Resilience in nursing: a comparative analysis of hospital and primary healthcare settings

Abstract

Objective

This study evaluated resilience levels and their key predictors among nurses working in hospital and primary healthcare settings in Eastern Saudi Arabia.

Methods

A cross-sectional survey was conducted with 479 nurses using the validated Connor-Davidson Resilience Scale (CD-RISC-25). Multiple regression analysis was performed to identify predictors of resilience based on demographic and occupational variables, including years of experience, education, job position, and work setting.

Results

Hospital nurses demonstrated significantly higher resilience levels compared to primary care nurses. Resilience was positively associated with longer work experience and higher educational attainment. Nurses in supervisory or quality-related roles exhibited higher resilience than staff nurses.

Conclusion

The findings underscore the importance of institutional and educational support in fostering resilience among nurses. Tailored interventions, especially within primary healthcare settings, are essential to strengthen coping capacities and promote psychological well-being in the nursing workforce.

Clinical trial registration number

Not applicable.

Introduction

Resilience is a critical attribute for nursing professionals, enabling them to adapt to the complex and high-stress demands of healthcare environments. Defined as the ability to recover from adversity, resilience plays a crucial role in ensuring nurses’ psychological well-being, job satisfaction, and professional longevity [1]. As frontline healthcare providers, nurses frequently encounter emotional distress, high patient loads, and workplace pressures, all of which necessitate strong coping mechanisms [2]. While resilience has been widely studied in hospital settings, there is a growing need to examine how it varies across different healthcare environments, particularly in primary care settings where stressors may differ.

Resilience is essential in nursing, as it helps professionals manage occupational stress, prevent burnout, and sustain high-quality patient care. Studies indicate that higher resilience levels are associated with better job performance, enhanced patient outcomes, and improved overall well-being [3]. Nurses with greater resilience are more likely to develop adaptive coping strategies, maintain emotional stability, and engage in self-care practices that mitigate workplace stress [4]. Moreover, resilience is linked to job retention, reducing turnover rates, and ensuring continuity of care in healthcare institutions [5].

Recent research has emphasised that resilience-building interventions are particularly beneficial for nurses working in high-stress environments, such as intensive care units and emergency departments [6]. However, resilience is equally important for nurses in primary healthcare settings, where they often face different but similarly challenging work conditions, including long-term patient care, community outreach, and resource limitations.

The working environments in hospitals and primary healthcare centres differ significantly, impacting nurses’ resilience levels in various ways. Hospital nurses frequently work in fast-paced, high-pressure environments where they deal with acute medical emergencies, complex procedures, and multidisciplinary team interactions [7]. These challenges necessitate strong emotional resilience to cope with patient suffering, high workloads, and demanding shift schedules.

In contrast, primary healthcare nurses focus more on preventive care, chronic disease management, and community health programs. While they may not face the same immediate clinical pressures as hospital nurses, they often encounter different stressors, such as limited resources, patient non-compliance, and extended patient care responsibilities [8]. Furthermore, primary care nurses may work in isolated environments with less direct support from colleagues, which can contribute to lower resilience levels [9].

Empirical studies support these differences in resilience levels between hospital and primary care nurses. For instance, a recent cross-sectional study found that hospital nurses exhibited significantly higher resilience scores compared to their counterparts in primary care settings [10]. This discrepancy may be attributed to increased teamwork, structured protocols, and specialised training in hospitals, which help nurses develop coping strategies more effectively.

Understanding resilience within the Saudi Arabian nursing context is especially important due to the nation’s unique cultural values, professional hierarchies, and ongoing healthcare transformation. Under Saudi Vision 2030, the healthcare system is experiencing rapid reform, including workforce nationalization, increased digitalization, and expanded privatization, all of which have intensified the demands placed on nurses [11]. In many Saudi hospitals, rigid hierarchical structures can limit nurses’ autonomy and decision-making capacity, which may negatively impact their psychological resilience [12]. Moreover, sociocultural expectations—such as emotional restraint, gendered workplace roles, and collectivist identity—affect how stress is perceived and managed among Saudi nurses [13]. These systemic and cultural factors distinguish the Saudi context from Western healthcare systems and support the need for localized research on resilience in nursing.

