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Article

Social Diversity in Focus: Assessing the Impact of Socioeconomic Backgrounds and Work Experience on Psychological Well-Being and Academic Confidence Among German First-Year Medical Students

1
Center for Medical Education, Ruhr University Bochum, 44801 Bochum, Germany
2
Department of Anatomy and Molecular Embryology, Institute of Anatomy, Medical Faculty, Ruhr University Bochum, 44801 Bochum, Germany
*
Author to whom correspondence should be addressed.
Educ. Sci. 2024, 14(11), 1173; https://doi.org/10.3390/educsci14111173
Submission received: 2 September 2024 / Revised: 24 October 2024 / Accepted: 25 October 2024 / Published: 28 October 2024
(This article belongs to the Section Education and Psychology)

Abstract

:
This investigation pioneers an examination of the scarcely explored terrain of social diversity in medical education, assessing the complex impacts of socioeconomic status (SES), prior working experiences, and completed apprenticeships on a spectrum of psychological and academic facets among first-year medical students (n = 336) in an urban university setting. By utilizing a systematic and detailed approach, the study illuminates prior anatomical knowledge and various psychological constructs, marking a significant stride into a domain where knowledge remains profoundly limited. Analyses indicated that SES significantly affects financial anxiety (F(4, 331) = 17.391, p < 0.001) and academic behavioral confidence (F(4, 331) = 4.323, p = 0.002). Students with prior working experience reported higher self-perceived clinical experience but lower A-level grades (t(279.459) = −6.690, p < 0.001), competence in natural sciences (t(321.045) = −3.178, p = 0.002), and online competence (t(319.429) = −2.026, p = 0.044). Those who completed an apprenticeship showed higher resilience, academic confidence, self-efficacy, and greater concerns about balancing studies and work (t values ranging from 2.020 to 3.158, p < 0.05). Correlation analysis revealed a positive relationship between resilience (RQS) and coping with academic stress (CAS) (r = 0.632, p < 0.001), as well as between resilience and academic behavioral confidence (ABC) (r = 0.608, p < 0.001). Negative correlations were observed between resilience, coping with academic stress, academic confidence, and cognitive test anxiety (CTA) (r values from −0.235 to −0.404, p < 0.001). The findings emphasize the need for tailored support for students from diverse SES backgrounds and with varying experiences. The study highlights the value of a comprehensive approach in medical education, considering the diverse backgrounds and experiences of students. Future research should explore the long-term impact of these factors on professional competencies and patient care, leveraging the diversity of the student body for a holistic educational experience.

1. Introduction

Medical education is a key component of the healthcare system, as it shapes the next generation of healthcare professionals. While medical education aims at producing well-trained physicians who provide high-quality care to a heterogeneous group of patients, research suggests that the medical school student population often lacks social diversity, possibly leading to a deficiency in multiplicity in the respective physician workforce as well as the distribution to a broader “socioeconomic spectrum” [1,2,3].
It has been suggested that it is essential to bridge the gap between the social backgrounds of doctors and patients, as it affects not only the quality of healthcare provided but also the ability of physicians to properly understand and effectively address the healthcare needs of diverse patient populations [4,5]. Moreover, social diversity in medical education is claimed to be a factor contributing “to the learning, well-being, and effectiveness” [6] of medical students.
While the definition of social diversity encompasses a variety of factors such as ethnicity, religious beliefs, language, geographical origin, gender, and sexual orientation (Oxford University Press, 2024), the socioeconomic background of students is a further component of social diversity influencing the overall academic outcome and dropout rates of higher education students [7,8]. Socioeconomic diversity itself involves factors such as income, parental education level, and financial resources.
With human medicine being a highly demanded university course, matching in a German medical program is an exceedingly competitive process [9].
After graduating from German high school, students receive an advanced high school diploma (Abitur), including a grade point average (A-level grade) based on their performance in 11th and 12th grade of school and their final school examinations. Achieving a high A-level grade is the main criterion for successful application and matching in one of the medical programs in Germany, and in the past, 20% of all spots were given to those applicants with the highest A-level grade [9]. Moreover, 60% of all applicants were distributed via the internal selection process of medical faculties mostly based on the A-level grade but also taking other criteria into account, such as medical school-specific tests, successful completion of an apprenticeship in a medical field (e.g., nursing), military service or volunteer work [9]. In the past, the final 20% of applicants were admitted by a waiting list, meaning that those who had waited the longest since high school graduation without being enrolled in another university course had the best chance of receiving a spot in medical school [9]. In December 2017, the German Federal Constitutional Court stated that essential aspects of these proceedings were unconstitutional (BVerfG, 2017). Thereupon, beginning in the summer semester of 2020, the student selection criteria for matching in one of the German medical programs were reformed (Bundestag, 2019) [10]. The reformation included, among other changes, an increase in the applicant group solely reliant on the A-level grade from 20% to 30% and the new introduction of the additional aptitude 10% quota, an A-level grade independent quota replacing the former waiting list [10]. This additional aptitude quota can, once again, include more mature students who have completed an apprenticeship or have some other kind of experience in the medical field, such as nurses, medical technical assistants, or paramedics.
Therefore, one can roughly divide the German student population into two cohorts: The group of mostly younger students who effectively apply for medical school right after graduating high school with a sufficiently good A-level grade and/or ability test scores, and the group of more mature students whose applications were successful due to their valued work experience in the medical field.
In the present study, the socioeconomic background of German first-year medical school students was surveyed, accompanied by various psychological questionnaires on aspects typically associated with academic skills, performance, and psychological well-being.
For mature students in medical school as well as higher education in general, it has been shown that their academic performance is similar to that of “normal-age students”, while the latter group was more prone to receiving undergraduate awards and postgraduate degrees, including specialized qualifications [11,12]. Mature students have been described as having a more intrinsic and altruistic motivation to study medicine, while younger students are more likely to be influenced by their family background and parental expectations [11,13]. Harth et al. (1990) [11] also found that mature medical students experienced more stress, for example, due to financial difficulties, highlighting the demand for target-group-orientated medical school curricula and supervision.
Apart from academic performance itself, various other associated psychological attributes were analyzed among the first-year medical student cohort. Financial anxiety, for example, has been linked to negative academic outcomes and mental health problems [14]. For academic behavioral confidence, which can be influenced by both personal and contextual factors, a positive association with self-regulation, a deep learning approach, and academic achievement has been described [15,16,17]. Socioeconomic background has also been defined as a variable moderating the relation between test anxiety and test performance [18]. With medicine being a demanding field of education, psychological stress shows a high prevalence in medical students [19]. Therefore, the ability to cope with academic stress, as a further possible predictor for academic performance [20], was compared in a socioeconomic-dependent manner.
For the characterization of the mature student group in comparison with the younger group, the A-level grade of their high-school diploma, competence in natural science subjects necessary for preclinical studies, and their self-assessment of previous clinical experience were surveyed. Moreover, the online competence of both groups was compared, which is especially relevant in times of the recent COVID-19 pandemic and the ongoing transition of solely face-to-face learning to hybrid study concepts with an increasing proportion of online education [21,22]. Mature students are characterized by the completion of an apprenticeship prior to entering medical school. In order to evaluate the impact of a completed apprenticeship as a form of non-academic vocational training on the academic skills of mature students, additional questionnaires on their resilience were compared. The capability of resilience, which also includes the aspect of self-efficacy, is described as the dynamic ability to thrive amidst challenges [23]. It is characterized as an important skill in clinical students and healthcare professionals and is recommended to be promoted during medical education [23,24]. Moreover, resilience is associated positively with academic performance [25]. Previous research shows that life experiences often associated with mature students, such as work and caregiving, might enhance resilience levels [26]. Additionally, worries about balancing studies and jobs as a possible concern of mature students, who often continue to work part-time in their prior positions during medical school, were surveyed and compared.
The aim of the present study was to analyze the sociodemographic constitution of German first-year medical students, focusing on differences between mature and younger students regarding their self-assessment of personal and professional skills postulated to be required for successful completion of university medical education. The results might contribute to an enhanced representation of the German medical student population, which allows for demand-oriented didactic concepts and superiorly convenient medical curricula.

