Personal readiness for employment among students with disabilities

Introduction

According to the labor market forecast for Russia presented by the Ministry of Labor and Social Protection of the Russian Federation at the Federation Council in January 2025, the labor force deficit will reach 3,1 million people by 2030. This figure is 0,7 million higher than the December 2024 forecast. Despite government support measures, the employment rate of persons with disabilities remains extremely low at 20,7% according to 2024 Rosstat data. The importance of realizing the talents and resources of people with disabilities and the significance of work for achieving their quality of life have been emphasized in speeches by the President of the Russian Federation. Given the labor resource shortage and the demand for increased employment of persons with disabilities, there is an objective need to study the factors determining their employment and to identify personal barriers and resources that affect their professional realization.

Research in occupational psychology, including concepts of professional self-determination, psychological readiness for work, professional reliability, and stress resistance, has established that successful entry into the labor market is determined not only by professional competencies but also by personal readiness for employment. The concept of personal readiness encompasses diverse characteristics. For instance, researchers have highlighted the ability to manage emotions, acceptance of professional values and meanings, desire for professional self-realization, absence of procrastination and learned helplessness (Mitina, Mitin, 2020), acceptance of professional activity values, readiness for interaction and initiative (Denisova et al., 2023), independence in problem-solving, conscious motivation for work (Guterman, Goryunova, 2023), and self-congruence as an internal disposition of personal autonomy (Sychev, Guryanova, 2025). Data on the interrelationship of personal readiness components for professional activity have been identified separately. For example, awareness of life perspective is directly related to the motivational, cognitive, and emotional components of readiness (Gut et al., 2021). Students' general self-efficacy is directly related to their academic performance (Mussa, 2023). Characteristics of adaptive readiness are predictors of academic adaptation (Shamionov, Sharov, 2025). Evidence suggests that intrinsic motivation for professional education and satisfaction with the profession, which determine the intention to work in one's field, are not related to academic success (Sychev, Guryanova, 2025).

Regarding persons with disabilities, the problem of work readiness is traditionally described through the place of inclusive employment in the socialization and integration of persons with disabilities (Osmuk, 2018), through identification of existing unemployment risks and ways to overcome them (Zabelina, Sergeeva, 2023; Pavlova, Salogub, 2024), and through actualization of the tasks of forming a personal foundation for continuous professionalization of persons with disabilities (Rubtsov et al., 2023). Some studies indicate lowered expectations, negative attitudes, insufficient motivation to work, and inadequate assessment by persons with disabilities of their personal and professional resources (Zhdanova, Mozheikina, 2020). The gap between physical capabilities, personal motivation to work, and labor market demand has also been noted (Bazhin et al., 2018). International studies confirm the presence of objective difficulties for persons with disabilities in employment (Lindsay et al., 2014).

Factors positively influencing the employment of persons with disabilities have been identified: previous successful experience of the employer and the applicant, the applicant's compliance with requirements, their adaptation, and the presence of economic support measures (Litwin et al., 2023); conditions of previous education, experience with remote work, and positive perceptions of the future profession (Park, Bouc, 2018; Grigal et al., 2019; Nowell et al., 2022; Enticott, Dew, 2025). Studies emphasize the need to consider cultural traditions and national context, including the role of the Chinese family in supporting employment of persons with disabilities (Wang et al., 2025), special employment options for persons with disabilities in South Korea (Hwang, Roulstone, 2015), and the connection between unstable employment and low success in labor market integration among German citizens (Stuth, Jahn, 2019). Research has also focused on overcoming discrimination against persons with disabilities in the labor market and establishing the concept of equal rights to work in society (Iwanaga et al., 2023).

Thus, predictors of successful employment include the presence of work motivation, readiness for business relationships, adaptation to the environment and activities, meaningfulness of life position, and overall satisfaction with the situation. Although the problem of employment of persons with disabilities has been partially studied, the question of the specificity and types of personal readiness for employment of students with disabilities remains insufficiently explored. The causes and ways to overcome barriers in the field of employment of persons with disabilities have not been determined, which establishes the relevance of this study.

