selected from a group of 4 reference hospitals. These settings were chosen based on criteria such as accessibility, patients’ attendance, quality of services and accommodation capacity.

• Study population and sampling.

The study population consisted of HCW employed at the study settings. Volunteer HCW with at least 12 months of continuous work assignment were enrolled in the study after being informed of the study objectives and the absence of risks associated to the research. Participants were recruited non randomly, by convenience. The minimal sample size was calculated using Lorentz formula for descriptive studies (n = Z².p.q/&²), with p = 43.50% prevalence of PD among some Cameroonian HCW during the COVID-19 pandemic [20].

• Data Collection.

The study variables were related to specific objectives and included socio-professional data (age, sex, social status, family responsibilities, occupation, seniority), living and working conditions (working hours, salary, overtime, sleep quality) and variables of the Kessler K10 questionnaire. This questionnaire assessed the participant’s psychological state of mind at work, during the month preceding the survey [21]. It is designed as an anonymous, self-administered tool to measure the prevalence of PD in people aged 18 years or older [22]-[24]. The scale consisted of 10 items rated from 1 to 4 according to the Likert scale [25]. The total score ranges from 0 to 40: 10 - 19: likely to be well; 20 - 24: likely to have a mild disorder; 25 - 29: likely to have a moderate disorder; 30 - 40: likely to have a severe disorder.

• Data processing and analysis.

All statistical analysis were calculated using SPSS software 20.0. Qualitative variables were analyzed according to their frequency and valid percentages while quantitative variables were analyzed to calculate the mean, standard deviation, median, minimum, and maximums. The relationship between PD and other characteristics was assessed using Pearson’s Chi-2 test. A multivariate analysis was performed to identify the risk factors of PD. The error threshold was set at & = 5%, significance fixed at 5% and the confidence interval was set at 95%.

• Ethical considerations.

Ethical principles of health research were respected throughout the study. The research was approved by the Ethical Committee of the University of Douala under the number 1737 CEI-UY/05/202319/T.

3. Results

• Socio-professional characteristics.

The participation rate was 86.3%. The sample comprised 389 participants, including 274 from Laquintinie hospital (70.44%) and 115 from the military hospital (29.56%). Their distribution according to socio-professional characteristics is illustrated in Table 1 below. Women (81.7%), nurses (77.6%) and singles (56.8%) predominated in the sample. The average age was 35.38 ± 8.95 years [19 - 60 years], with the modal group consisting of those aged 30 - 39 (46.8%). Regarding the religious faith, they were divided in 4 groups of Catholics (48.1%), Protestants (36.3%), Muslims (12.3%) and Animists. The majority of participants received salaries below 100.000 FCFA (63.2%).

• Living and working conditions.

More than 2/3 of participants had normal working time (67.6%). The majority worked overtime (63.7%) and reported poor sleep quality (54.3%).

• Psychological distress and associated factors.

The overall prevalence of PD was 19.5. PD was classified as mild (48.68%), moderate (18.42%), severe (32.89%) as shown in Figure 1. It’s distribution according to socio-professional characteristics is illustrated in Table 2 below. There were more cases of PD at Laquintinie hospital (n = 61; 80.26%) than at the military hospital (n = 15; 19.74%). PD was associated with age < 30 years (p = 0.015), low salary < 100.000 FCFA (152.44€) (p < 0.0001), long working hours (p < 0.0001), poor sleep quality (p = 0.038), income generating activities (p < 0.021), the number of dependent children (p < 0.024) and the religious faith (p < 0.029) (Table 3). The multivariate analysis shown in Table 4 identified 3 risks factors and a single protective factor for PD in nursing staff. Long working hours [OR = 2.23 (1.33 - 3.72); p = 0.002], doubled (x2) the risk of PS. The risk tripled (x3) in catholic participants [OR = 3.6 (1.04 - 13.0); p = 0.04] and quadrupled (x4) in cases of poor sleep quality [OR = 3.98 (2.36 - 6.71); p = 0.001]. In contrast, overtime lowered the risk of PD in participants [OR = 0.60 (0.36 - 0.99); p = 0.99); p = 0.004].

