Understanding resilience among workers in elderly care: insights from a mixed-methods study

Abstract

Background

Workers in elderly care face demanding conditions that may affect both staff well-being and quality of care. Resilience has been identified as a protective factor in this context; however, evidence on its predictors in elderly care settings remains limited. This study investigated the personal-level and organizational-level predictors of individual resilience among healthcare workers employed in residential facilities for older adults and people with disabilities.

Methods

As part of the European Horizon 2020 project Support4Resilience, a cross-sectional mixed-methods study was conducted among healthcare professionals working in residential care facilities in Italy. Quantitative data were collected from 231 workers through an online questionnaire including validated measures of individual resilience, mental well-being, burnout, organizational resilience capacity, intention to leave, and self-rated health. Pearson correlations and multivariate linear regression analyses were performed, controlling for sociodemographic characteristics. To contextualize and expand the quantitative findings, four focus groups involving 16 healthcare workers and leaders were conducted. Qualitative data explored experiences related to workload, teamwork, professional responsibility, and relational aspects of care. Findings were integrated using a joint display mixed-methods approach.

Results

In multivariate analyses, mental well-being emerged as the strongest predictor of individual resilience. Organizational resilience capacity was also positively associated with individual resilience. Burnout, intention to leave, and self-rated health were not significantly associated with resilience. Qualitative findings converged with and enriched the quantitative results across four main themes. Relational meaning in care emerged as a key source of psychological reward and professional fulfilment, consistent with the strong association between mental well-being and resilience. Teamwork and coordination illustrated how organizational resilience is enacted through collaborative practices and structured communication. Workload and fatigue highlighted the structural pressures and moral burden associated with staffing shortages. Finally, a strong sense of professional responsibility toward residents, including emotional self-regulation and collective commitment, appeared to sustain engagement despite challenging working conditions.

Conclusions

Interventions aimed at strengthening teamwork, coordination, and organizational support, while promoting mental well-being, may help sustain a resilient workforce and support high-quality care for older adults and people with disabilities.

Supplementary Information

The online version contains supplementary material available at https://doi.org/10.1186/s12913-026-15184-9.

Background

Elderly care constitutes one of the most demanding sectors within contemporary healthcare systems [1]. Professionals working in residential and long-term care facilities for older adults are routinely exposed to high physical workload, intense emotional labor, staff shortages, and prolonged contact with frailty, chronic illness, and death [2]. These structural characteristics place elderly care workers at elevated risk of psychological burden, burnout, and workforce attrition, with substantial implications for both staff well-being and continuity of care [3–5].

Health systems research has emphasized that caring for frail older adults requires adaptive coordination, teamwork, and organizational flexibility, particularly in contexts of multimorbidity and complex needs [6]. When such adaptive capacities are insufficient, strain at the workforce level may compromise not only staff outcomes but also patient safety and quality of care.

The present study investigates the individual and organizational predictors of healthcare workers’ resilience in residential elderly care facilities. Using a mixed-methods design combining quantitative survey data and qualitative insights from focus groups, the study examines how mental well-being, burnout, organizational resilience capacity, turnover intention, and self-assessed health are associated with individual resilience among professionals working with frail older adults.

A growing body of research has documented the cumulative burden experienced by healthcare workers, linking chronic job stress to poorer mental health, higher burnout, and increased intention to leave the profession [7, 8]. In elderly care settings, these risks are often amplified by limited organizational resources and reduced opportunities for recovery, making individual and organizational protective factors particularly salient [5, 9].

Qualitative investigations in diverse healthcare contexts further illustrate how professionals navigate emotionally demanding environments through coping strategies that are often constrained by systemic conditions [10, 11]. Evidence from resilience-building interventions also suggests that psychosocial support and relational dynamics at work play a crucial role in buffering stress and sustaining functioning under pressure [12]. However, these studies also highlight that focus exclusively on individual coping may be insufficient if structural and organizational conditions remain unaddressed [1, 11].

Within this context, individual resilience has been increasingly conceptualized as a key adaptive resource [13]. Resilience refers to the capacity to maintain or regain psychological functioning in the face of adversity and sustained stress [14, 15]. Among healthcare professionals, higher levels of individual resilience have generally been associated with better mental health outcomes, lower burnout, and enhanced capacity to cope with demanding work environments [16, 17].

Empirical evidence from nursing and broader healthcare populations suggests that resilience is closely linked to mental well-being and general psychological functioning, acting both as a protective factor against distress and as a resource for sustaining engagement in care work [17, 18]. At the same time, emerging research underscores that resilience should not be conceptualized solely as an individual trait but rather as a dynamic process influenced by relational and contextual factors [19, 20].

Importantly, resilience does not operate in isolation from the organizational context. Research in elderly care and healthcare more broadly indicates that organizational conditions can contribute to both organizational and individual level resilience. For example, organizational resilience capacity, can be shaped by the organizational conditions such as supportive workplace climate, and adequate structural resources. These in turn contribute to individual resilience and translate into well-being and work-related outcomes [5, 21, 22]. Organizational conditions may also either enable or constrain individual workers’ ability to cope effectively, influencing levels of burnout, turnover intention, and perceived health.