2. Materials and Methods

This study aims to explore the impact of socioeconomic backgrounds and work experience on the academic performance, psychological well-being, and professional skills of first-year medical students in Germany. It also examines the effects of different pathways to medical school, including those for mature students with prior work experience in the medical field, on academic and professional competencies with a strict focus on study entry conditions.
The questionnaire designed for this study, along with the performance assessment, was administered in digital form but completed in person to ensure focused participation and minimize the risk of external assistance during the performance test. Data collection was facilitated through the university’s learning management system, Moodle (eLeDia eLearning im Dialog GmbH, Berlin, Germany), which provided a secure and organized platform for participants to submit their responses. Following data collection, responses were exported to Excel (Microsoft Excel 2021) for initial processing and data management. The statistical analyses were then conducted using R statistical software (Version 4.3.3) (R Foundation for Statistical Computing, Vienna, Austria), ensuring a rigorous and transparent approach to data analysis. This process allowed for efficient handling of large datasets while maintaining accuracy and integrity throughout the analytical workflow. At the core of the developed survey tool was the processing of the grouping variable, which divided the personal socioeconomic status into the five categories ‘below average’, ‘slightly below average’, ‘average’, ‘slightly above average’, and ‘above average’. In developing a questionnaire that accurately reflects the research objectives, a selected panel of standardized scales was chosen. Each scale was chosen for its relevance and proven effectiveness in exploring the specific aspects of our research questions.
A more in-depth analysis of the aspect of socioeconomic status was carried out by incorporating a German translation of the Financial Anxiety Scale (FAS) developed by Archuleta, Dale, and Spann (2013) [27]. This instrument is recognized for its ability to quantify financial distress, capturing dimensions such as debt-related stress, financial satisfaction, and broader anxieties regarding economic security. The FAS has been widely applied in academic and counseling contexts to identify individuals experiencing elevated levels of financial anxiety, which can detrimentally affect psychological well-being and academic performance. Its relevance in a medical student cohort allowed us to examine the intricate relationship between socioeconomic status and financial anxiety, providing valuable insights into how financial stress may influence both the psychological and academic outcomes of students in a highly demanding educational environment [27].
To measure cognitive test anxiety among the participants, we utilized the Cognitive Test Anxiety Scale (CTAS) developed by Cassady and Johnson (2002) [28]. This scale is recognized for its precision in assessing the cognitive aspects of test anxiety, focusing on how anxiety disrupts cognitive processes during test situations. The CTAS has been validated as a reliable and effective tool for quantifying the extent to which anxiety interferes with academic performance. Prior research has demonstrated a strong association between elevated levels of cognitive test anxiety and poorer performance on various academic assessments. In the context of our study, the CTAS provided a robust framework for examining how anxiety related to testing may impact students’ ability to cope with the academic demands of medical education, offering valuable insights into the broader relationship between psychological well-being and academic achievement [28]. Furthermore, the study incorporated a culturally and linguistically adapted German version of this scale (G-CTAS), meticulously developed and psychometrically evaluated by Stefan, Berchtold, and Angstwurm (2020), thereby ensuring its appropriateness for the German student cohort [29].
A German translation of the academic behavioral confidence (ABC) scale [30,31] was employed to assess participants’ self-assurance across various academic tasks. The ABC scale uses a 24-item questionnaire covering key areas of academic performance and engagement, asking students to rate their confidence in activities such as managing independent study, producing work under examination conditions, giving presentations, engaging in academic debate, meeting deadlines, and asking questions in both one-to-one and lecture settings. These items are measured using a 5-point Likert scale ranging from “Not at all confident” to “Very confident,” allowing for a detailed assessment of academic self-confidence. The scale has demonstrated high internal consistency, indicating its reliability as a tool for evaluating students’ academic confidence. This instrument offers insights into how students perceive their ability to succeed in academic tasks, which can be crucial for identifying areas where additional support may be needed [32].
In addition, a German translation of the Resilience Questionnaire Scale (RQS) was used to assess the resilience levels of participants. This scale is based on the shortened version of the Nicholson McBride Resilience Questionnaire (NMRQ), which measures an individual’s capacity to thrive and adapt in the face of adversity. The RQS evaluates key dimensions of resilience, such as the ability to manage stress, maintain motivation, and recover quickly from setbacks. Participants are asked to rate themselves across these areas, providing a comprehensive understanding of their resilience levels. This tool has been widely recognized for its practical application in both educational and professional settings, offering insights into how well individuals can cope with challenges. Its use in the current study allows us to examine the relationship between resilience and academic performance in the demanding environment of medical education.