Table 1. Sociodemographic characteristics.

Variables		(n)	(%)
Sex	Male	71	18.3
	Female	318	81.7
Age	<30 years	102	26.2
	30 - 39 years	182	46.8
	40 - 49 years	75	19.3
	≥50 years	30	7.7
Marital status	Single	221	56.8
	Married	160	41.1
	Widow	8	2.1
Occupation	Nurse	302	77.6
	Assistant-nurse	87	22.4
Religion	Animist	11	2.8
	Protestant	141	36.3
	Catholic	189	48.1
	Muslim	48	12.3

Figure 1. Prevalence of psychological distress.

Table 2. Living and working conditions.

Variables		(n)	(%)
Salary (CFA.F) (n = 329)	≤100,000	208	63.2
	100,000 - 200,000	68	20.7
	>200,000	53	16.1
Working hours	Normal (8 H)	263	67.6
	Extended (>8 H)	126	32.4
Extra-time	Yes	248	63.7
	No	141	36.3
Quality of sleep	Good	178	45.7
	Bad	211	54.3
Travel time home-hospital	<30 mins	104	26.7
	30 - 60 mins	173	44.5
	>60 mins	112	28.8
Income generating activities	Yes	95	24.4
	No	294	75.6
Physical and sporting activities	Yes	211	54.2
	No	178	45.8
Relaxation activities	<2 hours	271	69.7
	>2 hours	118	30.3
Dependent children	0	128	32.9
	1 to 4	209	53.7
	>4	52	13.4

Table 3. Distribution of psychological distress.

Variables		Psychological distress (−)	Psychological distress (+)			p-value
		Light (n, %)	Mild (n, %)	Severe (n, %)
Hospital	Laquintinie	213 (68.1)	29 (78.4)	9 (64.3)	23 (92.0)	0.050
	Military Hospital	100 (31.9)	8 (21.6)	5 (35.7)	2 (8.0)
Sex	Female	258 (82.4)	28 (75.7)	13 (92.9)	19 (76.)	0.436
	Male	55 (17.6)	9 (24.3)	1 (7.1)	6 (24)
Age (years)	<30	81 (25.9)	12 (32.4)	7 (50.0)	2 (8.0)	0.015
	[30 - 39]	148 (47.3)	17 (45.9)	2 (14.3)	15 (60.0)
	[40 - 49]	55 (17.6)	7 (18.9)	5 (35.7)	8 (32.0)
	≥50 ans	29 (9.3)	1 (2.7)	0 (0.0)	0 (0.0)
Religion	Animist	6 (1.9)	2 (5.4)	0 (0.0)	3 (12.0)	0.029
	Protestant	115 (36.7)	12 (32.4)	5 (35.7)	9 (36.0)
	Catholiqc	157 (50.2)	19 (51.4)	7 (50.0)	6 (24.0)
	Muslim	35 (11.2)	4 (10.8)	2 (14.3)	7 (28.0)
Marital status	Single	169 (54)	29 (78.4)	8 (57.1)	15 (60)	0.157
	Married	136 (43.5)	8 (21.6)	6 (42.9)	10 (40)
	Widow	8 (2.6)	0 (0.0)	0 (0.0)	0 (0.0)
Dependent children	0	92 (29.4)	17 (46)	8 (57.1)	11 (44)	0.024
	1 - 4	172 (55)	17 (46)	6 (42.9)	14 (56)
	>4	49 (15.7)	3 (8.1)	0 (0.0)	0 (0.0)
Salary (n = 329)	<100,000	150	30	12	16
	100 à 200,000	57	3	0	1
	>200,000	42	3	0	4
Income generating activities	No	234 (74.8)	26 (70.3)	9 (64.3)	25 (100)	0.021
	Yes	79 (25.2)	11 (29.7)	5 (35.7)	0 (0.0)
Working time	Extended	90 (28.8)	11 (29.7)	6 (42.9)	19 (76)	<0.0001
	Normal	223 (71.3)	26 (70.3)	8 (57.1)	6 (24)
Relaxation activities	<2 hours	217 (69.3)	27 (73.0)	11 (78.6)	16 (64.0)	0.774
	>2 hours	96 (30.7)	10 (27.0)	3 (21.4)	9 (36.0)
Quality of sleep	Good	154 (49.2)	13 (35.1)	3 (21.4)	8 (32.0)	0.038
	Bad	159 (50.8)	24 (64.9)	11 (78.6)	17 (68.0)

Table 4. Multivariate analysis.