The resilient healthcare perspective conceptualizes resilience as a multilevel phenomenon encompassing individuals, teams, and systems, emphasizing capacities for anticipation, adaptation, and learning under varying conditions [19, 23]. Studies focusing on patient safety and organizational adaptation further demonstrate that resilience is strengthened when communication, leadership, and collaborative practices are embedded within daily work processes [24, 25].

These findings suggest that individual resilience may be partially contingent upon perceived organizational resilience capacity, particularly in complex care environments such as elderly care.

Mental well-being represents a central outcome within this framework, reflecting positive psychological functioning rather than merely the absence of mental illness [26]. Recent work has highlighted the conceptual distinctiveness of mental well-being from depressive symptomatology, supporting its relevance as an independent indicator of workforce health in healthcare settings [27, 28].

In high-demand healthcare contexts, mental well-being may function both as an outcome of supportive organizational conditions and as a resource facilitating adaptive responses to stress, thereby interacting closely with individual resilience.

Burnout, turnover intention, and perceived health represent additional dimensions of workforce sustainability. Persistent burnout has been associated with reduced job satisfaction, compromised quality of care, and workforce instability [4, 7]. Intention to leave constitutes a critical indicator of system-level vulnerability, particularly in sectors already affected by staffing shortages.

Together, existing evidence suggests that individual resilience, organizational resilience capacity, mental well-being, burnout, turnover intention, and self-assessed health are closely interconnected in shaping healthcare workers’ health and their capacity to deliver care to frail older patients. However, empirical research simultaneously examining these factors within elderly care settings remains limited, particularly when integrating quantitative and qualitative approaches.

This study examines which individual-level factors are associated with healthcare workers’ resilience in elderly care settings, while accounting for organizational resilience capacity and sociodemographic characteristics. Specifically, we investigate whether resilience is primarily linked to individual psychological resources—such as mental well-being, burnout, turnover intention, and self-assessed health—or whether it remains contingent upon organizational conditions. To address this question, we adopt a mixed-methods design combining quantitative analyses with qualitative insights from focus groups.

Methods

In this study we employ a mixed-methods approach involving the collection and subsequent analysis of quantitative and qualitative data, collected in parallel, in order to leverage the complementary strengths of each method and assess consistency between the two datasets [29]. The study was approved by the Joint Ethical Committee For Research (Scuola Superiore Sant’anna – Pisa and Scuola Normale Superiore), with Resolution No. 32/2024.

The study was conducted within a non-profit organization providing healthcare and social-health services to vulnerable populations in Italy. The organization supports approximately 500 service users and employs around 400 professionals, including physicians, psychologists, nurses, physiotherapists, psychiatric rehabilitation technicians, occupational therapists, educators, speech therapists, social workers, healthcare and social care assistants, and administrative staff. The organization operates within the public healthcare and social welfare system and is largely funded through public resources.

Services are provided to individuals with complex care needs, including older adults, people with physical and/or mental disabilities, individuals in vegetative states, and persons affected by Alzheimer’s disease and other forms of dementia.

As part of the broader European Horizon 2020 project Support4Resilience (S4R), a cross-sectional survey was conducted between November 2024 and February 2025. The survey was administered to all healthcare professionals working in the organization. Participation was voluntary. All participants provided written informed consent prior to taking part in the study. The study protocol was reviewed and approved by the competent ethics committee.

Participants to the quantitative study completed an online self-administered questionnaire that included demographic information and a battery of validated self-report instruments assessing individual resilience, mental wellbeing, burnout, intention to leave the job or profession, perceived organizational resilience, and self-rated health.

Individual resilience was measured using the 10-item version of the Connor–Davidson Resilience Scale (CD-RISC-10; [14, 30], which assesses the ability to cope with adversity, adapt to change, and recover from stress. Items are rated on a 5-point Likert scale ranging from 1 (not true at all) to 5 (true nearly all of the time), with higher scores indicating greater resilience.

Mental well-being was assessed using the Short Warwick–Edinburgh Mental Wellbeing Scale (SWEMWBS; [31, 32], a 7-item scale measuring subjective well-being over the previous two weeks, encompassing both hedonic and eudaimonic dimensions. Items are rated on a 5-point scale ranging from 1 (all of the time) to 5 (none of the time) and were reverse-coded so that higher scores indicate higher levels of mental well-being.

Perceived organizational resilience was measured using the Resilience Capacity Scale (RCS), a 33-item instrument assessing collective organizational capacities that support adaptation and effective responses to challenges. The scale covers multiple domains including teamwork and cooperation, adaptability and innovation, risk management, leadership, communication, learning environment, knowledge sharing, inter-unit collaboration, and organizational clarity. Responses are rated on a 5-point scale ranging from 1 (strongly disagree) to 5 (strongly agree), with higher scores reflecting greater perceived organizational resilience.

