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A resource-oriented perspective on the aging workforce – exploring job resource profiles and their associations with various health indicators

BMC Public Health volume 24, Article number: 2559 (2024) Cite this article

Abstract

Background

Promoting older workers’ health in the context of increasing labor force participation and skill shortages is crucial. Examining job resource profiles offers a promising approach to understanding how to promote and maintain the health of older workers within the workplace. However, it is unclear how different job resources interact within distinct worker subgroups. Thus, this study explores the association between the job resource profiles of distinct subgroups and various health indicators among older workers in Europe.

Methods

Data from 4,079 older workers (age range: 50–60 years, 57% female) from waves 6 and 8 of the Survey of Health, Ageing and Retirement in Europe (SHARE) were analyzed. Latent profile analysis was employed to identify distinct job resource profiles using social support, recognition, job promotion, autonomy, and development opportunities. Associations between these profiles and various health indicators were examined, alongside the sociodemographic and socioeconomic characteristics associated with each profile.

Results

Four distinct job resource profiles emerged: (I) average job resource workers (n = 2170, 53%), (II) high social job resource workers (n = 983, 24%), (III) low job resource workers (n = 538, 13%), and (IV) autonomous decision-making workers (n = 388, 10%). Workers in the (II) high social job resource profile had the highest socioeconomic status and reported the best self-perceived health, lowest depressive symptoms, and fewest limitations and chronic diseases. Conversely, workers in the (III) low job resource profile had the second-lowest socioeconomic status and reported the poorest health outcomes. Surprisingly, older workers with high autonomy (profile IV) had the lowest socioeconomic status and the second worst self-perceived health. This may be because they perceive themselves as autonomous while lacking support and recognition.

Conclusion

There is wide variation in the level and composition of resources available to older workers in the workplace. The most vulnerable subgroups, such as low job resource workers (profile III) and autonomous decision-making workers (profile IV), could benefit from tailored workplace health promotion interventions, such as support from supervisors or peers. Strengthening older workers’ job resources, including social support and recognition, can improve their health and contribute to them remaining in the workforce.

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Background

Older workers are more likely to face health challenges as they age. In the European Union, 38.1% of the working population between the ages of 55 and 64 years contended with long-standing health problems in 2021, while this figure was substantially lower for younger age groups [1]. This phenomenon has become increasingly important in the context of the trend toward longer working lives [2]. Given the substantial amount of time that individuals spend at their workplaces, these environments have significant potential for public health interventions aimed at promoting health [3]. The health promotion of older workers in the workplace is pivotal for their continued activity in the labor force, particularly pertinent for companies to address the shortage of skilled workers [4].

To better understand how health promotion in the workplace may contribute to older workers’ health, there is a need to shed light on the factors that foster workers’ health. Thus far, research has mainly focused on identifying factors contributing to individuals’ illness or, in other words, what makes people sick, neglecting the question of what makes individuals healthy or supports them in dealing with health impairments. Applying a salutogenic perspective, we argue that focusing on job resources might be a promising approach to understanding the role of health among older people in the workplace [5, 6].

Job resources encompass diverse physical, psychological, social, and organizational factors that can help to promote health and support older workers’ decisions to remain active in the workforce [7]. In a systematic review [8], the following job resources were shown to positively affect labor force participation among older workers: development opportunities, job autonomy, recognition, and respect, as well as social and organizational support (e.g., support by supervisors).

However, the use of some of these resources may be hampered by increasing age. Research has shown that the experience of ageism may negatively affect recognition [9, 10], and a lack of access to continuing training may negatively impact development opportunities and job promotion [11]. At the same time, some resources, such as autonomy, tend to increase with age [12]. Thus, the role of job resources for health may be different for older workers than for younger workers [13].