Several demographic and occupational factors influence resilience in nursing professionals, the factors that was discussed is years of experience, job le, education level, and workplace setting.

One of the most significant predictors is years of experience. Research has consistently shown that seasoned nurses exhibit higher resilience levels than their early-career counterparts, likely due to accumulated coping skills and professional confidence [14]. For example, a study by Yasui [15] found that nurses with over 20 years of experience demonstrated the highest levels of resilience, suggesting that it may develop over time through exposure to various clinical challenges.

Job position is another crucial factor influencing resilience. Nurses in leadership roles, such as supervisors and quality staff, often report higher resilience scores compared to staff nurses [16]. This may be attributed to increased autonomy, decision-making authority, and a greater sense of professional accomplishment. Conversely, entry-level nurses may experience higher levels of stress due to role uncertainty, workload pressures, and limited coping mechanisms.

Educational attainment also plays a role in resilience. Studies indicate that nurses with higher education levels, such as bachelor’s or master’s degrees, tend to have stronger resilience than those with diploma-level qualifications [17]. Higher education may equip nurses with better problem-solving skills, emotional intelligence, and theoretical knowledge on stress management, thereby enhancing their ability to navigate workplace challenges.

Despite the growing body of literature on nursing resilience, few studies have specifically compared resilience levels between hospital and primary healthcare nurses, particularly in the Middle Eastern context. Most existing research has been conducted in Western settings, where healthcare systems, work environments, and cultural factors may differ [18]. Understanding resilience dynamics within the Saudi Arabian healthcare system is crucial for designing tailored interventions that address the unique challenges faced by nurses in this region.

This study aims to bridge this research gap by evaluating resilience levels among hospital and primary healthcare nurses in Eastern Saudi Arabia. The results will help policymakers, hospital administrators, and healthcare educators come up with ways to make nurses more resilient by revealing key predictors of resilience and examining differences in the workplace. Ultimately, strengthening resilience among nursing professionals can lead to better patient care, improved job satisfaction, and reduced burnout rates across healthcare settings. The study also aims to determine the levels and determinants of resilience among nursing professionals across hospital and primary healthcare settings, considering demographic and occupational variables.

Method

Study design & sitting

This study used a cross-sectional quantitative design. This study was conducted in the Eastern Province of Saudi Arabia, encompassing both hospitals and primary healthcare centers. Participants were recruited from public healthcare facilities to ensure a diverse representation of nurses operating in various settings. The hospital component included both general and specialized care units, while the primary healthcare aspect concentrated on community health centers. By selecting these locations, the study aimed to elucidate differences in work environments and professional roles, thereby facilitating a comprehensive comparison of resilience levels among nurses across different types of healthcare settings.

Sampling & participant

Convenience sampling was used, and the survey was sent to qualified nurses who were available and willing to take part throughout the study period. The study aimed to obtain a representative sample of nurses employed in primary health care and hospital healthcare settings within selected facilities in the Eastern Province of Saudi Arabia. The study population consisted of nursing directors, nurse managers, nurse educators, and bedside nurses who had been employed in Saudi Arabia for a minimum of two years. Individuals in non-clinical roles, such as nursing assistants, patient care technicians, and those working in human resources, finance, or inventory management, were excluded from the study.

The required sample size for this study was determined utilizing the following formula:

Where Z = 1.96 (corresponding to a 95% confidence level), P = 0.5 (the most conservative estimate of the population proportion), and d = 0.0462 (the margin of error). First, the Z-value was squared, yielding (1.96)2 = 3.8416. The numerator was calculated as 3.8416 × 0.5 × 0.5 = 0.9604. The margin of error was squared as (0.0462)2 = 0.00213444. Finally, dividing the numerator by the squared margin of error resulted in a required sample size of approximately 450 participants. This calculation ensures that the study maintains a 95% confidence level with a margin of error of 4.62%.