The Student Self-Efficacy (SSE) Scale [33] was incorporated into this research to assess participants’ confidence in their ability to successfully complete academic tasks. This scale is designed to measure self-efficacy specifically related to academic coursework, offering valuable insights into students’ intrinsic motivation and self-regulation abilities. The SSE scale has demonstrated strong reliability and validity, as evidenced by a significant correlation (r = 0.70) with the well-established General Self-Efficacy (GSE) Scale, confirming its utility in academic settings. The scale captures students’ perceived competence in executing classroom tasks and is widely regarded as a robust tool for evaluating how self-efficacy influences academic performance. In this study, the SSE scale helped to assess the relationship between self-efficacy and academic success among first-year medical students. The Performance Failure Appraisal Inventory (PFAI) [34] is a comprehensive tool designed to assess the cognitive, motivational, and relational aspects associated with the fear of failure (FF). This inventory evaluates five key negative outcomes individuals may anticipate when facing failure, including (a) feelings of shame and embarrassment, (b) a diminished sense of self-worth, (c) uncertainty about one’s future, (d) concerns that important people may lose interest, and (e) distress from upsetting those close to them. The PFAI consists of 25 items organized into these five dimensions, which have been validated through a higher-order factor structure. This structure has been confirmed to be reliable across different samples, including a five-item short form that maintains high validity. Research using the PFAI has shown that fear of failure is linked to higher levels of anxiety, worry, and cognitive disruption while being inversely related to optimism. Interestingly, the general fear of failure does not appear to correlate with perceived competence or fear of success. In this study, we employed a German translation of the PFAI, validated by Henschel and Iffland (2021) [35], to ensure contextual relevance for our student participants, allowing us to explore the role of fear of failure in their academic experiences.
Worries about balancing studies and a job, previous clinical experience, competence in natural science subjects, and online competence were measured using a visual analog scale, enabling the collection of data that can be measured on both continuous and interval scales [36]. Further grouping variables were integrated to identify whether the participants already had work experience or had completed an apprenticeship.
To assess the academic competencies of first-year medical students, particularly in areas pertinent to their upcoming studies, a specialized expert panel was convened. This panel was composed of distinguished members from the academic medical community, including a professor and an academic counselor specializing in anatomy, as well as two senior medical students, in order to integrate the student perspective. The primary task of this panel was to formulate a set of science-related questions that would effectively evaluate the basic science knowledge of incoming medical students, with an emphasis on thematic areas crucial for their first-year studies, particularly in anatomy and physiology. To ensure the relevance and appropriateness of these questions, the panel adhered to two key criteria. Each question was designed to correspond with the general school curriculum. This approach was adopted to ensure that the assessment was grounded in the foundational knowledge that students are expected to have acquired prior to entering medical school. The content of the questions was carefully chosen to reflect the essential basic science knowledge that would be beneficial for students in their initial semester of medical studies. In total, the expert panel crafted nine questions that met these stringent criteria. These questions were then utilized as a key component of the performance assessment, providing valuable insights into the preparedness and foundational knowledge of the first-year medical student cohort.
The primary requirement for inclusion in the study was that participants must be duly enrolled as first-semester medical students at Ruhr University Bochum at the time of data collection. This criterion ensured that the study focused on individuals who were at the very outset of their medical education, providing a uniform baseline. In line with the study’s focus on exploring social diversity in medical education, the recruitment strategy was designed to be as inclusive as possible. The study did not impose any age restrictions, ensuring the representation of both younger students and mature students who might have entered medical school later in life. Notably, this approach resulted in the successful assessment of almost the entire cohort of first-year medical students for that academic year, thereby providing a comprehensive and representative overview of the group’s characteristics and experiences.
The study’s participant pool comprised 336 first-semester medical students from Ruhr University Bochum, offering a diverse demographic snapshot. Among the participants, females constituted the majority, with 227 students (67.56%), while male students accounted for 107 individuals (31.85%). Additionally, 2 students (0.60%) identified as gender diverse. The average age of the participants was 20.40 years, with a standard deviation of 3.10 years. Notably, female students had an average age of 20.15 years (SD = 2.97), slightly younger than male students who averaged 20.95 years (SD = 3.37). The gender-diverse group had an average age of 19.50 years (SD = 0.71) (Table 1). For more comprehensive characteristics of the cohort in terms of socioeconomic status, frequency of students whose parents did not go to university, frequency of students whose parents are physicians, frequency of students with working experience, and frequency of students with completed apprenticeships, please see Table 1. This study adhered to the ethical guidelines of the Declaration of Helsinki and was approved by the Ethics Committee of the Professional School of Education at Ruhr University Bochum (Reference No. EPSE-2022–005, dated 10 October 2022). This approval ensured the ethical conduct of the research in accordance with international standards.
The study employed a comprehensive suite of statistical methods to rigorously analyze the collected data. Descriptive statistics were calculated to provide a detailed characterization of the dataset, including measures of central tendency (median, mean), dispersion (standard deviation, interquartile range, variance), and distribution shape (skewness and kurtosis), along with their respective standard errors. Minimum and maximum values were also calculated to define the range of the data. These descriptive metrics offered an in-depth overview of the data’s structure and ensured a thorough understanding of the variables at play. For inferential analysis, a Welch’s t-test was utilized to compare differences between students with work experience or completed apprenticeships and those without such experience. This method was chosen for its robustness, particularly in scenarios with unequal variances, ensuring accurate and reliable results for two-group comparisons. When analyzing differences across more than two groups, analysis of variance (ANOVA) was employed. An alpha level of 0.05 was applied, and to mitigate the risk of Type I errors in multiple comparisons, p-values were adjusted using the Bonferroni–Holm correction method, which provides a more stringent control over false positives while retaining statistical power. To explore relationships between variables, correlation analyses were conducted, utilizing Pearson’s r to determine the strength and direction of linear relationships. Confidence intervals (95%) were calculated alongside these correlations to provide a robust estimation of the precision of the relationship. All statistical computations, from descriptive to inferential analyses, were executed using R statistical software (R Foundation for Statistical Computing, Vienna, Austria).