Furthermore, job resources do not function in isolation [14]; rather, they tend to interact [15,16,17,18,19] and often occur in patterns [20,21,22]. However, to date, there is still a lack of understanding regarding the underlying mechanisms that reflect the interplay between different resources [20]. On the one hand, current literature suggests that resources tend to aggregate, especially within the same domain, or fail to aggregate [20,21,22,23]. On the other hand, some evidence indicates that resource interactions vary in degree, potentially leading to synergistic or compensatory effects between specific resources [24,25,26,27]. For instance, Habe and Tement (2016) found a synergistic effect of high autonomy and increased job variety on workflow, surpassing the anticipated impact based solely on the individual contribution of each job resource [25]. Simultaneously, individuals may possess higher levels of some resources while having lower levels of others. For example, a lack of personal resources might be compensated through social support [27].

Previous research in the field of job resources mainly focuses on job-related outcomes, such as work engagement, work motivation, work commitment, and productivity (e.g., absenteeism) [28, 29], neglecting health outcomes. Brauchli et al. (2015) therefore extended the well-established Job Demands–Resources model [30,31,32] to include mechanisms for the development of health and established the Job Demands–Resources–Health model [33]. These mechanisms consist of salutogenic as well as pathogenetic pathways, linking job demands and resources to both positive and negative biopsychosocial health outcomes [33, 34]. In our study, we further examine the salutogenic pathway [5, 35], i.e., the mechanisms explaining how older workers` health can be maintained or strengthened at the workplace [5]. According to the model by Brauchli et al. (2015), job resources play a crucial role in this salutogenic pathway [33].

Current research primarily focuses on the average overall effect of job resources on health (e.g., [24]) however, this approach risks overlooking subgroups within the population of older workers [34] who exhibit specific combinations of job resources (profiles). Thus, a suitable theoretical-methodological approach is required to identify subgroups. By applying a person-oriented approach [36,37,38], we can focus on subgroups and identify distinct profiles of job resources among older workers. Furthermore, this approach also enhances our understanding of how different job resources interact (e.g. synergistic or compensatory effect) and their mechanism to impact various health indicators.

Although the person-oriented approach is gaining increased attention in research on the work context [39,40,41], few studies have used the approach to examine job resources, particularly among older workers. Recent studies by Friedrich et al. (2023) and Xu et al. (2022, 2022, 2023) have identified job resource profiles for the entire workforce [15,16,17,18]. Only one study by Xu et al. (2023) specifically examined job resource profiles among older workers aged between 40 and 65 years old, but without focusing on age-specific job resources for this target group [17]. The authors instead used job resources such as a culture of collaboration or leadership quality that are relevant for the entire workforce. All four studies suggest that there is usually a subgroup of workers with either overall low or high levels of job resources [15,16,17,18]. However, because these studies did not focus on age-specific job resources for older workers, their findings are not directly transferable, and future research is needed.

Research on the association between job resource profiles and health indicators is even more limited and has primarily focused on single health indicators. In a first study by Xu et al. (2022), they found four different profiles, including an “adverse” profile with overall low resources, as well as three more “beneficial” profiles associated with a low risk of type 2 diabetes, particularly among workers aged 55 years or older [16]. In a second study by Xu et al. (2022), profiles by high to intermediated resources were associated with a lower risk of cardiovascular diseases [16]. Similarly, in a third study, they found that profiles with higher resources were associated with a lower risk of sleep disturbances compared to the low resource profile [17]. In summary, profiles with overall high job resources seem beneficial in reducing the risk of specific chronic diseases.

However, not all chronic diseases impact an individual’s employment situation to the same degree, which is why using various health indicators is recommended to better understand the differential effects of job resources on older workers’ health [34]. For instance, people with type 2 diabetes may not be limited in their ability to work. However, if a chronic condition (e.g., arthritis) goes along with poor self-perceived health or limitations with activities, labor force participation may be impaired. In addition, depressive symptoms are importantly related to labor force participation and are therefore an essential measure to include [42,43,44]. Research has shown that particularly job autonomy is associated with lower levels of depressive symptoms [12, 42, 43]. To capture the multidimensional aspects of health, we follow the model of health used by Boehme et al. (2014) including indicators of physical, functional, and self-perceived health, as well as depressive symptoms to examine the relationship between these multidimensional aspects of health and job resources [45].