Data collection tool

Data were collected via a structured Google Forms survey, available in both Arabic and English. The tool consisted of three sections:

1. Demographics – Age, gender, education, job role, years of experience, and workplace.

2. Connor-Davidson Resilience Scale (CD-RISC-25) – A validated 25-item scale scored on a 5-point Likert scale (0–4), with total scores ranging from 0 to 100. Higher scores reflect greater resilience.

3. Consent Section – Participants provided digital informed consent before continuing.

The Arabic version of the CD-RISC-25 used in this study was previously validated in healthcare research. Due to prior validation, a pilot was deemed unnecessary. Permission to use the tool and scoring manual was obtained from the scale’s developers.

The tool was initially accessible on the main website in both Arabic and English, eliminating the necessity for a pilot. Data for this study was gathered utilising a Google forms.

The survey comprised three sections:

1. Demographic Information: age, gender, nationality, education level, years of experience, employer, and job position.

2. Connor-Davidson Resilience Scale (CD-RISC-25): a validated tool developed by Connor and Davidson in 2003 [19] to measure resilience and the ability to handle stress. It consists of 25 items rated on a 5-point Likert scale (0 = not true, 4 = virtually always true). Scores are summed, ranging from 0 to 100, with higher scores indicating greater resilience. The scale is well-validated and reliable, with a Cronbach’s alpha of 0.93. The resilience score is divided into three categories: low resilience, which includes scores ranging from 0 to 73 (the lowest quartile); moderate resilience, with scores between 74 and 90 (the second and third quartiles); and high resilience, which consists of scores from 91 to 100 (the highest quartile) [20].

3. Consent Section: participants provided digital informed consent before continuing.

Ethical approval and consent to participate

Ethical approval for the present study was secured from the Institutional Review Board of the Dammam Health Network, Saudi Arabia (IRB Protocol No. NUR-44). All procedures adhered to the principles outlined in the Declaration of Helsinki. Informed consent was obtained from all participants prior to the commencement of data collection. Participation was entirely voluntary, and participants were duly informed of their right to withdraw from the study at any point without facing any repercussions.

Availability of data and materials

The datasets generated and analyzed during the course of this study are accessible from the corresponding author upon reasonable request.

Data collection

A Google Forms-based online survey was used to collect data for this study. Nurses employed in hospitals and primary healthcare facilities in the Eastern Province of Saudi Arabia received the survey. The goal of the study was fully explained to participants before the survey started, and their informed consent was obtained from all the participants prior response to survey. The data collection period lasted two months, and they could fill out the survey whenever it was convenient for them. Participants’ anonymity and confidentiality were preserved since all answers were safely stored and no personally identifiable information was collected.

Data analysis

The data was analyzed using SPSS version 22.0.Descriptive statistics, such as frequencies, means, standard deviations, median, minimum, maximum, and interquartile range (IQR), were used to summarize participants’ sociodemographic details, and resilience levels. To maintain data integrity, Excel was used to identify unengaged responses, defined as those with identical answers across all questions (SD < 0.1). Responses with a standard deviation exceeding 0.1 were kept, except for 40 participants who showed no variation in their answers. Potential outliers were identified using Mahalanobis distances, with cases surpassing a threshold of 20.75 (p < 0.001) excluded from further analysis. SPSS was employed to conduct independent samples t-tests and ANOVA, comparing resilience levels between nurses in hospital and primary healthcare environments. Chi-square tests were utilized to examine differences in resilience scores between hospital and primary care nurses. To determine significant predictors of resilience, multiple regression analysis was employed, taking into account factors such as years of experience, education level, job position, and work setting. A significance level of p < 0.05 was applied to all analyses.

Results

The Cronbach’s Alpha coefficient for total resilience was found to be 0.929. The Alpha values for the subscales were as follows: Acceptance (0.740), Trust (0.809), Competence (0.852), and Control (0.640). The Spiritual subscale did not yield a calculable Alpha value, as it comprised only two items.