3. Results

The analysis of the collected data commenced with a comprehensive descriptive evaluation of the various scales utilized in the study. This initial step involved a detailed examination of the participants’ responses, providing a foundational understanding of the data’s distribution and central tendencies. Subsequently, we performed analyses of variance (ANOVA) to investigate the potential influence of socioeconomic background on specific factors. This analysis aimed to discern whether variations in socioeconomic status were associated with differences in key study variables. Further, the participants were grouped based on their work experience and completion of apprenticeships. This categorization allowed for an in-depth exploration of the impact of practical work experience on the assessed factors, offering insights into how these experiences might shape academic and psychological outcomes. Finally, correlation analyses were conducted to uncover potential relationships between relevant constructs. These analyses aimed to identify significant correlations, providing a deeper understanding of how these variables interplay within the context of medical education.
In the initial analysis of our data, we conducted descriptive statistics for various scales, as shown in Table 2. The measures included the Financial Anxiety Scale (FAS), academic behavioral confidence (ABC), German-Cognitive Test Anxiety Scale (CTAS), Fear of Shame and Embarrassment (FSE), coping with academic stress (CAS), Resilience Questionnaire Scale (RQS), Student Self-Efficacy (SSE), and others assessing factors such as worries about balancing studies and a job (WBSJ), online competence (OC), clinical experience (CE), Natural Sciences Competence (CNS), and A-level grade (ALG). The median scores across these scales provided a central tendency of the dataset. The median for Performance was 3.000, with other notable medians, including FAS at 1.143 and ABC at 6.958. The mean scores mirrored these tendencies, with performance at 3.234, FAS at 1.321, and ABC at 6.934. The variability of responses was quantified through the standard deviation and interquartile range (IQR). For instance, the standard deviation for performance was 2.207, indicating a moderate spread of scores around the mean. The IQR for the same scale was 3.250, highlighting the middle 50% range of scores. The data’s distribution characteristics were further explored through measures of skewness and kurtosis. The skewness of the performance scale was 0.425, suggesting a slight asymmetry in its distribution. The kurtosis value of −0.617 for this scale suggests a distribution that is somewhat flatter than that of a normal distribution. Finally, the ranges of the scales were considered, with the performance scale ranging from a minimum of 0.000 to a maximum of 9.000. This broad range suggests a wide variation in student performance (Table 2).
Subsequent to the descriptive analysis, analyses of variance (ANOVA) were conducted to examine the influence of socioeconomic status (SES) on various psychological and performance measures (Table 3).
For the Financial Anxiety Scale (FAS), the ANOVA showed a significant effect of SES, F(4, 331) = 17.391, p < 0.001, with a notable effect size (η2 = 0.174 and ω2 = 0.163), suggesting that variations in SES are strongly associated with differences in financial anxiety among students.
In the context of academic behavioral confidence (ABC), the analysis yielded F(4, 331) = 4.323, p = 0.002, with a smaller yet significant effect size (η2 = 0.050, ω2 = 0.038). This indicates that SES also plays a role in influencing students’ confidence in their academic abilities.
For the German-Cognitive Test Anxiety Scale (CTAS), SES-related differences were again significant, F(4, 331) = 4.482, p = 0.002, with effect sizes of η2 = 0.051 and ω2 = 0.040. This finding suggests a notable association between socioeconomic background and cognitive test anxiety among the students.
Similarly, in assessing the influence of SES on coping with academic stress (CAS), the results showed a significant effect, F(4, 331) = 5.837, p < 0.001, with η2 = 0.066 and ω2 = 0.054, indicating that SES significantly impacts how students cope with academic stress.
When examining Student Self-Efficacy (SSE), the ANOVA indicated a significant effect of SES, F(4, 331) = 3.915, p = 0.004, with effect sizes of η2 = 0.045 and ω2 = 0.034. This highlights SES as a factor influencing students’ beliefs in their capabilities to execute academic tasks.
However, the influence of SES on the Resilience Questionnaire Scale (RQS) approached significance, F(4, 331) = 2.372, p = 0.052, with smaller effect sizes (η2 = 0.028, ω2 = 0.016), suggesting a potential but less pronounced impact of SES on resilience.
The analysis for worries about balancing studies and a job (WBSJ) yielded F(4, 331) = 2.106, p = 0.080, with η2 = 0.025 and ω2 = 0.013, indicating a marginal influence of SES on concerns about managing studies and employment.
For Fear of Shame and Embarrassment (FSE), and performance (PERF), the results were not significant, with FSE showing F(4, 331) = 1.457, p = 0.215, and η2 = 0.017, ω2 = 0005, and PERF showing F(4, 331) = 1.067, p = 0.373, η2 = 0.013, ω2 = 0.0008. These findings suggest that SES does not significantly impact the fear of experiencing shame and embarrassment or the overall academic performance in this cohort.
The ANOVA results indicate that socioeconomic status plays a significant role in various aspects of students’ experiences, particularly in areas related to financial anxiety, academic confidence, test anxiety, and coping with academic stress. However, its impact on resilience, worries about balancing studies and a job, fear of experiencing shame and embarrassment, and academic performance appears to be less pronounced or not significant in this study group (Figure 1).
We further explored the differences between students with prior work experience and those without such experience. This comparison focused on self-assessment of previous clinical experience, A-level grade, competence in natural sciences subjects, and online competence.
Our analysis revealed significant differences in self-assessment of previous clinical experience, t(333.840) = 8.889, p < 0.001. Students with work experience reported higher levels of clinical experience compared to their counterparts without such background (Figure 2A).
In terms of performance, as measured by A-level grades, there was a significant difference between the two groups, t(279.459) = 6.690, p < 0.001. This result indicates that students who already gained work experience scored significantly lower in their A-level grades (Figure 2B).
When assessing self-perceived competence in natural sciences subjects, the study found a significant difference, t(321.045) = −3.178, p = 0.002, demonstrating that students without prior work experience reported higher competence in natural sciences compared to those with work experience (Figure 2C).
The analysis of online competence showed a significant difference, t(319.429) = −2.026, p = 0.044. Students without work experience demonstrated higher competence in online learning environments compared to those with work experience (Figure 2D).
Deepening these analyses, we assessed the differences between students who had completed an apprenticeship prior to their medical studies and those who had not. The focus was on worries about balancing studies and a job, resilience as measured by the Resilience Questionnaire Scale (RQS), academic behavioral confidence as measured by the academic behavioral confidence (ABC) scale, and self-efficacy as measured by the Student Self-Efficacy (SSE) Scale.
Regarding worries about balancing studies and a job, the t-test showed a significant difference, t(73.490) = 2.097, p = 0.039. This suggests that students with completed apprenticeships experienced greater concerns about managing their studies alongside a job compared to those without such experience (Figure 3A).
In terms of resilience (RQS), there was a significant difference, t(68.134) = 3.158, p = 0.002. Students who had completed an apprenticeship demonstrated higher levels of resilience compared to their peers without apprenticeship experience (Figure 3B).
Regarding academic behavioral confidence (ABC), the analysis revealed a significant difference, t(70.380) = 2.085, p = 0.041. This finding indicates that students with apprenticeship experience had higher academic confidence than those without such background (Figure 3C).
In the aspect of self-efficacy (SSE), the results showed a significant difference, t(68.847) = 2.020, p = 0.047. This suggests that students with apprenticeship experience possessed a stronger belief in their capabilities to successfully execute academic tasks compared to their counterparts without an apprenticeship (Figure 3D).
The study further investigated the relationships between key constructs: resilience (RQS), coping with academic stress (CAS), academic behavioral confidence (ABC), Cognitive Test Anxiety (CTA), and performance, using Pearson’s correlation coefficient (Pearson’s r) and associated p-values (Figure 4).
A strong positive correlation was observed between resilience (RQS) and coping with academic stress (CAS), r = 0.632, p < 0.001, suggesting that higher resilience is associated with better coping strategies in academic stress situations. Additionally, a significant positive correlation was found between RQS and academic behavioral confidence (ABC), r = 0.608, p < 0.001, indicating that increased resilience is linked with higher confidence in academic abilities (Figure 4).
Coping with academic stress (CAS) also showed a significant positive correlation with ABC, r = 0.526, p < 0.001, implying that students who are better at coping with academic stress tend to have higher academic confidence (Figure 4).
Conversely, there were negative correlations observed with Cognitive Test Anxiety (CTA). Resilience (RQS) inversely correlated with CTA, r = −0.356, p < 0.001, suggesting that higher resilience is associated with lower cognitive test anxiety. Similarly, both CAS and ABC negatively correlated with CTA, r = −0.235 and r = −0.404, respectively, both p < 0.001, indicating that better coping mechanisms and higher academic confidence are associated with lower test anxiety (Figure 4).
In terms of academic performance, the correlations were relatively weaker and less consistent. However, performance showed a significant positive correlation with ABC, r = 0.160, p = 0.003, suggesting a modest association between academic confidence and performance. However, correlations between performance and other constructs like RQS, CAS, and CTA were not statistically significant, with r values of 0.067 (p = 0.220), 0.070 (p = 0.199), and −0.094 (p = 0.087), respectively (Figure 4).