This study aims to investigate how job resource profiles in distinct subgroups of older workers are associated with various health indicators. Compared to existing research, our study offers several novel contributions. First, we apply a resource-oriented perspective on the aging workforce, focusing on the salutogenic pathway of positive health development in the work context [5]. Second, we use a person-oriented approach to identify job resource profiles in distinct subgroups of older workers providing a deeper understanding of how job resources interact. This knowledge will help to tailor workplace health promotion interventions that positively influence older workers’ health. To our knowledge, no previous study has used age-specific job resources to identify such profiles among older workers. Finally, unlike previous research that focused on single diseases (e.g., diabetes or cardiovascular diseases [15, 16]), we investigate differential associations between job resource profiles and various health indicators.

Considering these aspects, the first aim of the study was to examine the job resource profiles that exist in older workers. In line with the most relevant job resources identified for older workers in the literature review mentioned above [8], we include the following five age-specific job resources: social support, recognition, job promotion, job autonomy, and development opportunities.

  • H1a: Based on previous research focusing on job resource profiles, we assume that one profile with overall high job resources exists (see [15,16,17,18]).

  • H1b: As we focus on older workers, where the resource “autonomy” tends to occur particularly frequently, we expect to identify a profile with a high level of job autonomy (see [12, 18]).

Furthermore, to better characterize the distinct profiles, sociodemographic characteristics, such as gender and age, as well as socioeconomic characteristics, such as educational level and occupation status, were reported for each profile.

Second, this study aimed to investigate the job resource profiles and their associations with the health indicators of self-perceived health, limitations with activities, depressive symptoms, and number of chronic diseases.

  • H2a: Based on previous research focusing on job resource profiles, we assume that older workers in a profile with overall high job resources experience better outcomes in all health indicators (see [15,16,17]).

  • H2b: We assume that older workers in a profile with high levels of autonomy have fewer depressive symptoms (see [5, 12, 33, 42, 43]).

Since empirical evidence on job resource profiles is scarce, we rely on other empirical findings [12, 42, 46] and the theoretical assumptions of the Job Demands–Resources–Health model [5, 33], which suggest associations between job autonomy and depressive symptoms.

Methods

Design and participants

For data analysis, we used public data from two measurement time points (wave 6: 2015; wave 8: 2019) from the Survey of Health, Ageing and Retirement in Europe (SHARE) [47, 48]. This dataset includes information from the following European countries: Austria, Belgium, Switzerland, Germany, Denmark, Spain, France, Greece, Italy, the Netherlands, Sweden, Israel, the Czech Republic, Poland, Estonia, Hungary, Slovenia, Luxembourg, and Croatia. SHARE is a cross-national longitudinal panel survey that examines the effects of health, social, economic, and environmental policies on European citizens aged 50 years and older [49]. The survey data is assessed using computer-assisted personal interviewing (CAPI) and the data collection is centrally coordinated by the Munich Center for the Economics of Aging [50]. The survey has been approved by the Ethics Council of the Max Planck Society, along with numerous other ethics committees in the participating countries, ensuring adherence to ethical standards.

Applying a four-year longitudinal research design, older workers between 50 and 60 years at the first measurement point (t1) in 2015, with a least one value in the job resource variables at t1, were included, comprising 8,103 individuals. Furthermore, workers were included, if they had four years later (t2), in 2019, at least a value in one of the health variables, yielding a final sample of 4,079 individuals (Meanage = 56.6, SDage = 2.4; female = 57%, male = 43%).

Dropout-analysis showed small age differences (t(8101) = 4.763, p < .001, d = 0.106; 95% CI [0.062, 0.149]): Workers in the final sample were slightly older (Meanage = 56.6, SDage = 2.4) compared to those workers who dropped out of the sample. However, no differences were found between genders ((χ2 (1, N = 4,079) = 0.5671, p > .05). Regarding the relevant job resources, no or only small non-significant differences between the preliminary and the final sample were found (d ranging from − 0.019 to 0.099).