Variables		OR (IC)	P-value
Sex	Female	0.79 (0.42 - 1.49)	0.48
	Male
Occupation	Nurse	1.42 (0.82 - 2.53)	02.2
	Assistant nurse
Religious faith	Animist	1	082
	Catholic	3.6 (1.04 - 13.0)	0.04
	Muslim	0.90 (0.50 - 1.59)	0.72
	Protestant	1.6 (0.76 - 3.53)	0.20
Marital status	Single	›106 (‹0.001 - › 106)	0.99
	Married
Extra time	No	0.60 (0.36 - 0.99)	0.04
	Yes
Quality of sleep	Good	3.98 (2.36 - 6.71)	‹ 0.001
	Bad
Working time	Extended	2.23 (1.33 - 3.72)	0.002
	Normal

4. Discussion

4.1. Study Limits

The main difficulties were related to the unavailability of participants, as well as the information biases common to descriptive studies. The methodology used enabled us to minimize bias, and to collect and analyze data on working and living conditions, including the psychological state of participants in the month preceding the survey.

4.2. Socio-Professional Characteristics

Nurses (77.6%) and women were overrepresented in the sample (81.7%). PD was not associated to these characteristics despite the consistent evidences that exist about these risk factors [13]. This overrepresentation of nurses is also observed worldwide among the HCW (76.9%) [26]. Regarding gender, the feminization of healthcare human resources is underway globally. According to Stamar et al, the number of women in healthcare is increasing annually by 4.5% in low-income countries, 5.8% in middle-income countries and 1.1% in rich countries [27]. This trend of feminization is also observed in African countries [28], China [29], Brazil [30] and the USA [31]. According to Sirois and Owens, age is a significant risk factor for PD during infectious diseases outbreaks [13]. The participants in our study were young adults, with and average age of 35.38 ± 8.91 [19 - 60 years]. They were younger than nurses in the USA whose average age increased from 46.8 [31] to 52 [32] between 2004 and 2019. In terms of numbers, the WHO estimates that there are 30 millions nurses worldwide, with densities varying from 7/10.000 inhabitants to 95/10.000 inhabitants between low-income and high-income countries respectively [26]. The highest numbers are found in the USA (4.2 million nurses in 2019) [32]. and China (4.7 million in 2020) [29]. In our context, there were more than 15.000 nurses and assistant-nurses in 2011 [33]. Regarding the marital status, no significant association was found with PD as per the available evidences [13].

4.3. Living and Working Conditions

The majority of participants faced challenging living and working conditions, characterized by low wages, regular overtime and poor sleep quality. The salaries of the majority of participants (63.2%) were low and insufficient (<100.000 C.FAF ≈ €152) to cover their social responsibilities. In an environment of widespread poverty, some resorted to income generating activities (24.4%; p < 0.021) while others rely on overtime (63.7%; p < 0.0001). Regular overtime is not without risk to physical and psychological health. According to Ross, overtime exposes workers to excessive fatigue [34] [35] and physical and mental health disorders [35]. These includes chronic illnesses, minor psychiatric disorders, sleep disorders, family problems even social marginalization [36]. Sleep disorders need to be taken seriously as they can contribute to the onset of other pathologies such as metabolic syndrome, hypertension, cardiovascular diseases, obesity and impaired glycemic regulation [37]. The poor sleep quality reported by the majority of participants (54.3%) could be caused by overtime [36]. Overtime can also lead to the reduction of alertness and cognitive performance [38] or interfere with nurses’ abilities to achieve work’s objectives, impacting the quantity of services provided, job satisfaction, engagement in operations and desire to continue working [39].

Giving that the practice of physical and sporting activities (54.2%) and recreational activities (30.7%) was average, the use of aroma inhalation therapy could benefit the nurses in our study because of its proven effectiveness against sleep disorders, stress, fatigue and depression [40].