Taking into account the identified contradictions and the stated relevance, the purpose of the study was determined: to identify the state and types of personal readiness for employment among students with disabilities during their university studies. In the course of the study, we proceeded from the assumption that students with disabilities exhibit distinctive characteristics in the state of personal readiness for employment and variability in the state of its components.

Materials and Methods

Participants. The study was carried out in the 2023–2024 academic year, in the middle of the first semester. Fifty students with disabilities participated: 94% were undergraduate students in their 2nd-4th years and 6% were master’s students; 66% studied full-time and 34% part-time. The mean age of respondents at the time of assessment was 22,6 years (range 19-46). Distribution by nosology of impairment was as follows: 6% – visual impairments; 12% – hearing impairments; 24% – musculoskeletal disorders; 58% – disability associated with general medical conditions. Acknowledging that barriers and resources may differ substantially between nosological groups, the paper presents an aggregated analysis under the umbrella «students with disabilities» because the subgroup sizes were too small for methodologically valid comparative statistics. The sample was 38% male and 62% female. The sample mean grade point average was 4,17. Overall, 89,2% of the total population of students with disabilities enrolled at Cherepovets State University at the time of the study participated; this included all students with disabilities in their 2nd year and above. The sample’s nosological and age composition and the diversity of study programs generally correspond to the overall structure of students with disabilities in Russian universities.

The primary objective of the present study was to identify internal differences and the structure of readiness for employment within the group of students with disabilities; the design did not involve comparisons with non-disabled students. Given the relatively small sample size (N = 50), which was determined by the specific characteristics of the target population, this study should be regarded as a pilot and does not permit generalization to the entire population. Therefore, the results should be considered preliminary and hypothesis-generating. The application of multivariate methods (factor and cluster analysis) in this study is also exploratory. Although factor analysis typically requires a larger sample (commonly at least 5–10 participants per variable), the authors note that, with N = 50, this criterion is only minimally met. Nevertheless, measures of sampling adequacy (KMO = 0,78) and a significant Bartlett’s test (p < 0,001) indicate that factorization is feasible for uncovering latent structure. We emphasize that the stability of the extracted factors and clusters requires further verification on larger samples of students with disabilities from multiple universities; however, the multivariate methods used retain heuristic value by revealing latent relationships and generating hypotheses for subsequent research. Consequently, the present findings should be interpreted as a basis for further empirical testing within larger-scale projects.

Methods. The study procedure involved administering a questionnaire. The first block comprised seven questions on gender, age, nosology of impairment, academic performance, year of study, mode of study (full-time/part-time), and field of study (UGSN). The second block comprised 84 items drawn from standardized instruments: the Subjective Well-Being Inventory (R. M. Shamionov, T. V. Beskova, 2018), the Locus of Control Questionnaire (E. F. Bazhin, E. A. Golynkina, A. M. Etkind, 1993), and the Scale for Assessing Social and Academic Adaptation of Students (T. D. Dubovitskaya, A. V. Krylov, 2010). These instruments allowed a comprehensive assessment of characteristics that determine capacity for integration into the professional community and constitute the core of personal readiness for employment.

For analysis, we selected variables corresponding to the primary subscales of the chosen instruments, including: emotional well-being; ego well-being; existential–activity well-being; social well-being; physical well-being; general situational well-being; internality in achievements; internality in failures; internality in interpersonal relations; internality in health; internality in family; internality in work; overall internality; relationships with instructors; relationships with peers; self-organization; acceptance of university norms; satisfaction with education; acceptance of academic demands; emotional state; and engagement.

Statistical processing was performed using Python (libraries: pandas, scipy, scikit-learn, factor_analyzer) and IBM SPSS. The analysis comprised several interconnected stages: (1) assessment of data distribution using the Shapiro–Wilk test; (2) factor analysis via principal component analysis (PCA) with Varimax rotation; (3) clustering using hierarchical analysis (Ward’s method) and the k-means algorithm; and (4) predictive modeling using logistic regression and decision trees (CART).

Results

The results of testing the suitability of parametric analytical methods using the Shapiro-Wilk criterion indicated that the distributions for all scales of the diagnostic instruments deviated from normality (p < 0,05), which determined the selection of analytical methods applied at subsequent stages of the study.