Measures

To build the job resource profiles, we used the following five indicators: (1) social support: “I receive adequate support in difficult situations,” (2) recognition: “I receive the recognition I deserve for my work,” (3) job promotion: “My job promotion prospects/ prospects for job advancement are poor,” (4) autonomy: “Little freedom to decide how I do my work in (main) job,” and (5) development opportunities: “Opportunity to develop new skills in (main) job.” All indicators were assessed with a 4-point Likert scale ranging from 1 = strongly disagree to 4 = strongly agree. Indicators 3 and 4 were recoded reverse to how they were assessed to improve the interpretation of the results. All five items were derived from two established questionnaires based on the Demand-Control Model and the Effort-Reward Imbalance Model (see Siegrist et al., 2007 [51]).

As outcome measures, we used the following four health indicators: (1) self-perceived health on a Likert scale ranging from 1 = excellent to 5 = poor, (2) number of chronic diseases (sum of chronic diseases based on a list of different chronic diseases), (3) depressive symptoms (percentage of individuals with symptoms), and (4) limitation with activities on a Likert scale (1 = severely limited; 2 = limited, but not severely; and 3 = not limited).

Statistical analyses

For statistical analyses, longitudinal data from wave 6 (t1) and wave 8 (t2) of SHARE were used. To identify latent job resource profiles (RQ1), a latent profile analysis with a series of one to six profile solutions was conducted using data from wave 6 of SHARE [52], using the following statistical indicators: loglikelihood value, Bayesian information criterion (BIC), Vuong-Lo-Mendell-Rubin likelihood ratio test (VLMR), and bootstrap likelihood ratio test (BLRT) [52,53,54]. In addition, z-values for the job resources of each profile were calculated to better interpret the profiles. To validate the profiles found at wave 6, we repeated the analysis with the job resources at wave 8 and calculated the average squared Euclidian distance to test the structural stability of the profiles over time.

Wald-Tests [55, 56] were used to investigate the associations between job resource profiles and different health indicators (wave 8) (RQ2). For all analyses, Mplus Version 8.8 with full information maximum likelihood procedures was applied [57]. Missing values in all variables were < 5%.

Results

Descriptive statistics

Considering the statistical indicators (see Table 1) and applying the elbow criterion [54], BIC shows the lowest value in the 4-profile solution. Similarly, loglikelihood has the highest values in the 4-profile solution, and VLMR and BLRT also pointed to the 4-profile solution. Moreover, analyses for the 5- and 6-profile solutions yielded estimation problems, indicating that the 5- and 6-profile solutions are too complex. Given the results for these statistical indicators, a 4-profile solution seems to be the best option. In accordance, the 4-profile solution demonstrated high structural stability across waves 6 and 8 regarding the average squared Euclidean distance (0.00–0.16; see also S1 for the results of the profiles at wave 8). Considering content-related criteria, the 2- and 3-profile solutions showed only level profiles, meaning that all resources are either low, medium, or high in one profile [58,59,60]. In summary, according to both statistical and content-related criteria, a 4-profile solution seems to be most promising (see Table 1).

Table 1 Overview of fit indices for 1- to 6-profile solutions (class-invariant, unrestricted)
Full size table

Characteristics of profiles

The first profile (see Fig. 1 and Table 2) is labeled as (I) average job resource workers (n = 2,170, 53%) due to an average level of job resources. In this profile, workers have an average age of 56.5 years, and 58% are female. Furthermore, regarding education level and occupation, this profile has an average distribution in both socioeconomic characteristics.