4.4. Psychological Distress and Associated Factors

In the literature, the prevalence of PD in the general population varies from 5% to 27% [8]-[10] [41] [42]. Corresponding prevalence’s have been obtained in Guandong, China (25.1%) [43] and Canada (20.74%) [11]. Among the workforce, prevalence’s also varied, whether they were HCW or not. For the healthcare personnel involved in recent pandemics such as SARS, H1N1, MERS and COVID19, PD prevalence’s ranged from 14.1% to 76.4% [13]. In terms of severity, the prevalence of severe PD was lower in our study [32.8%], than in Ethiopia (27.7%) [44]. India (35%) [45], Jordan (41%) [15], Malaysia (41%) [46] and Nigeria (44.1%) [47]. During the recent COVID-19 pandemic, working conditions were disrupted in several sectors, contributing to increased psychological pressure among workers. In the same context in Nigeria, Olagunju et al revealed a significant association between PD and sleeping problems that were reported by the HCW r = 0.2; p = 0.001) [48]. Higher levels of PD were also observed in South-Africa (57.4%) [49] and Wuhan, China (65%) [50]. In hospitals, nurses where the first line of defense in the fight against COVID-19. Their risk of contamination was very high, and they had to deal with suffering and death. The fear of contracting COVID-19 during care (ORa = 2.35, p < 0.01) [19] could explain the high prevalence of PD observed in this category of workers.

Out of hospitals, non-healthcare staff were also exposed to PD. Similar prevalence’s of PD were observed among workers in private (65.1%, p = 0.04) and public (61%, p = 0.04) companies in Spain [12] and among young workers and professionals in Canada (p < 0.01) [51]. The prevalence of PD in our study (19.5%) is similar to those observed in other European and Northern American workers (15 - 20%) [52], and higher than the prevalence of severe PD in Akita workers in Japan (10.8%) [53].

Epidemiologically, PD may be caused by or associated with certain individual, social and occupational characteristics and working conditions. It was significantly associated with age, gender, smoking, alcohol consumption, sense of cohesion, place of control and stressful childhood events (p < 0.01) [51] and sleep disorders [54].

We identified 3 risk factors and one protective factor for PD in nurses. The effects of long working hours [OR = 2.23 (1.33 - 3.72); p = 0.002] and poor sleep quality [OR = 3.98 (2.36 - 6.71); p = 0.001] can impact the functioning and metabolism of human body as already described by other authors [36] [55]. The majority of nurses in our study had poor sleep quality (54.3%). This poor sleep quality was also observed among their colleagues during the COVID-19 pandemic (p < 0.05) [54], and among nurses performing shift work in England [56]. At the organization level, sleep distress can induce complications such as metabolic syndrome [37] [57] and other pathologies such as high blood pressure, cardiovascular diseases, obesity and impaired glycemic regulation [37]. These disorders need to be corrected, as good sleep provides a sense of rest and normalcy, whereas poor sleep will induce fatigue, irritability, daytime dysfunction and slowed-down functioning [55].

The association of the catholic faith with PD [OR = 3.6 (1.04 - 13.0); p = 0.04] raises questions about the perception that nurses of the Catholic faith had regarding the true origin and significance of the COVID-19 pandemic, which some viewed as an apocalyptic event. Intrinsically, working overtime appeared to have a protective effect against PD [OR = 0.60 (0.36 - 0.99); p = 0.99); p = 0.004]. Some employees worked overtime to supplement their insufficient financial income which prevented them from meeting their social and economic obligations. Our result is controversial as working overtime is associated with increased risk of morbidity and mortality in workers [38]. Working overtime can also result in poor quality of nursing care (OR = 1.32), poor patient safety (OR = 1.67) and higher rates of care left undone (RR = 1.29) [58].

5. Conclusion

The participants in our study were predominantly female nurses in their thirties. Their working and living conditions were challenging, characterized by low incomes, long working hours and frequent overtime. Psychological distress was highly prevalent in our study and was associated with long working hours, poor sleep quality and adherence to the catholic faith. Improvements in the living and working conditions of these workers are essential to prevent the progression of this distress into traumatic psychosomatic complications.