Burnout was measured using the work-related burnout subscale of the Copenhagen Burnout Inventory (CBI; [33, 34], which assesses emotional and physical exhaustion related to work demands. Items are rated on a 5-point scale ranging from 1 (never) to 5 (always), with higher scores indicating higher levels of burnout.

Intention to leave was measured using four items from the Michigan Organizational Assessment Questionnaire – Intention to Turnover scale (MOAQ; [35]. Responses are provided on a 7-point Likert scale ranging from 1 (strongly disagree) to 7 (strongly agree), with higher scores indicating stronger turnover intentions.

Finally, general health status was assessed using a single-item self-rated health measure (Self-Assessed Health; [36], asking respondents to rate their overall health from 1 (poor) to 5 (excellent). Single-item self-rated health measures have been shown to be robust predictors of both physical and mental health outcomes. Additional file 1 displays the full survey.

At the same time, in November 2024, we conducted four focus groups with 16 workers working in three of the seven residential care facilities for older adults (Table 1). Interviews were conducted without the presence of patients [37], using semi-structured interview guide (Additional file 2) and lasted around one hour on average. All interviews were audio recorded and transcribed verbatim.

Table 1.

Focus group participants (n = 16) breakdown

Participant ID	Focus Group	Facility	Interview Date	Professional Role	Modality
W1–P1	W1	Facility 1	05 Nov 2024	Nurse	In person
W1–P2	W1	Facility 1	05 Nov 2024	Nursing assistant	In person
W1–P3	W1	Facility 1	05 Nov 2024	Nursing assistant	In person
W1–P4	W1	Facility 1	05 Nov 2024	Nursing assistant	In person
W1–P5	W1	Facility 1	05 Nov 2024	Administrative staff	In person
W2–P1	W2	Facility 2	19 Nov 2024	Nurse	Online
W2–P2	W2	Facility 2	19 Nov 2024	Nurse	Online
W2–P3	W2	Facility 2	19 Nov 2024	Nursing assistant	Online
W3–P1	W3	Facility 2	19 Nov 2024	Nursing assistant	Online
W3–P2	W3	Facility 2	19 Nov 2024	Nursing assistant	Online
W3–P3	W3	Facility 2	19 Nov 2024	Nursing assistant	Online
W3–P4	W3	Facility 2	19 Nov 2024	Nursing assistant	Online
W4–P1	W4	Facility 3	21 Nov 2024	Administrative staff	In person
W4–P2	W4	Facility 3	21 Nov 2024	Nursing assistant	In person
W4–P3	W4	Facility 3	21 Nov 2024	Nursing assistant	In person
W4–P4	W4	Facility 3	21 Nov 2024	Occupational therapist	In person

Quantitative data were analyzed using Stata. The dataset consisted of 231 healthcare workers employed in residential care facilities for older adults. Composite scores were computed for each scale following the original scoring procedures. All SWEMWBS items were reverse-coded so that higher scores indicated higher levels of mental well-being. Sum scores were calculated for individual resilience (CD-RISC-10), mental wellbeing (SWEMWBS), and burnout (CBI), whereas mean scores were computed for intention to turnover (MOAQ) and perceived organizational resilience (RCS). To facilitate interpretation and comparability of effects, all continuous variables included in regression analyses were standardized (z-scores) prior to analysis. Age was entered as a continuous variable, while gender and education were treated as categorical variables.

Interviews from focus groups were analysed using Nvivo 12 software and following the principles of content analysis [38]. In this process we adopted an abductive approach which allowed us to combine deductive and inductive reasoning. We coded the interviews in two levels, first order codes generated were close to the language of our informants and these were then grouped into themes that gave the codes a more theoretical meaning. In line with the adopted content analysis approach, each focus group transcript was first coded independently to retain site-specific nuances. The resulting codes were then systematically compared and synthesized across groups to develop higher-order themes. The mixed-methods analysis was conducted by first analyzing the qualitative and quantitative data separately and then integrating the results. The combined findings were presented in an integrated comparative framework (Table 2) highlighting areas of agreement and complementarity between the two datasets since no disagreement were found [39, 40].

Table 2.

Sample characteristics

Gender (n = 228)		n	%
Female		193	84.65%
Male		35	15.35%
Education (n = 228)
Primary school		48	21.05%
Secondary school		79	34.65%
Less than three years of higher education		38	16.67%
Bachelor		37	16.23%
Master’s degree		26	11.40%
Experience (n = 229)
Less than 1 year		10	4.37%
1–5 years		45	19.65%
6–10 years		31	13.54%
11–15 years		26	11.35%
16–20 years		35	15.28%
21 years or more		82	35.81%
Age (n = 209)
mean		48.23
std. dev.		11.38

Note. Descriptive variables were optional in the questionnaire

Results

Quantitative findings

The sample of respondents to the quantitative online survey was predominantly female (Table 2). As descriptive items in the survey were optional, the number of responses varies slightly across variables. Among the 228 respondents who reported gender, 193 participants were women (84.65%) and 35 were men (15.35%). Regarding educational attainment (n = 228), 21.05% of participants had completed primary school (n = 48), 34.65% had a secondary school qualification (n = 79), and 16.67% reported less than three years of higher education (n = 38). A total of 16.23% held a bachelor’s degree (n = 37), while 11.40% had completed a master’s degree (n = 26).