Fig. 1
figure 1

Identified job resource profiles in older workers at wave 6

Full size image
Table 2 Job resource profiles, including socioeconomic characteristics and their association with different health aspects
Full size table

The second profile is labeled as (II) high social job resource workers (n = 983, 24%) due to an overall high level of all job resources, especially in social support and recognition. Workers in this profile have an average age of 56.45 years (ageSE = 0.12) and have the highest percentage of women (63%) compared to all other profiles (ranging from 54 to 58%). This profile has the highest level of individuals with tertiary education (41% tertiary education) compared to all other profiles (ranging from 23 to 35%). In addition, this profile has the largest number of upper non-manual workers (36%) compared to all other profiles (ranging from 16 to 25%).

The third profile, labeled as (III) low job resource workers (n = 538, 13%), has the lowest level of job resources. In this profile, workers have an average age of 56.7 years, and 54% are female. Regarding education, this profile also has an average distribution. However, regarding occupation, the profile is characterized by the second-highest rate of manual workers but also the second-highest rate of upper-non-manual workers.

The fourth profile, labeled (IV) autonomous decision-making workers (n = 388, 10%; agemean = 57.05, ageSE = 0.13), is characterized by high autonomy and low social support. The highly autonomous decision-making workers are slightly older (agemean = 57.05) compared to the workers in all other profiles (agemean ranging from 56.45 to 56.69 years) and have an average percentage of women (57%). This profile has the highest level of individuals with only primary or secondary education (23%) compared to the other profiles (ranging from 10 to 20%), and the highest number of manual workers (32%) compared to the other profiles (ranging from 15 to 29%).

Association of job resource profiles and health indicators

Regarding the association of job resources and various health indicators, the (II) high social job resource workers have the best self-perceived health, the lowest percentage of depressive symptoms, the lowest number of chronic diseases, and the lowest percentage of severe limitations with activities. In contrast, the (III) low job resource workers have the worst self-perceived health, the highest percentage of depressive symptoms, the highest number of chronic diseases, and the highest percentage of severe limitations with activities.

Additionally, the (I) average job resource workers have, compared to the other profiles, an average self-perceived health, an average percentage of individuals with depressive symptoms, an average number of chronic diseases, and an average level of limitations with activities. (IV) Autonomous decision-making workers have the second worst self-perceived health, the second highest percentage of workers with depressive symptoms, and the second highest number of chronic diseases and limitations with activities.

Discussion

Adopting a resource-oriented perspective, we examined the job resource profiles. We have identified four different profiles, which shed light on the heterogeneous job situations of older workers. By far, the most frequent (53%) profile was (I) average job resource workers. The second most frequent (24%) profile was (II) high social job resource workers, which is characterized by more-than-average levels in all job resources and particularly high levels of social support. Finding this profile supports hypothesis H1a, according to which there is a profile with overall high levels of job resources. Evidence suggests that resources aggregated, especially within the same domain, and come in “caravans” [14, 20,21,22,23], which might explain the co-occurrence of social support and other job resources in the same profile. The third most frequent (13%) profile was (III) low job resource workers, which is characterized by less-than-average levels in all job resources, with particularly low levels of social support. In accordance with current research, this subgroup might have more difficulties in aggregating resources and is more vulnerable to resource loss [20, 21, 23]. The least frequent (10%) profile was (IV) autonomous decision-making workers with low levels of social support but high levels of autonomy, job promotion, and development opportunities. Finding this profile partially supports hypothesis H1b, according to which there is an existence of a profile with high levels of autonomy. Identifying profile (IV) underlines the importance of the person-oriented approach as a theoretical-methodological framework that focuses on subgroups [36, 39, 40]; it shows that some older workers—although only a minority—simultaneously have a high level in some job resources and low levels in others. This finding provides a valuable contribution to the ongoing research discussion on the interaction between job resources [20,21,22,23], demonstrating that not only an overall low or high level of job resources exists. The use of a latent profile analysis thus provides us with a more detailed understanding of the job resources interaction among older workers that might not have been discovered without a focus on subgroups.

In accordance with our hypothesis (H2a), we found the best values for all health indicators among workers in profile (II) high social job resource workers. This finding nuances previous research which has established social support as a crucial resource for health within the realm of work [61]. Research has shown that social support can convey esteem [62] and foster appreciation, which in turn might enhance recognition [63]. Therefore, it can be inferred that these two job resources are closely interconnected and may mutually reinforce each other.