Conflicts of Interest

The authors declare no conflicts of interest regarding the publication of this paper.

References

[1]	Drapeau, A., Marchand, A. and Beaulieu-Prevost, D. (2012) Epidemiology of Psychological Distress. In: L’Abate, L., Ed., Mental Illnesses-Understanding, Prediction and Control, InTech, 105-134.[CrossRef]
[2]	Keyes, C.L.M. (2002) Selecting Outcomes for the Sociology of Mental Health: Issues of Measurement and Dimensionality. Journal of Health and Social Behavior, 43, 1207-222.
[3]	Dohrenwend, B.P. (1980) Nonspecific Psychological Distress and Other Dimensions of Psychopathology. Archives of General Psychiatry, 37, 1229-1236.[CrossRef] [PubMed]
[4]	Simard, M., Hudon, C. and van Reekum, R. (2009) Psychological Distress and Risk for Dementia. Current Psychiatry Reports, 11, 41-47.[CrossRef] [PubMed]
[5]	Black, D. and Grant, J. (2014) DSM-5 Guidebook: The Essential Companion to the Diagnostic and Statistical Manual of Mental Disorders. 5th Edition, American Psychiatric Pub.
[6]	Franzen, P.L. and Buysse, D.J. (2008) Sleep Disturbances and Depression: Risk Relationships for Subsequent Depression and Therapeutic Implications. Dialogues in Clinical Neuroscience, 10, 473-481.[CrossRef]
[7]	World Health Organization (WHO) (2008) The Global Burden of Disease 2004. 31-37.
[8]	Benzeval, M. and Judge, K. (2001) Income and Health: The Time Dimension. Social Science & Medicine, 52, 1371-1390.[CrossRef] [PubMed]
[9]	Chittleborough, C.R., Winefield, H., Gill, T.K., Koster, C. and Taylor, A.W. (2010) Age Differences in Associations between Psychological Distress and Chronic Conditions. International Journal of Public Health, 56, 71-80.[CrossRef] [PubMed]
[10]	Gispert, R., Rajmil, L., Schiaffino, A. and Herdman, M. (2003) Sociodemographic and Health-Related Correlates of Psychiatric Distress in a General Population. Social Psychiatry and Psychiatric Epidemiology, 38, 677-683.[CrossRef] [PubMed]
[11]	Caron, J. and Liu, A. (2010) Étude descriptive de la prévalence de la détresse psychologique et des troubles mentaux au sein de la population canadienne: Comparaison entre la population à faible revenu et la population à revenu plus élevé. Maladies chroniques et blessures au Canada, 30, 86-97.[CrossRef]
[12]	Ruiz-Frutos, C., Ortega-Moreno, M., Allande-Cussó, R., Domínguez-Salas, S., Dias, A. and Gómez-Salgado, J. (2021) Health-Related Factors of Psychological Distress during the COVID-19 Pandemic among Non-Health Workers in Spain. Safety Science, 133, Article 104996.[CrossRef] [PubMed]
[13]	Sirois, F.M. and Owens, J. (2021) Factors Associated with Psychological Distress in Health-Care Workers during an Infectious Disease Outbreak: A Rapid Systematic Review of the Evidence. Frontiers in Psychiatry, 11, Article 589545.[CrossRef] [PubMed]
[14]	Dufour, M., Bergeron, N., Rabasa, A., Guay, S. and Geoffrion, S. (2021) Assessment of Psychological Distress in Health-Care Workers during and after the First Wave of COVID-19: A Canadian Longitudinal Study. The Canadian Journal of Psychiatry, 66, 807-814.[CrossRef] [PubMed]
[15]	Shahrour, G. and Dardas, L.A. (2020) Acute Stress Disorder, Coping Self‐Efficacy and Subsequent Psychological Distress among Nurses Amid COVID‐19. Journal of Nursing Management, 28, 1686-1695.[CrossRef] [PubMed]