To identify the latent structure of personal readiness, factor analysis was conducted using principal component analysis (PCA) with Varimax rotation. The Kaiser–Meyer–Olkin measure of sampling adequacy was KMO = 0,81, indicating a high level of sampling adequacy. Bartlett’s test of sphericity was statistically significant (χ² = 845,62, p < 0,001), confirming the suitability of the data for factorization. The analysis yielded three factors that together explained 69,4% of the total variance. Table 1 presents the factor loadings of the variables (loadings > 0,50), reflecting the strength of the associations between the observed variables and the latent factors.

Table 1

Factor structure of personal readiness for employment of students with disabilities at the stage of study at the university

Within the framework of the study, each factor was assigned a descriptive label, and the characteristics of the key variables loading on each factor were identified and interpreted.

F1. Emotional-regulatory resource. This factor includes the following variables: emotional well-being, existential–activity well-being, social well-being, physical well-being, general situational well-being, and ego well-being.

F2. Motivational-volitional potential. This factor is represented by internality in the domains of achievements, failures, interpersonal relationships, health, family, work, and overall locus of control.

F3. Social-academic adaptation. This factor comprises variables related to relationships with instructors and fellow students, self-organization, acceptance of university norms, satisfaction with studies, acceptance of academic requirements, emotional well-being, and engagement.

Based on factor scores, three levels of expression were defined:

1. Low level (factor score < -0,5), indicating a pronounced resource deficit and the need for targeted support measures;
2. Medium level (factor score from -0,5 to +0,5), reflecting a baseline level of the resource sufficient for adaptation under standard conditions;
3. High level (factor score > +0,5), indicating a well-developed and stable resource manifested in behavioral competence and self-regulation.

Individual factor scores were calculated for each participant, which made it possible to assess the level of expression of each factor within the sample.

For F1, the distribution was relatively balanced but raises concern: 26% of students demonstrated a high level of this resource, 46% a medium level, and 28% a low level.

For F2, this factor emerged as the most vulnerable. Only 18% of students showed a high level of motivational–volitional potential, whereas 48% were at a medium level and 34% fell into the risk zone (low level).

For F3, this factor represented a relative strength of the sample. A high level was observed in 26% of students, a medium level in 52%, and a low level in 22%. Overall, more than half of the students demonstrated medium or high levels of social-academic adaptation.

Hierarchical cluster analysis (Ward’s method with Euclidean distance) was first used to determine the optimal number of clusters. Subsequently, the k-means algorithm was applied to refine cluster boundaries and content. Based on scores for the three identified latent factors (F1–F3), k-means cluster analysis yielded three clusters, representing distinct types of personal readiness for employment among students with disabilities (Table 2).

Table 2

Clusters of personal readiness for employment of students with disabilities

Note: н – below average; нн – significantly below average; в – above average; вв – significantly above average.

Based on the obtained data, the distribution of students with disabilities across clusters was determined, and distinct types of personal readiness for employment were described.

Cluster 1. Students with individual risks of reduced employment readiness (32%, n = 16). This cluster is characterized by medium levels across all three factors. A noticeable gap is observed between social–academic adaptation (F3) and motivational–volitional potential (F2). Students in this cluster generally demonstrate emotional stability and adequate social adaptability; however, intrinsic motivation, persistence, and goal-directed behavior are less pronounced.

Cluster 2. Potentially successful students (28%, n = 14). This cluster demonstrates high levels across all identified factors. It includes students with well-developed emotional–regulatory resources and motivational–volitional potential, pronounced social activity, and a high level of adaptation to the academic environment. These characteristics indicate a well-formed personal readiness for employment.

Cluster 3. Students with a combination of limiting factors (40%, n = 20). This cluster is characterized by low levels of expression for most factors, with particularly low scores on motivational–volitional potential (F2) and emotional–regulatory resources (F1).

Discussion

The results of the study confirm that personal readiness for employment among students with disabilities is a multidimensional construct comprising emotional–regulatory, motivational-volitional, and adaptive components. Students with disabilities demonstrate varying levels of deficits and resources across these components of personal readiness for employment.