We do not find support for H2b, which states that older workers in a profile with high levels of autonomy have fewer depressive symptoms. In contrast, individuals in profile (IV) autonomous decision-making workers have the second highest level of depressive symptoms of all profiles. This finding can be better understood if we consider that these workers experience a very low level of social support and a low level of recognition despite their high degree of autonomy. Since profile (IV) is also characterized by a high share of individuals with the lowest level of education and a high share of individuals in manual occupations, it may include individuals working in low-skilled occupations that are comparable to self-employment, with a large level of task discretion and low supervision, such as cleaning workers or janitors. These workers may have greater autonomy to decide when and how they work and may therefore consider themselves autonomous. At the same time, they work alone and lack both support and recognition for their work [64]. Our finding differs from previous research showing that low autonomy is generally associated with worse health outcomes, including higher levels of depressive symptoms [65]. We show that workers can have poor health despite high levels of autonomy—if they simultaneously experience little social support and recognition. It may even be precisely this combination of high autonomy, which can be experienced as a burden [66], and low social support and recognition, which are detrimental to health. Another explanation for our finding may be that the burden of high autonomy may be exacerbated among workers with low socioeconomic status.

We further highlight the relationship between profile (III) low job resource workers and health. In profile (III), 27% of workers indicated suffering from depressive symptoms, and they have the worst self-perceived health of all profiles. This finding is in accordance with a large body of literature showing that a low level of job resources is associated with worse health outcomes [15,16,17, 65]. Moreover, workers in profile (III) have the second-lowest socioeconomic status, which could also be a supplementary explanation for their poor health status [65, 67, 68].

Overall, our study innovates in two respects. First, by identifying workers’ complex job resource profiles instead of using single measures of job profiles, with a specific focus on age-specific job resources. Second, we innovate by examining the relationship between these profiles and various health outcomes. Following Boehme et al. (2014), we include not only measures for self-perceived, physical, and functional health but also depressive symptoms as they are particularly relevant to the work context [45].

Our findings contribute, on the one hand, to the literature on the health and working conditions of older workers in general, and on the other hand, provide a better understanding of the differential effects of working conditions for distinct subgroups of older workers. While older workers generally may have more job resources than younger workers (e.g., a larger network providing social support or more occupational experience providing recognition), they may also be more vulnerable in terms of health. For instance, if older workers experience health problems, these problems are more likely to develop into chronic conditions than in younger workers [69]. Promoting job resources for older workers thus holds promise to hinder the onset of chronic conditions and an early exit from the labor force [70, 71].

Practical implications

Tailoring workplace health promotion interventions [31] based on job resource profiles, particularly for the most vulnerable subgroups of older individuals – such as the low job resource workers and autonomous decision-making workers - may enhance intervention effectiveness. The low job resource workers may benefit from interventions that broadly target job resources. Studies have shown that workplace health promotion interventions addressing job resources at various levels - organizational, individual, and team - are most effective, highlighting the importance of a multi-level approach [31, 72,73,74]. Moreover, supervisors have a strong impact on older workers’ individual job resources and a positive effect on their health outcomes [72, 73, 75]. For example, supervisors could have a direct positive influence on workers’ job resources by providing social support and autonomy, as well as opportunities for individual growth and job promotion. For autonomous decision-making workers, improving social job resources (e.g., social support and recognition) is essential. However, assuming a high proportion of self-employed or independent workers in the group of autonomous decision-making workers, targeting social job resources becomes challenging due to a lack of traditional workplace structures, such as a supervisor. Creating supportive social networks for these workers could be achieved, for instance, through peer support programs, as existing evidence indicates positive outcomes for health and well-being from peer-to-peer approaches within workplace settings [76, 77]. Finally, beyond focusing on individual job resources, it is crucial to examine how the labor market and social policies may contribute to the health of older workers and their working conditions [78,79,80].