[16]	Altwaijri, Y., Bilal, L., Almeharish, A., BinMuammar, A., DeVol, E., Hyder, S., et al. (2022) Psychological Distress Reported by Healthcare Workers in Saudi Arabia during the COVID-19 Pandemic: A Cross-Sectional Study. PLOS ONE, 17, e0268976.[CrossRef] [PubMed]
[17]	Rollin, L., Gehanno, J. and Leroyer, A. (2022) Occupational Stressors in Healthcare Workers in France. Revue d’Épidémiologie et de Santé Publique, 70, 59-65.[CrossRef] [PubMed]
[18]	Cena, L., Rota, M., Calza, S., Janos, J., Trainini, A. and Stefana, A. (2021) Psychological Distress in Healthcare Workers between the First and Second COVID-19 Waves: The Role of Personality Traits, Attachment Style, and Metacognitive Functioning as Protective and Vulnerability Factors. International Journal of Environmental Research and Public Health, 18, Article 11843.[CrossRef] [PubMed]
[19]	Lee, H., Wilson, K.S., Bernstein, C., Naicker, N., Yassi, A. and Spiegel, J.M. (2022) Psychological Distress in South African Healthcare Workers Early in the COVID-19 Pandemic: An Analysis of Associations and Mitigating Factors. International Journal of Environmental Research and Public Health, 19, Article 9722.[CrossRef] [PubMed]
[20]	Nguepy, K.F.R., Mboua, P.C., Djifack, T.T., et al. (2021) Psychological Distress among Health Care Professionals of the Three COVID-19 Most Affected Regions in Cameroon: Prevalence and Associated Factors. https://www.em-consulte.com/article/1422826/psychological-distress-among-health-care-professionals
[21]	Andrews, G. and Slade, T. (2001) Interpreting Scores on the Kessler Psychological Distress Scale (K10). Australian and New Zealand Journal of Public Health, 25, 494-497. [Google Scholar] [CrossRef] [PubMed]
[22]	Furukawa, T.A., Kessler, R.C., Slade, T. and Andrews, G. (2003) The Performance of the K6 and K10 Screening Scales for Psychological Distress in the Australian National Survey of Mental Health and Well-Being. Psychological Medicine, 33, 357-362.[CrossRef] [PubMed]
[23]	Furukawa, T.A., Kawakami, N., Saitoh, M., Ono, Y., Nakane, Y., Nakamura, Y., et al. (2008) The Performance of the Japanese Version of the K6 and K10 in the World Mental Health Survey Japan. International Journal of Methods in Psychiatric Research, 17, 152-158.[CrossRef] [PubMed]
[24]	Fassaert, T., De Wit, M.A.S., Tuinebreijer, W.C., Wouters, H., Verhoeff, A.P., Beekman, A.T.F., et al. (2009) Psychometric Properties of an Interviewer‐Administered Version of the Kessler Psychological Distress Scale (K10) among Dutch, Moroccan and Turkish Respondents. International Journal of Methods in Psychiatric Research, 18, 159-168.[CrossRef] [PubMed]
[25]	Cohen, S., Spacapan, S. and Oskamp, S. (1988) Perceived Stress in a Probability Sample of the United States. In: Spacapan, S. and Oskamp, S., Eds., The Social Psychology of Health, Sage Publications Inc., 31-67.
[26]	Kharazmi, E., Bordbar, N. and Bordbar, S. (2023) Distribution of Nursing Workforce in the World Using Gini Coefficient. BMC Nursing, 22, Article No. 151.[CrossRef] [PubMed]
[27]	Shannon, G., Minckas, N., Tan, D., Haghparast-Bidgoli, H., Batura, N. and Mannell, J. (2019) Feminisation of the Health Workforce and Wage Conditions of Health Professions: An Exploratory Analysis. Human Resources for Health, 17, Article No. 72.[CrossRef] [PubMed]
[28]	Russo, G., Gonçalves, L., Craveiro, I. and Dussault, G. (2015) Feminization of the Medical Workforce in Low-Income Settings; Findings from Surveys in Three African Capital Cities. Human Resources for Health, 13, Article No. 64.[CrossRef] [PubMed]