With respect to work experience in the sector (n = 229), most participants reported substantial professional tenure. Specifically, 35.81% had 21 years of experience or more (n = 82), followed by 15.28% with 16–20 years (n = 35), 13.54% with 6–10 years (n = 31), and 11.35% with 11–15 years of experience (n = 26). Shorter tenure was less common, with 19.65% reporting 1–5 years of experience (n = 45) and only 4.37% reporting less than one year (n = 10).

Age information was available for 209 participants. The mean age of the sample was 48.23 years (SD = 11.38), with ages ranging from 21 to 64 years, indicating a predominantly middle-aged workforce.

Table 3 presents descriptive statistics and internal consistency coefficients for the study measures. Individual resilience (CD-RISC) had a mean of 37.21 (SD = 6.26), with good internal consistency (α = 0.86). Mental well-being (SWEMWBS) showed a mean score of 15.68 (SD = 4.29) and good reliability (α = 0.81). Organizational resilience capacity (RCS) showed a mean of 3.66 (SD = 0.65) and excellent reliability (α = 0.97).

Table 3.

Scales’ characteristics

Scale	Item	n	Range	Mean	SD	α
CD-RISC – Individual Resilience	10	231	22–50	37.21	6.26	0.86
SWEMWBS – Mental Wellbeing	7	231	14–35	15.68	4.29	0.81
RCS – Organizational Resilience	33	231	1.15-5	3.66	0.65	0.97
CBI - Burnout	7	231	9–32	19.69	4.99	0.92
MOAQ – Intention to Turnover	4	231	1–7	2.37	1.44	0.92
SAH – General Health	1	229	1–5	2.89	0.92	-

Note: CD-RISC = Connor–Davidson Resilience Scale; SWEMWBS = Short Warwick–Edinburgh Mental Wellbeing Scale RCS = Resilience Capacity Scale; CBI = Copenhagen Burnout Inventory; MOAQ = Michigan Organizational Assessment; SAH = Self-Assessed Health

Burnout (CBI) had a mean score of 19.69 (SD = 4.99) and excellent internal consistency (α = 0.92). Intention to turnover (MOAQ) showed a relatively low mean (M = 2.37, SD = 1.44) and high reliability (α = 0.92). General health (SAH) had a mean score of 2.89 (SD = 0.92); reliability was not applicable as it was measured with a single item.

Overall, all multi-item scales demonstrated adequate to excellent internal consistency.

Table 4 reports Pearson correlations among the study variables. Individual resilience (CD-RISC) was positively associated with organizational resilience capacity (RCS; r = .48, p < .001) and negatively associated with burnout (CBI; r = − .33, p < .001) and intention to turnover (MOAQ; r = − .16, p < .05). Organizational resilience capacity was also negatively correlated with burnout (r = − .52, p < .001) and turnover intention (r = − .47, p < .001).

Table 4.

Correlations of model variables

1. CD-RISC – Individual Resilience	1	2	3	4	5	6
	-
2. SWEMWBS – Mental Wellbeing	0.65***	-
3. RCS – Organizational Resilience	0.48***	0.61***	-
4. CBI - Burnout	− 0.33***	− 0.53***	− 0.52***	-
5. MOAQ – Intention to Turnover	− 0.16*	− 0.39***	− 0.47***	0.61***	-
6. SAH – General Health	0.04	0.11	0.12	− 0.27***	− 0.12	-

Note: *p<.05, **p<.01, ***p<.001. CD-RISC = Connor–Davidson Resilience Scale; SWEMWBS = Short Warwick–Edinburgh Mental Wellbeing Scale RCS = Resilience Capacity Scale; CBI = Copenhagen Burnout Inventory; MOAQ = Michigan Organizational Assessment; SAH = Self-Assessed Health

Mental well-being (SWEMWBS) was positively associated with individual resilience (r = .65, p < .001) and organizational resilience capacity (r = .61, p < .001) and negatively associated with burnout (r = − .53, p < .001) and intention to turnover (r = − .39, p < .001). Burnout showed a strong positive association with intention to turnover (r = .61, p < .001) and a negative association with self-assessed health (SAH; r = − .27, p < .001).

No significant correlations were observed between self-assessed health and individual resilience or mental well-being.

Table 5 presents standardized regression coefficients (β) from the multivariate linear regression model predicting individual resilience. The model accounted for 50% of the variance in resilience scores (R² = 0.50).

Table 5.