Factor analysis made it possible to identify three leading factors underlying personal readiness for employment among students with disabilities. The emotional-regulatory factor (F1), which includes indicators of emotional, social, physical, general situational, ego, and existential-activity well-being, was highly expressed in 26% of students with disabilities. This group is characterized by emotional stability and a positive perception of life prospects. At the same time, 28% of students demonstrated low values on this factor, reflecting elevated anxiety and reduced life satisfaction.

The motivational-volitional factor (F2), associated with general internality as well as internality in the domains of achievements, interpersonal relationships, health, family, work, and overcoming failures, was expressed at a high level in only 18% of students. This indicates a pronounced goal orientation and a capacity for self-regulation. However, 34% of respondents showed reduced values on this factor, pointing to difficulties in goal setting and internal mobilization that limit readiness for professional self-determination.

The social and academic adaptation factor (F3), which includes indicators of relationships with instructors and fellow students, self-organization, acceptance of university norms, satisfaction with studies, acceptance of academic requirements, emotional well-being, and engagement, represented a strength for 26% of students. These individuals demonstrated a high degree of involvement in the educational process and confidence in communication. In contrast, low values on this factor were observed in 22% of respondents, indicating difficulties in social integration and academic interaction. Overall, F2 emerged as the most vulnerable factor: more than one-third of students showed insufficient development of goal-setting and self-regulation mechanisms, which may reduce career resilience. At the same time, F3 functioned as a compensatory resource, reflecting opportunities for successful integration into the academic and social environment.

Cluster analysis identified three key clusters: students with individual risks of reduced employment readiness (32%), potentially successful students (28%), and students with a combination of limiting factors (40%). Students in the first cluster, despite a relatively high level of engagement in the academic environment and acceptable indicators on the emotional–regulatory factor (F1), demonstrated low values on the motivational–volitional factor (F2). The substantial proportion of students with a combination of limiting factors (40%) can be explained by the inclusion of a wide range of parameters in the clustering procedure, which naturally revealed a group with multiple deficits reflecting real adaptation difficulties experienced by some students with disabilities. We assume that disability is associated with increased vulnerability across several parameters of personal readiness. At the same time, high grade point average scores were found to compensate for deficiencies in F1 and F2, thereby reducing the likelihood of inclusion in the high-risk cluster (Cluster 3).

Cluster 3 can be considered a “pivotal resource” group, in which well-structured support has the potential to qualitatively change trajectories of professional development. Students in this cluster require assistance in strengthening work motivation, forming a professional identity, and mastering self-presentation skills. The potentially successful group demonstrates high adaptability to the academic and social environment, which is associated with emotional stability, a pronounced sense of meaning, and strong goal orientation. This group represents a strategic talent pool ready for integration into employment and primarily requires support in building long-term career development pathways. In contrast, students with a combination of limiting factors experience significant internal barriers, including insufficient emotional stability, low levels of internality in the work domain, and weakly developed goal-setting mechanisms. This group is at risk of social passivity and avoidance of employment and therefore requires systematic, interdisciplinary support.

Overall, only 28% of students with disabilities demonstrated pronounced personal readiness for employment. This finding is consistent with previous studies pointing to the presence of internal vulnerability within this population. The identified intermediate group (approximately 32%) requires activation of motivational and volitional resources, which are critical for transitioning into the category of employed persons with disabilities. Accordingly, practical recommendations for supporting representatives of different clusters should be differentiated in terms of their primary strategies and tactics. For Cluster 1, the main strategy involves developing professional identity and engagement in career-oriented projects; recommended measures include participation in internships, employer-based mentoring programs, and the development of competency portfolios. For Cluster 2, the main strategy is targeted correction of weaker areas while maintaining overall resource levels; recommended measures include tutoring support for challenging subjects, soft skills training (communication and self-presentation), and psychological support programs. For Cluster 3, the primary strategy is comprehensive, long-term support with an emphasis on basic academic success and social inclusion; recommended measures include individualized educational pathways, continuous tutor or mentor support, adapted employment practices (such as sheltered workplaces and preliminary internships in inclusive organizations), and collaboration with families and academic advisors.