Table 1 presents a summary of the characteristics of the 479 participants. A significant proportion of the participants were employed in hospital settings (77.2%), identified as female (93.9%), and possessed either a Bachelor’s degree (46.6%) or a diploma (45.1%). The majority of participants reported having 11–20 years of experience (56.6%), with the predominant job position being staff nurse (83.3%). Fewer participants occupied roles such as managers (6.7%) or supervisors (4.4%).

Table 1.

Characteristics of the participants (N = 479)

		Frequency	Percent
Setting	Hospital	370	77.2
	Primary Health	109	22.8
Gender	Male	29	6.1
	Female	450	93.9
Education Level	BSc	223	46.6
	Diploma	216	45.1
	Higher Diploma	17	3.5
	MSN	23	4.8
Experience (years)	< 2	24	5.0
	2–10	130	27.1
	11–20	271	56.6
	21–30	45	9.4
	> 30	9	1.9
Job position	Director	5	1.0
	Educator	16	3.3
	Manager	32	6.7
	Quality Staff	6	1.3
	Staff	399	83.3
	Supervisor	21	4.4

The table presents descriptive statistics for total resilience and its subscales. The overall resilience score has a mean of 74.4. The Acceptance subscale has a mean of 14.8, indicating moderate levels of acceptance, while the Trust subscale (mean = 19.3) and the Competence subscale (mean = 24.8) suggest relatively high levels in these domains. The Control subscale has a mean of 9.0, reflecting moderate perceived control (Table 2).

Table 2.

Descriptive statistics for total resilience and its subscales

Scale and Subscales	Mean	SD	Median	Minimum	Maximum
Total resilience	74.4	12.7	74.0	5.0	99.0
Acceptance	14.8	2.8	15.0	3.0	20.0
Trust	19.3	4.3	19.0	0.0	28.0
Competence	24.8	4.5	25.0	0.0	32.0
Control	9.0	1.9	9.0	0.0	12.0
Spiritual	6.5	1.3	7.0	0.0	8.0

Table 3 illustrates the distribution of resilience levels across various settings. Within the total sample (n = 479), 48.6% of participants exhibited low resilience, predominantly within hospital settings (70.8%), while a lesser proportion was found in primary health settings (29.2%). Moderate resilience was observed in 34.2% of the total sample, with a higher prevalence in hospital settings (80.5%) compared to primary health settings (19.5%). Finally, 17.1% of participants demonstrated high resilience, with 89.0% located in hospital settings and only 11.0% in primary health settings. A Chi-square test indicated a significant difference in resilience levels between the two settings, yielding a value of 12.934 and a p-value of 0.002.

Table 3.

Distribution of resilience levels by setting and chi-square test results (n = 479)

Low (≤ 73)	233 (48.6)	165 (70.8%)	68 (29.2%)
Moderate (74–90)	164 (34.2)	132 (80.5%)	32 (19.5%)
High (≥ 91)	82 (17.1)	73 (89.0%)	9 (11.0%)
	Chi Square Value	12.934
	Sig.	0.002

Table 4 illustrates significant variations in resilience scores across various demographic and professional variables. Participants employed in hospital settings reported a mean resilience score of 75.4, which is higher than that of their counterparts in primary health settings, who had a mean score of 71.2. Furthermore, males demonstrated a higher mean resilience score of 77.1 compared to females, whose mean score was 74.3. Individuals holding a Master’s in Nursing exhibited the highest mean resilience score of 78.0, whereas those with a Diploma achieved the lowest mean score of 73.3. Participants with 21–30 years of experience reported the highest resilience score, with a mean of 77.8. Among various job positions, Quality Staff achieved the highest mean score of 86.0, while Staff members recorded the lowest mean score of 73.7.

Table 4.