Standardized regression coefficients for individual resilience (CD-RISC) scores

Factors	Coefficient	Std. err.
SWEMWBS – Mental Health	0.61***	0.07
CBI - Burnout	0.02	0.08
MOAQ – Intention to Turnover	0.13	0.07
SAH – General Health	− 0.03	0.06
RCS – Organizational Resilience	0.16*	0.08
Controls
Age	0.01	0.01
Gender
Female	-	-
Male	0.16	0.15
Education
Primary school	-	-
Secondary school	0.36*	0.15
Less than three years of higher education	− 0.08	0.18
Bachelor	0.00	0.19
Master’s degree	− 0.03	0.20
Constant	− 0.38	0.31
R-square	0.50

Note: n = 205; *p<.05, ***p<.001. CD-RISC = Connor–Davidson Resilience Scale; SWEMWBS = Short Warwick–Edinburgh Mental Wellbeing Scale RCS = Resilience Capacity Scale; CBI = Copenhagen Burnout Inventory; MOAQ = Michigan Organizational Assessment; SAH = Self-Assessed Health

Mental well-being showed a statistically significant positive association with individual resilience (β = 0.61, p < .001), indicating that a one standard deviation increase in mental well-being was associated with a 0.61 standard deviation increase in resilience, holding all other variables constant. Organizational resilience capacity was also positively associated with individual resilience (β = 0.16, p < .05), corresponding to a 0.16 standard deviation increase in resilience for each standard deviation increase in perceived organizational resilience.

Burnout (β = 0.02, p > .05), turnover intention (β = 0.13, p > .05), and self-assessed health (β = −0.03, p > .05) were not statistically significant predictors in the adjusted model, suggesting that their associations with resilience were not independent of the other variables included in the model.

Among control variables, age was not significantly associated with resilience (β = 0.01, p > .05). No significant differences emerged between male and female participants. Compared with primary education (reference category), having completed secondary education was positively associated with resilience (β = 0.36, p < .05), whereas no statistically significant differences were observed for higher educational levels.

Qualitative findings

The analysis of qualitative data generated four themes: Relational meaning in care; teamwork and coordination; workload and fatigue and sense of professional responsibility.

Relational meaning in care was conceptualized as the emotional reciprocity generated through daily contact with residents, and it was perceived by participants as a central source of psychological reward and professional fulfilment. Across the focus groups, participants described how small gestures of recognition from residents, a smile, a greeting, an expression of concern, counterbalanced the demands of the role and reinforced commitment to the work. One participant described how this reciprocity sustained her through the demands of daily care:

Everything you give comes back to you in their smiles and in their ‘well done’ […] this repays you a lot, because our work is tiring, it is not simple. But then when you start your shift and everyone greets you: ‘Ah, you’re back!‘, in the end it is a beautiful thing. (W3)

The relational dimension was also described as functionally important for the quality of care itself. Participants noted that trust built through sustained relationship enabled more effective and personalized assistance. As one participant explained:

The human relationship is very important, because when trust develops and a good relationship is established, it also becomes possible to provide good care, to understand each other, to understand what they want, and also to make them understand where we can get to and what our possibilities are. (W2)

Several participants reflected on how the intensity of relational care also carried an emotional cost, particularly in end-of-life care. Feelings of helplessness and attachment were described as inseparable from the human dimension that gave the work its meaning. A participant working in a residential facility described this tension:

There is also the emotional stress, the management of end of life, when you see them starting to get worse, losing their last remaining abilities, you feel a little powerless. You feel like you are doing everything you can to make them feel as well as possible. But at a certain point their condition advances and you realize you cannot do more than that. After working in such close contact with them you also become a little attached, and then it hurts. (W4)

Teamwork and coordination emerged as a set of informal practices and structured communication mechanisms through which participants translated individual care responsibilities into collective problem-solving. Participants across all focus groups described care as fundamentally team-based. One participant described this role:

It really is teamwork, because we are the eyes, we see the residents every day and notice things. If we see that something is not right, our duty is to inform the nurse on duty and collaborate with them. (W3)

Structured coordination mechanisms, including regular briefings, shared digital documentation, were described as catalysts for concrete outcomes. Participants in one focus group described a quality improvement project in which the team collectively identified pressure ulcer prevention as a priority and, through collaborative effort over six to seven months, reduced the number of affected residents from seven to three. One participant observed:

Collaboration is our real strength. We work together to reach objectives and in the past we have succeeded, precisely because there is so much collaboration. Decisions are always taken together, not individually. (W1)

In another facility, participants described ward briefings held every fifteen days as a key forum in which all professional roles gathered to discuss emerging issues and develop shared responses. As one participant explained:

We hold ward briefings every fifteen days, all the professional roles take part and we try to solve issues together. If working better makes them feel better too, then that is what matters. (W4)

Workload and fatigue was conceptualized as the cumulative physical, psychological and moral burden experienced by workers as a consequence of care demands, insufficient staffing, and the structural gap between the quality of care they wished to provide and what conditions permitted. Participants across all focus groups described schedules characterized by tight deadlines, high resident-to-staff ratios and demanding tasks. One participant described the overall toll:

You deal with people, with fragile people, and you deal with the suffering of people, daily. It is truly emotionally, psychologically, mentally, physically stressful. Of all the jobs I have done, this is perhaps the most stressful I have ever done. (W1)