4. Discussion

This research project provides a comprehensive exploration of the impact of socioeconomic backgrounds and work experience on first-year medical students. The findings provide insightful revelations into how these factors influence various aspects of medical education, including academic performance, psychological well-being, and professional skills. An important aspect of this research project is the characterization of the study site—the city of Bochum, located in the central west of Germany. Bochum’s geographical and socio-cultural context played a significant role in shaping the findings of our study. Being a city with a diverse population and a rich industrial history, Bochum represents a setting that reflects the broader dynamics of urban centers in Germany. This diversity is crucial in understanding the backgrounds and experiences of the medical students enrolled at Ruhr University Bochum.
In line with this, our study’s descriptive analysis revealed significant diversity within the student cohort in terms of financial anxiety, academic confidence, and coping strategies for academic stress, among other factors. These variations underscore the complexity of the student experience in medical education, influenced by an array of social and personal factors. Notably, the socioeconomic status (SES) of students emerged as a significant determinant in several key areas, especially in financial anxiety and academic confidence, aligning with the existing literature that underscores the impact of SES on educational outcomes [37,38,39]. Our findings align with those of a recent study that explored the psychological dimensions affecting first-year medical students, particularly the impact of socioeconomic status (SES) on Anxiety Sensitivity (AS) and Intolerance of Uncertainty (IU) [40]. Similar to our results, that study highlighted the significant influence of SES on psychological well-being, revealing that lower SES is associated with heightened anxiety and uncertainty, which exacerbate stress among students [40].
However, contrary to the significant correlation between socioeconomic status (SES) and academic achievement found in earlier studies, such as the landmark research by Coleman et al. (1966) [37], our study did not observe a significant difference in performance based on SES among first-year medical students. Coleman’s comprehensive study, involving over 640,000 students across 4000 schools in the USA, highlighted the dominant role of family SES in academic achievement, surpassing even the influence of schools. Reardon (2013) [38] suggests that the strength of the SES–academic achievement relationship reflects educational equity: a higher correlation indicates a wider achievement gap between high-SES and low-SES students. The lack of significant differences in our study could imply that factors other than SES have become more relevant in the current educational context, possibly due to improvements in educational equity and access. This notion is supported by the work of Heyneman and Loxley (1983) [41], who noted that the relationship between SES and academic achievement is not consistent and varies across different contexts. They highlighted that this relationship depends significantly on the socioeconomic and cultural environment, suggesting that in certain contexts, SES might not be as pivotal a determinant of academic performance as it once was. Additionally, the findings of Liu et al. (2020) [42] and Soharwardi et al. (2020) [43] hint at evolving dynamics in the influence of SES. Liu et al. (2020) [42] reported a decreasing trend in the relationship between SES and academic achievement over the past decades. Simultaneously, Soharwardi et al. (2020) [43] emphasized the role of maternal education and governmental support in enhancing student performance, suggesting that these factors might now be playing a more critical role.
However, it is crucial to consider factors that indirectly influence academic performance, particularly the significant effect of socioeconomic status (SES) on financial anxiety, as demonstrated in our study. This link is critical, given the substantial evidence suggesting that financial anxiety can adversely affect students’ mental health and educational outcomes. Financial stress correlated with increased mental health issues and disrupted sleep, factors that can impede academic success [44]. Financial worries can extend beyond monetary concerns, often having a negative impact on students’ academic performance. Echoing this, Xiao et al. (2017) [45] observed trends of rising anxiety and depression among college students, correlating with financial stress. Further, financial stress can be linked to academic decisions, such as reduced course loads or dropout, and to poorer performance [46]. Additionally, Potter et al. (2020) [14] emphasize the heightened financial anxiety among first-generation students, suggesting that targeted financial counseling could alleviate some of these stressors. In light of our findings, it becomes imperative to heighten awareness about the relevance of financial anxiety in the context of academic performance, particularly as a step towards achieving more equal educational opportunities. The significant relationship between socioeconomic status (SES) and financial anxiety observed in our study underscores the need for educational institutions to recognize and address the broader implications of financial stress on student well-being and learning outcomes.
It is common knowledge that academic behavioral confidence shows a positive correlation with academic performance, yet its influence is subject to variation across diverse contexts and individual characteristics. The interplay of self-confidence, the possibility of overconfidence [47], and personal expectations in determining academic success is rather complex and influenced by a multitude of factors. Academic behavioral confidence is also used as a relevant marker to predict the success of first-semester students in meeting the newly emerging demands of higher education [31]. This aligns with our findings, suggesting that students from higher SES backgrounds may have an advantage in adapting to the demands of higher education. In light of our findings, the relationship between SES and academic behavioral confidence becomes a crucial area for educational interventions. Efforts to equalize educational opportunities should consider not only the direct academic support for lower SES students but also strategies to build and correctly calibrate their academic confidence, ensuring that all students can optimally engage with and benefit from higher education.
The significant impact of socioeconomic status (SES) on cognitive test anxiety, as observed in our study, where higher SES correlated with lower test anxiety, aligns with findings from recent research. Xu et al. (2021) [39] highlight that higher SES is associated with factors like increased learning resources and academic self-efficacy, leading to lower test anxiety. This complements our results, suggesting that students from higher SES backgrounds enjoy advantages that alleviate anxiety during tests. Further research links first-generation student status, a proxy for lower SES, with increased financial anxiety, shaping academic experiences [14].
Contrary to our initial expectations, it was not the students with the lowest SES who reported the most challenges but rather those who placed themselves in the mid-to-lower range of the SES spectrum. This pattern was particularly evident in areas such as academic behavioral confidence, cognitive test anxiety, and coping with academic stress. This finding suggests a more complex dynamic than a straightforward linear relationship between SES and these variables. Students in the mid-to-lower SES category may face unique pressures and challenges that are less prevalent among their peers with the lowest SES. For instance, these students might experience heightened expectations to succeed or may lack the same level of support systems or coping mechanisms available to those at either end of the SES spectrum. This could potentially explain their lower academic confidence, higher test anxiety, and reduced ability to cope with academic stress. Furthermore, this observation underscores the importance of considering the full spectrum of SES in educational and psychological research rather than focusing solely on the extremes. It highlights that students with mid-to-lower SES face distinct challenges that require targeted support and intervention. This understanding is crucial for the development of nuanced educational policies and mental health services that cater to the diverse needs of the entire student body, particularly those who might fall into this overlooked and potentially vulnerable group.