Strengths, limitations, and future research

Our study exhibits several noteworthy strengths. First, it follows a paradigmatic shift, transitioning from an exploration of mechanisms leading to older workers’ illness to an investigation of salutogenic mechanisms. Emphasizing job resources offers an innovative approach, fostering both the overall health of workers and supporting those with existing health problems to remain in the labor force. Second, to the best of our knowledge, this is the first study to focus on the association of job resources on various health indicators rather than solely on individual diseases. Third, other strengths are the high-quality standards in data collection, the large sample size, and the representativeness of the data, which enhance the generalizability of our results.

Despite the strengths identified in our study, several limitations warrant consideration, pointing to new avenues for future research. First, previous research suggests that job resources and health mutually influence each other in a dynamic manner [81], potentially giving rise to feedback loops over time, which are commonly referred to as gain and loss spirals [17, 21]. More longitudinal studies over longer periods are needed to disentangle the intertwined mechanisms of job resources and health. Therefore, advanced longitudinal methods for the analysis of causality mechanisms should be applied (for an overview of methods for causal inferences, see [82], and for an in-depth discussion of causality in observational data, see [83]). Second, each job resource was evaluated using a single-item question. Consequently, it is not feasible to make statements about the internal consistency of these constructs. Nevertheless, the comprehensive and multidisciplinary nature of the SHARE with a broad scope of topics, coupled with length restrictions, precluded the possibility of assessing each resource with multiple items (see also [46, 51]); thus, items with the best psychometric properties were selected. Finally, in this study, we focus on older workers’ job resources. However, personal and family resources are increasingly important predictors of the overall health and well-being of employees in their work environments. Future research should include additional information on personal and/or family resources, as well as financial resources, to better understand the interconnection of resources in various life domains [31].

Conclusion

Our study contributes to research by taking a resource-oriented perspective on older workers. By focusing on the job resource profiles of the aging workforce, we examined factors that contribute to maintaining good health or dealing with health impairments. Our findings indicate that the high social job resource workers have the best self-perceived health, the lowest percentage of depressive symptoms, and the lowest number of chronic diseases. In contrast, the low job resource workers and the autonomous decision-making workers have the worst health indicators and could benefit the most from tailored workplace health promotion interventions, particularly those aimed at enhancing social job resources. Tailoring interventions to different job resource profiles may significantly enhance their effectiveness. Ultimately, fostering favorable job resources can positively impact the health of older workers, enabling them to continue working despite growing health challenges. This might be of particular importance for companies in times of skill shortages and countries grappling with heightened financial pressures on old-age pension systems.

Data availability

The data analyzed are free and publicly available after registration at the SHARE Research Data Center: https://releases.sharedataportal.eu/login?redirect=/users/login.

Abbreviations

BIC:

Bayesian information criterion

BLRT:

Bootstrap likelihood ratio test

SHARE:

Survey of Health, Ageing and Retirement in Europe

VLMR:

Vuong-Lo-Mendell-Rubin likelihood ratio test

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Acknowledgements

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Funding

This study was funded by the Swiss National Science Foundation (SNSF) grant number 179696.

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  1. Institute of Public Health, ZHAW Zurich University of Applied Sciences, Winterthur, Switzerland

    Vanessa Gut, Sonja Feer & Isabel Baumann

  2. Faculty of Health Sciences and Medicine, University of Lucerne, Alpenquai 4, Lucerne, 6005, Switzerland

    Vanessa Gut

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VG and SF: conceptualization; VG, SF, and IB: writing and revision; VG: data analysis; VG, SF, and IB: interpretation of data; IB: acquisition. All authors revised the manuscript critically for important intellectual content and read and approved the final manuscript.

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Gut, V., Feer, S. & Baumann, I. A resource-oriented perspective on the aging workforce – exploring job resource profiles and their associations with various health indicators. BMC Public Health 24, 2559 (2024). https://doi.org/10.1186/s12889-024-20098-4

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