A recurring concern across groups was not simply the volume of work but the cost of time pressure. Participants frequently described the frustration of being unable to spend adequate time with residents who needed human contact. One participant illustrated this:

The work gets done, obviously you cannot leave someone without washing them or changing their pad. But unfortunately to do this you cannot talk to them, you cannot spend a little more time. You have to be as quick as possible. And you notice that in that moment they might need to talk, to tell you something, and you actually don’t have the time. This is a real challenge, because you feel bad, you would want to stay with them, to listen to them. (W4)

This tension between structural constraints and person-centered care was articulated most explicitly by participants who described time-based work plans as fundamentally incompatible with the human pace of care. One participant stated:

We are not working with machines, we are working with people, so I have to go along with their needs in every way. A real improvement in care would be to be able to work with calm, to give the time that their care requires. Otherwise, we become machines. The human relationship is not an industrial matter; it must always be maintained. (W3)

Sense of professional responsibility was conceptualized as a deeply internalized moral commitment to the residents in care workers’ charge, experienced not as an externally imposed obligation but as a core professional value. Despite the pressures described above, participants across all focus groups articulated this commitment consistently. One participant described the imperative to prioritize service continuity even at personal cost:

At a certain point, you have to sacrifice yourself a little to guarantee a continuous service. (W2)

Workers also reflected on the collective dimension of this responsibility, describing the need to maintain team as a duty toward vulnerable residents. One participant explained:

We have to try in every possible way to stay cohesive as a team, because we are dealing with people who have serious difficulties. We have to be very careful about what we do, in the eyes of others too. We need to hand over properly, to signal problems, because we are talking about people who have real needs. (W3)

The professional discipline required to maintain limits, to care deeply while avoiding responses that could be unsustainable, and this was framed not as emotional detachment but as responsibility toward the group as a whole. As one participant put it:

We cannot put ourselves on the same level as the residents, otherwise we would need twenty staff members per shift. We have to try in every way to be cohesive among ourselves, to hand over properly, to flag issues. (W3)

Workers also described the need to regulate their own emotional states before entering the ward, recognizing that personal stress transmitted directly to residents. This self-awareness was framed as an expression of professional responsibility. As one worker stated:

If someone comes to work nervous, the nervousness, like with children, with older people it is the same thing, gets transmitted directly, instantaneously. So we also need to think about our own lives, otherwise it unfortunately has an impact on the work too. And in our work you cannot let that happen. (W3)

Mixed-methods findings

Qualitative findings are presented in conjunction with the quantitative results through a joint display to facilitate integrated interpretation. Table 6 presents the integration between quantitative predictors of individual resilience and the main qualitative themes that emerged from the analysis. Following established mixed-methods integration frameworks, four distinct patterns of integration were identified: convergence, complementarity, expansion, and explanatory complementarity.

Table 6.

Joint display integrating quantitative and qualitative findings

Quantitative finding	Qualitative evidence (theme and quotes)	Integrated interpretation
Mental well-being positively predicts resilience (β = 0.61, p < .001)	Relational meaning in care. W3: “In the end everything you give comes back to you in their smiles and in their ‘well done’.” W3: “When you start your shift and everyone greets you— ‘Ah, you’re back!’—in the end it’s really nice.” W2: “The human relationship is very important… because when trust develops and a good relationship is established, it also becomes possible to provide good care.”	Convergence. The statistical association between well-being and resilience aligns with qualitative accounts emphasizing emotional reciprocity, meaningful relationships, and fulfillment of image motivation derived from care interactions.
Organizational resilience capacity positively predicts resilience (β = 0.16, p < .05)	Teamwork and coordination. W3: “It really is teamwork, because we are the eyes… we see the residents every day and notice things.” W4: “We hold ward briefings every fifteen days… all the professional roles take part, and we try to solve issues together.” W4: “There is a system that everyone can access… we can write anything about the resident and then look it up later.”	Complementarity. Organizational resilience is enacted through collaboration, shared monitoring, structured briefings, and collective documentation practices, complementing its smaller but significant quantitative contribution.
Burnout not significantly associated with resilience (CBI, p = .352, ns)	Workload and fatigue. W2: “We often work double shifts and come back to work again, and that definitely increases stress.” W3: “By the end of the day we’re completely exhausted.” W4: “During COVID many of our colleagues were at home sick… so we had to work double nights and split shifts.”	Expansion. Qualitative findings elaborate the lived experience of workload, staffing shortages, and accumulated fatigue captured by the burnout construct, expanding on what the non-significant quantitative effect may not fully explain.
	Sense of professional responsibility. W2: “Sometimes you have to sacrifice yourself a bit to guarantee continuous service.” W3: “We have to try in every possible way to stay cohesive as a team… because we’re dealing with people who have serious difficulties.” W3: “We can’t put ourselves on the same level as the residents… otherwise we would need twenty staff members per shift.”	Explanatory Complementarity. Despite strain and fatigue, participants emphasize moral duty, boundary-setting, and collective responsibility. This endurance-oriented professional stance may help explain why burnout was not statistically associated with resilience.