[29]	Li, M., Raven, J. and Liu, X. (2024) Feminization of the Health Workforce in China: Exploring Gendered Composition from 2002 to 2020. Human Resources for Health, 22, Article No. 15.[CrossRef] [PubMed]
[30]	De Souza, H.S., Trapé, C.A., Campos, C.M.S. and Soares, C.B. (2021) The Brazilian Nursing Workforce Faced with the International Trends: An Analysis in the International Year of Nursing. Physis: Revista de Saúde Coletiva, 31, e310111.[CrossRef]
[31]	Aiken, L. and Cheung, R. (2008) Nurse Workforce Challenges in the United States: Implications for Policy. OECD Health Working Papers No. 35. OECD Publishing.[CrossRef]
[32]	Smiley, R.A., Ruttinger, C., Oliveira, C.M., Hudson, L.R., Allgeyer, R., Reneau, K.A., et al. (2021) The 2020 National Nursing Workforce Survey. Journal of Nursing Regulation, 12, S1-S96.[CrossRef]
[33]	Ministère de la Santé Publique (MINSANTE) (2010) Politique, plannification des RHS et situation des effectifs. In: Ministère de la Santé Publique Ed., Analyse de la situation des ressources hmaines pour la santé au Cameroun, Ministère de la Santé Publique, 70-96.
[34]	van Dijk, F.J.H. and Swaen, G.M.H. (2003) Fatigue at Work. Occupational and Environmental Medicine, 60, i1-i2.[CrossRef] [PubMed]
[35]	Rosa, R.R. (1995) Extended Workshifts and Excessive Fatigue. Journal of Sleep Research, 4, 51-56.[CrossRef] [PubMed]
[36]	Keller, S.M., Berryman, P. and Lukes, E. (2009) Effects of Extended Work Shifts and Shift Work on Patient Safety, Productivity, and Employee Health. AAOHN Journal, 57, 497-504.[CrossRef] [PubMed]
[37]	Gozal, D., Dumin, M. and Koren, D. (2016) Role of Sleep Quality in the Metabolic Syndrome. Diabetes, Metabolic Syndrome and Obesity: Targets and Therapy, 9, 281-310.[CrossRef] [PubMed]
[38]	Caruso, C.C., Hitchcock, E.M., Dick, R.B. and Russo, J.M. (2004) Overtime and Extended Work Shifts: Recent Findings on Illnesses, Injuries, and Health Behaviors. US Department of Health and Human Services.
[39]	Kunaviktikul, W., Wichaikhum, O., Nantsupawat, A., Nantsupawat, R., Chontawan, R., Klunklin, A., et al. (2015) Nurses’ Extended Work Hours: Patient, Nurse and Organizational Outcomes. International Nursing Review, 62, 386-393.[CrossRef] [PubMed]
[40]	Kang, J., Noh, W. and Lee, Y. (2020) Sleep Quality among Shift-Work Nurses: A Systematic Review and Meta-Analysis. Applied Nursing Research, 52, Article 151227.[CrossRef] [PubMed]
[41]	Kuriyama, S., Nakaya, N., Ohmori-Matsuda, K., Shimazu, T., Kikuchi, N., Kakizaki, M., et al. (2009) Factors Associated with Psychological Distress in a Community-Dwelling Japanese Population: The Ohsaki Cohort 2006 Study. Journal of Epidemiology, 19, 294-302.[CrossRef] [PubMed]
[42]	Phongsavan, P., Chey, T., Bauman, A., Brooks, R. and Silove, D. (2006) Social Capital, Socio-Economic Status and Psychological Distress among Australian Adults. Social Science & Medicine, 63, 2546-2561.[CrossRef] [PubMed]
[43]	Nie, A., Su, X., Zhang, S., Guan, W. and Li, J. (2020) Psychological Impact of COVID‐19 Outbreak on Frontline Nurses: A Cross‐Sectional Survey Study. Journal of Clinical Nursing, 29, 4217-4226.[CrossRef] [PubMed]
[44]	Belay, A.S., Guangul, M.M., Asmare, W.N. and Mesafint, G. (2021) Prevalence and Associated Factors of Psychological Distress among Nurses in Public Hospitals, Southwest, Ethiopia: A Cross-Sectional Study. Ethiopian Journal of Health Sciences, 31, 1247-1256.[CrossRef] [PubMed]