The discussion of the findings indicates that the identified factors expand upon existing research in the fields of inclusive education and employment of persons with disabilities. The importance of emotional stability and motivation—assessed in this study through subscales of internality in achievements and work, as well as emotional and ego well-being-corroborates the conclusions of previous national studies (Zhdanova, Mozheikina, 2020; Zabelina, Sergeeva, 2023; Pavlova, Salogub, 2024) and confirms their key role in preventing social isolation among persons with disabilities. The present study уточняет, that these characteristics function as compensatory resources for overcoming barriers. The position of Tu, Liu, and Díaz (2024), who argue that academic motivation and perceived support in the learning environment are directly related to the well-being and professional expectations of students with disabilities, is further refined here: motivation and emotional stability among students with disabilities operate not only as internal personal resources but also as mechanisms for overcoming stigmatization, existing deficits, and risks of unemployment. Thus, the identified factor structure aligns with general research trends while simultaneously demonstrating a qualitatively distinct pattern of personal resource manifestation among students with disabilities.

The high proportion of students with a combination of limiting factors identified in this study is consistent with evidence for the existence of such groups, although it differs quantitatively from findings reported in thematically similar international studies, where comparable groups typically account for only 15-20% of samples (Lipka et al., 2020). This discrepancy may be explained by several factors, including more pronounced stigmatization of students with disabilities, processes of acceptance and inclusion (togetherness), and the impact of social identity threat on primary professionalization (Lipai, 2023), as well as the relatively early stage of development of individualized educational pathways and mentoring systems (Bazhin, Simonova, Bashmakova, 2018), alongside a labor market that remains insufficiently adapted. Identifying the leading contributors to this difference—whether internal barriers or external factors such as labor market conditions and support systems for persons with disabilities in Russia and internationally-requires further, more detailed analysis and empirical verification.

Conclusions

The study empirically confirmed that the personal readiness of students with disabilities for employment is determined by the state of emotional-regulatory, motivational-volitional, and social-adaptive resources, which critically influence the ability to overcome barriers and build an individual trajectory of professionalization. Students with disabilities demonstrate the most deficient motivational-volitional potential, while the resource of social-academic adaptation remains the most preserved. Although emotional stability and motivation are traditionally considered key factors for successful employment, the findings of this study clarify their role specifically for students with disabilities. Unlike the general population, where these qualities are predominantly associated with individual career orientations, for students with disabilities they perform a compensatory function, enabling them to overcome external barriers such as stigmatization, limited available vacancies, and lack of an inclusive environment. Thus, the identified patterns reflect not general principles but specific mechanisms of professional development for students with disabilities, which constitutes the novelty of the research.

Cluster analysis identified three types of personal readiness for employment: those with individual risks of reduced employment readiness, potentially successful individuals, and those with a combination of limiting factors. The latter cluster is most prevalent among students with disabilities and is characterized by pronounced internal barriers to employment. The study identified predictive risks in the sphere of unemployment among persons with disabilities, associated with the negative impact of deficits in each of the identified types. The resources and deficits of each type should be considered when implementing inclusive higher education practices.

The identified factors expand existing understanding of the personal characteristics of students with disabilities and the structure of their employment readiness. The findings enable the development of evidence-based, individually differentiated trajectories for professionalization and employment facilitation. Thus, the data both confirm known patterns and demonstrate new contextual features, allowing for a deeper examination of the specifics of personal employment readiness among students with disabilities and their correlation with previous research in this field. The results offer a new perspective on the employment readiness of students with disabilities and reveal both universal and specific patterns of their professional development, enabling a transition from general statements to concrete support strategies.

Overall, the presented data confirm the relevance of developing the problem of employment readiness for persons with disabilities and demonstrate the existence of a distinct structure of personal employment readiness among students with disabilities. The study has value as a pilot and exploratory phase, which allowed for the refinement of measurement approaches and identification of directions for further analysis. At the same time, the data establish a justified agenda for the next step: expanding the sample, multi-center design and stratified analysis, as well as validation of model stability (clustering, logistic regression/CART) on independent subsamples. Such a transition will ensure methodologically rigorous, differentiated assessment of personal employment readiness and enable the formulation of practical recommendations for universities and support services.

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