In addition, our data reveal a complex picture of the impact of prior working experience on medical students’ competencies. While students with such experience show significantly higher self-perceived clinical experience, they also tend to have lower A-level grades, less competence in natural sciences, and weaker online skills. This finding aligns with Benor and Hobfoll (1981) [48], who observed that medical students with diverse life experiences, such as military or science backgrounds, often demonstrate superior clinical performance. Further exploration revealed that medical students with pre-matriculation clinical experience outperformed their peers in crucial medical exams [49]. Similarly, McKenzie and Mellis (2017) [50] reported that practical experience prior to medical school enhances students’ preparedness for clinical practice. This can additionally be reinforced by findings that highlight the positive impacts of working as healthcare assistants on medical students’ understanding of clinical environments [51]. These studies suggest that while prior work experience can lead to gaps in certain academic areas, it significantly enhances clinical skills and understanding, highlighting the value of practical experience in medical education. However, awareness should be created for the associated less developed skills, for example, in the area of online competence, as this can be of particular relevance in the course of advancing digitalization in the academic sector.
Our findings, which show that students with completed apprenticeships exhibit higher resilience, academic behavioral confidence, and self-efficacy but also face greater challenges in balancing studies and work, resonate with the existing literature on the value of practical experiences in education. Previous findings highlight the role of academic self-efficacy in fostering key student competencies, suggesting that hands-on experiences from apprenticeships could enhance these attributes [52]. It could be shown that there is a strong link between academic self-efficacy and resilience [53], a pattern observed in our study, where apprenticeship-experienced students displayed both traits robustly. Related research work emphasizes the impact of resilience on performance and well-being, mediated by self-efficacy [54]. Our data reflect this complex interaction, as apprenticeship students demonstrate significant resilience and self-efficacy yet struggle with the added pressure of managing work alongside their studies.
The recent findings of Schröpel et al. (2024) [55] both contrast with and complement our observations, suggesting a nuanced view of the impact of prior experiences on medical education. While they report that prior professional and academic qualifications do not translate to academic success, as measured by traditional examinations, our research indicates that such experiences enhance students’ resilience and academic confidence. This discrepancy highlights the complexity of assessing academic success, underscoring the potential of personal qualities and non-academic skills gained from prior experiences, which are not captured by conventional metrics but are crucial for navigating medical education.
The correlation analysis of our study, examining the relationships between resilience (RQS), coping with academic stress (CAS), academic behavioral confidence (ABC), Cognitive Test Anxiety (CTA), and performance, yields insights that align with and extend the findings of the existing literature in the field of educational psychology. Significantly, we found a strong positive correlation between resilience and coping with academic stress, as well as between resilience and academic behavioral confidence. This indicates that higher resilience is associated with better coping abilities and greater confidence in academic settings, echoing the findings of Mulati and Purwandari (2022) [56], who reported a negative correlation between resilience and academic stress. Our results reinforce the idea that resilience is a crucial factor in managing academic stress effectively. Further, our analysis revealed negative correlations between resilience, coping with academic stress, academic behavioral confidence, and Cognitive Test Anxiety (CTA). Specifically, higher resilience and confidence were associated with lower CTA. This complements the work of Havnen et al. (2020) [57], who found that resilience moderates the relationship between stress and anxiety. Our findings suggest that resilience not only helps in coping with academic stress but also plays a vital role in reducing test anxiety, a key factor impacting academic performance. Regarding performance, the correlations were weaker. There was a modest but significant positive correlation between academic behavioral confidence and performance (r = 0.160, p = 0.003), suggesting that confidence does contribute to academic achievement, albeit to a lesser extent. This is in line with Duty et al. (2016) [58], who found a correlation between cognitive test anxiety and academic performance among nursing students, indicating that psychological factors, including confidence and anxiety, have important, albeit complex, roles in academic success. These correlations underscore the interconnectedness of resilience, stress coping, confidence, and anxiety in the academic context.
While this study provides valuable insights into how socioeconomic background and prior work experience influence psychological well-being and academic confidence in first-year medical students, there are several limitations that warrant consideration. These limitations also offer promising directions for future research.
Although the study explored the general impact of extracurricular work experience, it did not account for the specific duration or intensity of that experience. It is possible that the length of time spent in professional roles could have a differential impact on academic and psychological outcomes. Future research should examine how the duration and nature of work experience—whether short-term or long-term—affects factors such as academic resilience, confidence, and performance. Further, the study did not differentiate between various types of clinical experience, such as surgical versus non-surgical specialties, which may influence students’ academic outcomes in distinct ways. For example, students with experience in high-pressure environments, like surgical departments, may develop different skills compared to those in less intense settings. Future research should investigate whether specific areas of clinical experience contribute differently to students’ academic performance and psychological resilience.
While mature students with prior work experience generally performed better in several psychological and academic measures despite lower A-level grades, this study did not directly address how these findings might inform medical school admission policies. Future research could explore whether practical experience should play a larger role in admission decisions and whether it leads to long-term professional success. Longitudinal studies that track students throughout their medical education and into their careers would provide deeper insights into the lasting impact of work experience on clinical competencies.
Based on the limitations identified, several areas of future research are recommended. First, studies could focus on the specific duration and nature of work experience to understand its varied impacts on students’ academic and psychological development. Second, a more detailed analysis of different clinical specialties could reveal how experience in specific areas shapes academic success. Lastly, research on the long-term implications of these findings could inform potential revisions to admission criteria, ensuring that medical schools consider a balanced approach between academic qualifications and practical experience.