[45]	Shruthi, M.N., Veena, V. and Seeri, J.S. (2023) Prevalence of Psychological Distress and Perceived Stress among Nursing Staff in a Tertiary Care Center, Bengaluru. MRIMS Journal of Health Sciences, 11, 41-47.[CrossRef]
[46]	Ghawadra, S.F., Abdullah, K.L., Choo, W.Y. and Phang, C.K. (2019) Psychological Distress and Its Association with Job Satisfaction among Nurses in a Teaching Hospital. Journal of Clinical Nursing, 28, 4087-4097.[CrossRef] [PubMed]
[47]	Okwaraji, F. and En, A. (2014) Burnout and Psychological Distress among Nurses in a Nigerian Tertiary Health Institution. African Health Sciences, 14, 237-245.[CrossRef] [PubMed]
[48]	Olagunju, A.T., Bioku, A.A., Olagunju, T.O., Sarimiye, F.O., Onwuameze, O.E. and Halbreich, U. (2021) Psychological Distress and Sleep Problems in Healthcare Workers in a Developing Context during COVID-19 Pandemic: Implications for Workplace Wellbeing. Progress in Neuro-Psychopharmacology and Biological Psychiatry, 110, Article 110292.[CrossRef] [PubMed]
[49]	Lee, H., Wilson, K.S., Bernstein, C., Naicker, N., Yassi, A. and Spiegel, J.M. (2022) Psychological Distress in South African Healthcare Workers Early in the COVID-19 Pandemic: An Analysis of Associations and Mitigating Factors. International Journal of Environmental Research and Public Health, 19, Article 9722.[CrossRef] [PubMed]
[50]	Ahmed, F., Zhao, F. and Faraz, N.A. (2020) How and When Does Inclusive Leadership Curb Psychological Distress during a Crisis? Evidence from the COVID-19 Outbreak. Frontiers in Psychology, 11, Article 1898.[CrossRef] [PubMed]
[51]	Cadieux, N. and Marchand, A. (2014) Psychological Distress in the Workforce: A Multilevel and Longitudinal Analysis of the Case of Regulated Occupations in Canada. BMC Public Health, 14, Article No. 808.[CrossRef] [PubMed]
[52]	International Labour Office (2000) Mental Health in the Workplace.
[53]	Fushimi, M., Saito, S., Shimizu, T., Kudo, Y., Seki, M. and Murata, K. (2011) Prevalence of Psychological Distress, as Measured by the Kessler 6 (K6), and Related Factors in Japanese Employees. Community Mental Health Journal, 48, 328-335.[CrossRef] [PubMed]
[54]	Alimoradi, Z., Broström, A., Tsang, H.W.H., Griffiths, M.D., Haghayegh, S., Ohayon, M.M., et al. (2021) Sleep Problems during COVID-19 Pandemic and Its’ Association to Psychological Distress: A Systematic Review and Meta-Analysis. EClinicalMedicine, 36, Article 100916.[CrossRef] [PubMed]
[55]	Nelson, K.L., Davis, J.E. and Corbett, C.F. (2021) Sleep Quality: An Evolutionary Concept Analysis. Nursing Forum, 57, 144-151.[CrossRef] [PubMed]
[56]	McDowall, K., Murphy, E. and Anderson, K. (2017) The Impact of Shift Work on Sleep Quality among Nurses. Occupational Medicine, 67, 621-625.[CrossRef] [PubMed]
[57]	Chaiard, J., Deeluea, J., Suksatit, B., Songkham, W., Inta, N. and Stone, T.E. (2019) Sleep Disturbances and Related Factors among Nurses. Nursing & Health Sciences, 21, 470-478.[CrossRef] [PubMed]
[58]	Griffiths, P., Dall’Ora, C., Simon, M., Ball, J., Lindqvist, R., Rafferty, A., et al. (2014) Nurses’ Shift Length and Overtime Working in 12 European Countries: The Association with Perceived Quality of Care and Patient Safety. Medical Care, 52, 975-981.[CrossRef] [PubMed]