Note: Quotes were translated from Italian by the authors

Mental well-being emerged as the strongest quantitative predictor of individual resilience (β = 0.61, p < .001). Qualitative findings converged with this result, highlighting the relational and emotional dimension of care work as a central source of psychological reward. Participants frequently described how interactions with residents generated emotional reciprocity, recognition, and a sense of meaning in their work.

For example, one participant explained how emotional feedback from residents represented a form of personal reward, noting that “everything you give comes back to you in their smiles and their ‘well done’.” Another participant emphasized that trustful relationships with residents enable better care and strengthen the human dimension of the work. These accounts align with the strong statistical association observed between mental well-being and resilience.

Organizational resilience capacity was also positively associated with individual resilience in the quantitative model (β = 0.16, p < .05). Qualitative findings complemented this result by illustrating how resilience is enacted through collaborative work practices and coordination among staff.

Participants described elderly care as fundamentally team-based, involving shared monitoring of residents’ conditions and continuous communication among different professional roles. Organizational routines such as regular ward briefings and shared documentation systems facilitated collective problem solving and information exchange. These findings provide contextual insight into how organizational resilience capacity operates in everyday care practices.

Burnout did not emerge as a statistically significant predictor of resilience in the multivariate model. However, qualitative findings expanded the understanding of this construct by documenting the lived experience of workload and fatigue among healthcare workers.

Participants frequently referred to double shifts, staff shortages, and physically demanding schedules that contributed to accumulated stress and exhaustion. Some workers also recalled particularly challenging periods, such as the COVID-19 pandemic, when staff absences and emergency conditions intensified work demands. These qualitative accounts deepen the interpretation of the burnout construct by illustrating the concrete working conditions that shape workers’ experiences in elderly care settings.

A further theme emerging from the qualitative analysis concerned a strong sense of professional responsibility toward residents. Participants emphasized moral commitment, collective responsibility, and the importance of maintaining professional boundaries when working with vulnerable individuals.

Several participants described the need to “sacrifice” or remain cohesive as a team in order to guarantee continuity of care. This theme provides an explanatory complement to the quantitative findings, suggesting that a strong professional ethos and sense of duty may sustain workers’ functioning despite experiences of fatigue and strain. Such a commitment may help explain why burnout did not show a statistically significant association with resilience in the multivariate model.

The mixed-methods findings indicate that individual resilience among elderly care workers emerges from the interaction between psychological resources and organizational practices. Mental well-being appears closely linked to the relational meaning derived from care interactions, while organizational resilience is expressed through collaborative routines and teamwork structures. At the same time, qualitative findings highlight persistent workload pressures and a strong sense of professional responsibility that shape how workers navigate demanding care environments.

Discussion

This study examined the individual and organizational factors associated with resilience among healthcare workers in elderly care settings, integrating quantitative and qualitative findings through a mixed-methods approach. The results indicate that mental well-being emerged as the strongest predictor of individual resilience, while organizational resilience capacity also showed a significant positive association. Burnout, turnover intention, and self-assessed health were not independently associated with resilience in the multivariate model. The qualitative findings provided additional insights across four themes: relational meaning in care, teamwork and coordination, workload and fatigue, and sense of professional responsibility, collectively illuminating the relational, organizational, and moral dimensions through which resilience is sustained in everyday care work.

The strong association between mental well-being and resilience is consistent with previous research showing that psychological well-being functions both as a protective factor and as a resource that enables healthcare workers to cope with demanding environments [16, 17]. The qualitative findings help contextualize this relationship by illustrating how relational interactions with residents constitute an important source of emotional reward and professional meaning. Participants frequently described how recognition, trust, and emotional reciprocity in everyday care interactions reinforced their motivation and psychological engagement.

These findings resonate with research emphasizing the relational nature of care work, particularly in elderly and disability care settings where long-term relationships with residents are central to professional identity and job satisfaction [5]. In this sense, resilience appears not only as an individual psychological resource but also as a capacity nurtured through meaningful interpersonal interactions.

The positive association between organizational resilience capacity and individual resilience suggests that organizational conditions play an important role in shaping how resilience is expressed in practice. Qualitative findings illustrated that resilience is often enacted through teamwork, communication, and collaborative routines such as ward briefings and shared documentation systems.

These findings align with the resilient healthcare perspective, which conceptualizes resilience as a multilevel phenomenon emerging from interactions between individuals, teams, and organizational systems [9, 19, 23]. In elderly care contexts characterized by high complexity and workload, supportive organizational practices may therefore enable workers to translate individual coping resources into sustained functioning and adaptive responses to challenges [22].

Interestingly, burnout did not emerge as a significant predictor of resilience in the quantitative model. The qualitative findings help contextualize this result by integrating two complementary dynamics. On one hand, participants described substantial workload pressures, including staff shortages, double shifts, and physically demanding schedules. These accounts reflect the structural challenges widely documented in long-term care environments [2, 4, 11].