Descriptive statistics of resilience scores by demographic and professional variables (n = 479)

		Mean	SD	Median	IQR
Setting	Hospital	75.4	12.7	75.0	16.0
	Primary Health	71.2	12.0	71.0	13.0
Gender	Male	77.1	10.5	78.0	14.0
	Female	74.3	12.8	74.0	16.0
Education Level	BSc	75.1	13.3	75.0	17.0
	Diploma	73.3	12.2	72.0	13.0
	Higher Diploma	74.9	10.9	74.0	16.0
	MSN	78.0	12.5	77.0	23.0
Experience (years)	< 2	72.4	15.1	73.0	26.5
	2–10	72.5	11.5	73.5	13.0
	11–20	74.9	13.1	74.0	16.0
	21–30	77.8	11.5	75.0	18.0
	> 30	76.7	13.2	82.0	22.0
Job position	Director	76.2	12.4	73.0	23.0
	Educator	78.9	15.0	81.0	23.0
	Manager	74.8	10.7	72.5	17.5
	Quality Staff	86.0	10.8	89.0	18.5
	Staff	73.7	12.7	73.0	15.0
	Supervisor	80.8	11.6	79.0	18.5

SD; Standard Deviation, IQR; Interquartile Range

Table 5 presents a comparison of resilience levels across various demographic factors in hospital and primary healthcare environments. Among nurses overall, resilience generally increased with years of experience, peaking at 21–30 years (mean = 77.8) and for those with over 30 years (mean = 76.7), although this difference was not statistically significant (p = 0.110). Job position had a significant impact on resilience (p = 0.017), with quality staff demonstrating the highest resilience (mean = 86.0), followed by supervisors (mean = 80.8) and educators (mean = 78.9). Conversely, staff nurses and managers exhibited lower resilience scores. Post-hoc tests indicated significant differences among quality staff, managers, and staff nurses, as well as between staff nurses and supervisors. Furthermore, resilience was notably higher in hospital settings (mean = 75.4) compared to primary healthcare settings (mean = 71.2, p = 0.003).

Table 5.

Comparison of resilience levels across demographic variables in hospital and primary health care settings

Variable and categories		Overall				Hospital setting				Primary health setting
		Mean	SD	t/f	Sig.	Mean	SD	t/f	Sig.	Mean	SD	t/f	Sig.
Gender	Male	77.1	10.5	1.17	0.243	77.5	9.6	0.87	0.384	74.5	16.4	0.55	0.583
	Female	74.3	12.8			75.2	12.9			71.1	11.9
Education	BSc	75.1	13.3	1.42	0.236	76.0	12.9	1.68	0.170	69.8	14.1	1.60	0.207
	Diploma	73.3	12.2			73.9	12.8			72.2	11.0
	High Diploma	74.9	10.9			74.9	10.9			0	0
	MSN	78.0	12.5			79.9	11.4			58.5	2.1
Experience	< 2 Yrs	72.4	15.1	1.89	0.110	72.4	15.1	2.58	0.037b	0	0	1.66	0.180
	2–10 Yrs	72.5	11.5			73.1	11.6			66.8	9.6
	11–20 Yrs	74.9	13.1			76.5	13.1			70.6	12.0
	21–30 Yrs	77.8	11.5			80.0	11.1			74.8	11.5
	> 30 Yrs	76.7	13.2			76.0	11.3			77.0	15.1
Position	Director	76.2	12.4	2.78	0.017a	77.5	13.9	3.22	0.007c	71.0		0.07	0.990
	Educator	78.9	15.0			80.8	15.9			70.3	6.0
	Manager	74.8	10.7			75.4	10.7			73.2	11.4
	Quality	86.0	10.8			86.0	10.8			0	0
	Staff	73.7	12.7			74.5	12.6			71.0	12.5
	Supervisor	80.8	11.6			84.3	10.7			71.8	9.2
Setting	Hospital	75.4	12.7	3.01	0.003	-	-	-	-	-	-	-	-
	Primary Health	71.2	12.0			-	-	-	-	-	-	-	-