5. Conclusions

The findings of this study emphasize the complex relationship between socioeconomic status (SES), prior work experience, and academic performance in first-year medical students. These factors significantly influence students’ academic confidence, resilience, and coping mechanisms, which are critical for thriving in medical school. Our results suggest that students with diverse backgrounds bring unique strengths to the academic environment, but they also face distinct challenges that warrant further investigation. Future research should explore how the interaction of these factors evolves over the course of medical education, particularly in relation to long-term professional competencies and patient care outcomes. Investigating how early experiences, such as apprenticeships or extracurricular work, influence clinical readiness and adaptability in high-stakes environments could provide valuable insights for both educators and policymakers. In addition, interventions aimed at supporting students from lower SES backgrounds or those lacking professional experience could be pivotal. Research should focus on identifying which support systems most effectively enhance academic confidence, resilience, and overall well-being. This approach aligns with the principles of competency-based medical education (CBME), which emphasizes the development of core competencies necessary for medical practice, regardless of students’ backgrounds. Tailored interventions, such as mentoring programs, skill-building workshops, or financial assistance initiatives, would not only support academic growth but also foster the essential competencies required for future healthcare professionals. In conclusion, this study emphasizes the value of recognizing the diverse experiences and backgrounds of medical students and how these factors impact their academic performance and resilience. One of the strengths of this study lies in its detailed analysis of how socioeconomic status and prior work experience shape key competencies that are crucial for success in medical education. By addressing the specific needs of students from varied backgrounds, medical schools can implement practical strategies to improve both academic outcomes and clinical preparedness. This approach not only enhances educational success but also contributes to developing a capable and adaptable healthcare workforce ready to meet the complex demands of patient care.

Author Contributions

Conceptualization, M.G. and T.S.; methodology, M.G.; software, M.G.; validation, M.G., T.S., B.B.-S. and M.B.; formal analysis, M.G., T.S., M.B. and B.B.-S.; investigation, M.G.; resources, T.S. and B.B.-S.; data curation, M.G., T.S., M.B. and B.B.-S.; writing—original draft preparation, M.G.; writing—review and editing, M.G., M.B., T.S. and B.B.-S.; visualization, M.G.; project administration, M.G. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

This study adhered to the ethical guidelines of the Declaration of Helsinki and was ap-proved by the Ethics Committee of the Professional School of Education at Ruhr Uni-versity Bochum (Reference No. EPSE-2022–005, dated 10 October 2022).