On the other hand, workers also emphasized a strong sense of professional responsibility toward residents, often framed in terms of commitment, duty, and teamwork. This moral dimension of care may partially buffer the impact of strain and contribute to maintaining functional engagement despite fatigue. Such professional commitment has been widely observed among healthcare workers and may sustain engagement in care even under challenging working conditions [10, 12].

These findings suggest that resilience in elderly care may involve not only psychological coping resources, but also professional norms and collective responsibility embedded in care practices.

The workload theme points to something that burnout scores do not fully capture. Participants were not simply exhausted — they felt the cost of not being able to give residents the time and attention they deserved. At the same time, the strong sense of professional responsibility they described may have worked as a buffer: caring deeply about residents, and feeling accountable to them, can itself be a source of resilience, even under pressure.

These findings support the idea that resilience among healthcare workers should not be understood solely as an individual trait or coping strategy. Rather, resilience appears to emerge from the interaction between individual psychological resources, relational aspects of care work, and supportive organizational practices [13].

From a practical perspective, interventions aimed at strengthening workforce resilience in elderly care should therefore operate at multiple levels [1]. Alongside initiatives promoting individual well-being and coping skills, healthcare organizations may benefit from strengthening team coordination, communication structures, and collaborative practices that support collective resilience [24, 25]. Such organizational approaches may be particularly relevant in long-term care settings, where the complexity of residents’ needs requires sustained coordination among professionals.

However, several limitations should be acknowledged. First, the cross-sectional design limits the possibility of drawing causal inferences regarding the relationships observed between resilience and the examined predictors, thereby constraining the internal validity of the findings. In addition, the regression models were based on the variables included in the survey; the inclusion of additional individual or organizational factors may have produced different associations with individual resilience. The study also relied on self-reported measures, which may introduce reporting or common method bias despite the use of validated instruments.

Second, data were collected within residential care facilities located in a specific geographical and organizational context, which may limit the external validity and generalizability of the results to other healthcare systems or care settings.

With regard to the qualitative component, findings were derived from a limited number of focus groups and therefore reflect the perspectives of participating workers rather than the full diversity of experiences across the sector. Although the qualitative data provided contextualized insights that complemented the quantitative results, the relatively small sample size may limit the transferability of the findings to other elderly care contexts.

Future research could further explore the multilevel nature of resilience in healthcare by examining how individual, team, and organizational factors interact over time. Longitudinal studies may help clarify causal pathways between well-being, organizational conditions, and resilience. In addition, further qualitative and mixed-methods studies may deepen the understanding of how professional values, relational dynamics, and workplace structures contribute to sustaining resilience in demanding care environments.

Conclusion

In conclusion, this study provides evidence that resilience among healthcare workers in elderly care settings is influenced by both individual psychological resources and organizational conditions. Mental well-being and organizational resilience capacity emerged as key correlates of individual resilience, while qualitative findings highlighted how relational reciprocity with residents, teamwork practices, a strong sense of professional responsibility, and the structural pressures of workload affect the experience of resilience in everyday care work. Strengthening resilience in elderly care therefore requires approaches that address both individual and organizational dimensions of workforce well-being.

Supplementary Information

Below is the link to the electronic supplementary material.

Author contributions

All Authors contributed equally. The author(s) read and approved the final manuscript.

Funding

Support4Resilience - Strengthening resilience and mental wellbeing through the Support4Resilience toolbox for leaders in elderly care (GA: 101136291 - CUP: J53C24000360006). This research was funded by the European Union (Call reference: HORIZON-HLTH-2023-CARE-04). This research is funded by the European Union, grant agreement number: 101136291. Views and opinions expressed are however those of the authors only and do not necessarily reflect those of the European Union. Neither the European Union nor the granting authority can be held responsible for them.

Data availability

The datasets generated and/or analyzed during the current study are not publicly available due to the Consortium Agreement.

Declarations

Ethics approval and consent to participate

The study was approved by the Joint Ethical Committee for Research of Scuola Superiore Sant’Anna, Pisa, and Scuola Normale Superiore (Resolution No. 32/2024, September 30, 2024), following the Request for Opinion under Protocol No. 22/2024, as part of the Support4Resilience – Strengthening Resilience and Mental Wellbeing through the Support4Resilience Toolbox for Leaders in Elderly Care project (Grant Agreement No. 101136291; CUP J53C24000360006). No additional ethical requirements beyond those established by the Committee were applicable. All participants received written information describing the purpose, procedures, potential risks and benefits of the study, and provided informed consent prior to participation by selecting the dedicated consent option in the online survey and/or signing a written consent form before participating in focus groups. Participation was entirely voluntary, and participants were informed that they could decline to participate or withdraw from the study at any time without providing a reason and without any consequences. Interviews were audio-recorded only after explicit consent had been obtained. Audio recordings were transcribed, anonymized prior to analysis, and all personal data were processed in accordance with the General Data Protection Regulation (EU) 2016/679.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.