SD; stand deviation, Sig.; Significant, t/f stat.; t or f statistics

a LSD Quality staff vs. Manager, and vs. Staff, (P = 0.045, P < 0.018), and Staff vs. Supervisor (P = 0.012)

b LSD < 2year vs. 21–30 year (P < 0.033), 2–10 year vs. 11–20 and 21–30 year, (P = 0.021, P = 0.011)

c LSD Staff vs. Quality staff (P = 0.027), and vs. Supervisor (P = 0.003), Manager vs. supervisor (P < 0.033)

In hospital environments, experience was significantly linked to resilience (p = 0.037), with the highest resilience found in individuals with 21–30 years of experience (mean = 80.0). Post-hoc tests showed significant differences between those with less than 2 years of experience and those with 21–30 years (p < 0.033), and between individuals with 2–10 years and those with 11–20 years (p = 0.021) and 21–30 years (p = 0.011). Job position also significantly affected resilience (p = 0.007), with quality staff (mean = 86.0) and supervisors (mean = 84.3) scoring highest, while staff nurses (mean = 74.5) and managers (mean = 75.4) scored lower. Post-hoc analysis revealed significant differences between staff nurses and quality staff (p = 0.027), staff nurses and supervisors (p = 0.003), and managers and supervisors (p < 0.033).

In primary healthcare settings, the findings indicated no significant differences in resilience levels across demographic variables, suggesting that factors such as gender, education, experience, and job position do not substantially influence resilience in these environments.

The F-value of 3.68 (p < 0.05) shows that the model is statistically significant overall, meaning the predictors collectively influence resilience, The table shows that the regression model accounted for 7% of the variance in resilience among nurses (R² = 0.07, p < 0.05). Nurses working in primary care settings exhibited significantly lower resilience than their counterparts in hospital settings (B = -5.05, p < 0.001). In terms of education level, nurses holding a Diploma demonstrated significantly lower resilience compared to those with a Bachelor’s degree (B = -2.98, p = 0.025). Regarding experience, nurses with 11–20 years (B = 5.49, p = 0.046), 21–30 years (B = 9.74, p = 0.003), and those with over 30 years (B = 10.46, p = 0.038) reported significantly higher resilience compared to those with 2 years of experience or less. Conversely, other variables did not show significant results (Table 6).

Table 6.

Multiple regression of predictors of resilience among nurse in both setting (n = 479)

Variable	B	SE B	β	Sig.	R²	F	Adj R²
(Constant)	75.11	3.39		< 0.001	0.07	3.68	0.05
Setting (Ref: Hospital)
Primary Setting	-5.05	1.43	-0.17	< 0.001
Gender (Ref: Male)
Female	-2.88	2.43	-0.05	0.237
Education Level (Ref: BSc)
High Diploma	0.42	3.14	0.01	0.894
MSN	1.81	2.73	0.03	0.509
Diploma	-2.98	1.33	-0.12	0.025
Experience (Ref: ≤ 2 years)
2–10 Years	0.77	2.77	0.03	0.782
11–20 Years	5.49	2.75	0.22	0.046
21–30	9.74	3.28	0.22	0.003
> 30 Years	10.46	5.02	0.11	0.038

Table 7 presents the results of a multiple regression analysis aimed at identifying predictors of resilience among nurses within a hospital setting, accounting for 6% of the variance in resilience scores (R² = 0.06). The overall model demonstrated statistical significance (F = 3.10, p = 0.002). Significant predictors included education level and professional experience. Specifically, nurses holding a diploma exhibited significantly lower resilience scores compared to their counterparts possessing a bachelor’s degree (B = -4.68, p = 0.003). In terms of professional experience, nurses with 11 to 20 years (B = 6.37, p = 0.024) and those with 21 to 30 years of experience (B = 10.47, p = 0.004) demonstrated significantly higher resilience scores in comparison to nurses with two years or fewer of experience. No significant associations were found for other variables in relation to resilience. For nurses in primary health settings, the regression model did not show significant results.

Table 7.

Multiple regression of predictors of resilience among nurses in hospital setting (n = 369)

Variable	B	SE B	β	Sig.	R²	F	Adj R²
(Constant)	74.95	3.53		< 0.001	0.06	3.10	0.04
Gender (Ref: Male)
Female	-2.61	2.65	-0.05	0.326
Education Level (Ref: BSc)
Diploma	-4.68	1.55	-0.18	0.003
High Diploma	0.13	3.18	0.00	0.969
MSN	2.82	2.89	0.05	0.329
Experience (Ref: ≤ 2 years)
2–10 Years	0.79	2.81	0.03	0.777
11–20 Years	6.37	2.82	0.25	0.024
21–30	10.47	3.65	0.21	0.004
> 30 Years	6.78	7.70	0.05	0.379

Discussion

The objective of this study was to evaluate resilience levels and identify predictors among nurses employed in hospital and primary healthcare settings, accounting for both demographic and professional factors. The results indicated that nurses working in hospital settings exhibited higher levels of resilience compared to their counterparts in primary healthcare. Resilience was found to be significantly correlated with years of experience and job position. Specifically, nurses with 21–30 years of experience demonstrated the highest levels of resilience, highlighting distinct differences between early-career and more experienced nurses. Additionally, job position impacted resilience, with quality staff and supervisors displaying greater resilience than staff nurses and managers. A multiple regression analysis further substantiated that primary healthcare nurses possessed significantly lower resilience than hospital nurses, and those holding a diploma exhibited lower resilience than those with a bachelor’s degree.

The findings revealed that hospital nurses exhibited significantly higher resilience than primary healthcare nurses. These results are in line with earlier research that suggested hospitals may offer structured support systems, chances to work together as a team, and chances to grow professionally, all of which can help people be more resilient [21]. In contrast, primary healthcare nurses often face greater resource limitations, patient non-compliance, and reduced peer interaction, which may negatively impact their ability to develop strong coping mechanisms [22].

Primary care nurses in Saudi Arabia face distinctive operational and organizational challenges that contribute to lower resilience levels compared to hospital nurses. One key issue is workforce shortages, which place a heavier burden on available staff and often result in extended work hours and increased patient loads [23]. Unlike hospital settings that offer specialized departmental support and streamlined workflows, primary healthcare centers frequently require nurses to fulfill multiple clinical and administrative responsibilities, including chronic disease management, immunizations, health education, and documentation. These demands can lead to role overload and fatigue, reducing opportunities for recovery and self-care. Additionally, resource limitations, such as limited access to diagnostic tools or timely referrals to specialists, create obstacles in delivering comprehensive care, which can heighten job-related stress. Patient non-compliance, particularly in managing long-term conditions like diabetes or hypertension, further exacerbates professional frustration and emotional strain [24]. Combined, these factors reduce opportunities for support, recognition, and professional growth, ultimately affecting nurses’ psychological resilience and well-being.

Researchers identified a significant relationship between years of experience and resilience, with nurses in the middle age year experience range exhibiting the highest levels of resilience. This supports existing evidence that professional exposure enhances nurses’ emotional stability and adaptive coping strategies [25]. Early-career nurses, particularly those with fewer than 10 years of experience, reported significantly lower resilience, likely due to higher workplace stress, role uncertainty, and limited coping mechanisms [26].

The study also discovered that job position had a significant effect on resilience. Quality staff had the highest level of resilience, followed by supervisors. Staff nurses had the lowest level of resilience, and managers had the highest level of resilience. Higher job positions associated with greater autonomy, leadership responsibilities, and decision-making authority may explain this trend [27]. In contrast, staff nurses may experience greater workload burdens, contributing to increased stress and lower resilience [28].

While nurse managers are often assumed to have higher resilience due to their leadership roles, the findings of this study indicated otherwise. Specifically, resilience scores were highest among quality staff and supervisors, whereas nurse managers reported lower mean resilience levels. One plausible explanation is that nurse managers face substantial administrative burdens, including staff oversight, scheduling, conflict resolution, and compliance with institutional policies. These responsibilities are often carried out in isolation from clinical peer groups, limiting emotional and collegial support. Unlike quality staff or supervisors who may maintain more direct clinical roles and benefit from team-based interactions, managers may experience greater pressure with less day-to-day engagement in supportive care teams [29]. Additionally, the demands of balancing organisational expectations with frontline realities may create role conflicts and emotional fatigue, which can compromise resilience over time [30].

Additionally, educational attainment was a significant predictor of resilience. Nurses with a diploma exhibited lower resilience than those with a bachelor’s degree. This finding is consistent with studies suggesting that higher education equips nurses with advanced problem-solving skills, stress management strategies, and emotional intelligence, all of which contribute to enhanced resilience [31].

Interestingly, demographic factors such as gender did not significantly impact resilience levels. This aligns with research indicating that professional experience and work environment are stronger determinants of resilience than personal attributes [32].

For nurses who work in primary care, there were no significant differences in resilience across demographic variables. This suggests that the stresses that nurses face in primary care settings may be more similar to those they face in hospitals. Unlike hospital nurses, who work in high-pressure, acute-care environments, primary healthcare nurses often manage chronic diseases, community outreach programs, and long-term patient care [33]. The lack of variation in resilience among primary care nurses may indicate that factors such as education and experience play a lesser role in their coping strategies.

This study has several limitations. The sample was mostly female (93.9%), limiting the generalizability of the findings to male nurses. The cross-sectional design restricts causal interpretation of the identified relationships. Self-reported measures may also introduce response bias. Additionally, the regression model explained only 7% of the variance in resilience, indicating that factors like organizational climate, coping strategies, and workload were not captured. Future studies should aim for a more gender-balanced sample, include additional variables, and utilize longitudinal designs to better understand resilience dynamics.

Conclusion

The findings of this study underscore the importance of resilience in nursing practice, particularly in high-stress healthcare environments. Hospital nurses demonstrated higher resilience than those in primary healthcare, likely due to greater institutional support, team collaboration, and structured training programs. Key predictors of resilience included years of experience, job position, and educational attainment, reinforcing the importance of professional development and career growth in fostering resilience.

Given the lower resilience observed in primary healthcare nurses, targeted interventions should be implemented to enhance their ability to cope with workplace stress. These interventions could include resilience training programs, peer mentorship initiatives, and structured professional development workshops. Providing emotional and psychological support systems, particularly for early-career nurses, can help mitigate occupational stress, reduce burnout, and improve overall job satisfaction.

Furthermore, continuing education and leadership training should be prioritized to equip nurses with the necessary skills to manage workplace challenges effectively. Strengthening resilience-building initiatives in both hospital and primary healthcare settings will not only enhance nurses’ well-being but also contribute to better patient care, reduced turnover rates, and improved healthcare outcomes. Future research should explore longitudinal studies to assess how resilience evolves over time and identify effective intervention strategies tailored to different nursing environments.

Abbreviations

CR-RISC

Connor Davidson Resilience Scale

IRB

Institutional Review Board

Author contributions

Mona & Emad Co- Author: Ali, Mustafah, Sajedah, Maryam, Sheeren, Mona: idea creator, proposal developer, write the final manuscript. Ali, create link for data collection. MUSTAFAH, Sajedah Fatimah, Maryam Sheeren, data collector Emad: data analyst. Maryam: proofread the final manuscript. In the approval letter from IRB you will find some names that not listed with above co- authors where they participate in data collection but since the study is not funded by sponsor where the payment will be completed by the authors some they reject to complete the publication process.

Funding

This study received no funding.

Data availability

The datasets generated and analyzed during the course of this study are accessible from the corresponding author upon reasonable request.

Declarations

Ethics approval and consent to participate

Ethical approval for this study was obtained from the Institutional Review Board of the Dammam Health Network, Saudi Arabia (IRB Protocol No. NUR-44). All procedures were conducted in accordance with the Declaration of Helsinki. Informed consent to participate was obtained from all participants prior to data collection.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.