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The data supporting the findings of this study are available from the corresponding author upon reasonable request.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. This figure illustrates the distribution of scores for each scale as related to socioeconomic status (SES). The boxplots display the median (central line within each box), interquartile range (extent of each box), and overall range (whiskers extending from the boxes), providing a visual summary of the central tendency and variability within each SES group. Individual measurement points are plotted around the boxplots, offering a detailed view of the data distribution and highlighting outliers.
Figure 1. This figure illustrates the distribution of scores for each scale as related to socioeconomic status (SES). The boxplots display the median (central line within each box), interquartile range (extent of each box), and overall range (whiskers extending from the boxes), providing a visual summary of the central tendency and variability within each SES group. Individual measurement points are plotted around the boxplots, offering a detailed view of the data distribution and highlighting outliers.
Education 14 01173 g001
Figure 2. This figure illustrates the differences in key outcomes between students with and without prior working experience. The bar chart is divided into four parts, labeled (AD): self-assessment of previous clinical experience (A), A-level grade (B), competence in natural sciences subjects (C), and online competence (D). The height of each bar indicates the mean score for each group, with error bars representing the standard error. A-level grade was rated on a scale from 1.0 to 4.0, where 1.0 represents the highest achievable A-level grade. Significant differences between groups are marked with asterisks, where * denotes p < 0.05, ** denotes p < 0.01, and *** denotes p < 0.001.
Figure 2. This figure illustrates the differences in key outcomes between students with and without prior working experience. The bar chart is divided into four parts, labeled (AD): self-assessment of previous clinical experience (A), A-level grade (B), competence in natural sciences subjects (C), and online competence (D). The height of each bar indicates the mean score for each group, with error bars representing the standard error. A-level grade was rated on a scale from 1.0 to 4.0, where 1.0 represents the highest achievable A-level grade. Significant differences between groups are marked with asterisks, where * denotes p < 0.05, ** denotes p < 0.01, and *** denotes p < 0.001.
Education 14 01173 g002
Figure 3. This bar chart depicts the comparative analysis between students who have completed an apprenticeship and those who have not. Divided into four segments labeled (AD), the figure represents worries about balancing studies and a job (A), resilience (B), academic behavioral confidence (C), and self-efficacy (D). Bars represent the mean values for each group, with standard error depicted by the error bars. Asterisks indicate the level of significance in the differences observed, with * denoting p < 0.05 and ** for p < 0.01.
Figure 3. This bar chart depicts the comparative analysis between students who have completed an apprenticeship and those who have not. Divided into four segments labeled (AD), the figure represents worries about balancing studies and a job (A), resilience (B), academic behavioral confidence (C), and self-efficacy (D). Bars represent the mean values for each group, with standard error depicted by the error bars. Asterisks indicate the level of significance in the differences observed, with * denoting p < 0.05 and ** for p < 0.01.
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Figure 4. This figure presents a comprehensive visualization of the correlations between five key constructs: resilience (RQS), coping with academic stress (CAS), academic behavioral confidence (ABC), Cognitive Test Anxiety (CTA), and performance. Each correlation is depicted through a combination of scatter plots with fitting lines, demonstrating the nature of the relationship between each pair of constructs. Accompanying each scatter plot are density plots, providing a detailed view of the distribution of scores for each variable. The figure also includes the calculated Pearson’s r values and their corresponding 95% confidence intervals for each correlation, summarizing the strength and direction of the relationships. Positive correlations are indicated by upward-sloping lines, and negative correlations by downward-sloping lines in the scatter plots.
Figure 4. This figure presents a comprehensive visualization of the correlations between five key constructs: resilience (RQS), coping with academic stress (CAS), academic behavioral confidence (ABC), Cognitive Test Anxiety (CTA), and performance. Each correlation is depicted through a combination of scatter plots with fitting lines, demonstrating the nature of the relationship between each pair of constructs. Accompanying each scatter plot are density plots, providing a detailed view of the distribution of scores for each variable. The figure also includes the calculated Pearson’s r values and their corresponding 95% confidence intervals for each correlation, summarizing the strength and direction of the relationships. Positive correlations are indicated by upward-sloping lines, and negative correlations by downward-sloping lines in the scatter plots.
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Table 1. Demographics.
Table 1. Demographics.
Gender and Age
GenderFrequencyPercentAge Mean (SD)
Female22767.56020.15 (2.97)
Male10731.84520.95 (3.37)
Diverse20.59519.50 (0.71)
Total33610020.40 (3.10)
Frequency of socioeconomic status characteristics
Socioeconomic statusFrequencyPercent
Below average4212.500
slightly below average3410.119
average8725.893
slightly above average9528.274
above average7823.214
Total336100
Frequency of students whose parents did not go to university
Study pioneersFrequencyPercent
yes7923.512
no25776.488
total336100
Frequency of students whose parents are physicians
Physician parentsFrequencyPercent
yes6017.857
no27682.143
total336100
Frequency of students with work experience
Working experienceFrequencyPercent
yes15746.726
no17953.274
total336100
Frequency of students with completed apprenticeship
Completed apprenticeshipFrequencyPercent
yes5416.071
no28283.929
total336100
Note: SD means standard deviation.
Table 2. Descriptive statistics.
Table 2. Descriptive statistics.
PerformanceFASABCCTAFSECASRQSSSEWBSJOCCECNSALG
Median3.0001.1436.9582.1762.4293.50038.0003.4002.0007.0005.0007.0001.200
Mean3.2341.3216.9342.2652.5553.39738.4523.4242.2297.1834.8636.3211.381
Std. Error of Mean0.1200.0280.0610.0420.0530.0460.4260.0360.0600.1110.1550.1040.025
Std. Deviation2.2070.5091.1230.7680.9660.8527.8170.6631.0922.0352.8371.9080.466
IQR3.2500.4291.4271.1181.2861.00011.0000.9002.0003.0004.2503.0000.500
Variance4.8700.2591.2620.5900.9330.72661.1080.4401.1924.1408.0473.6410.217
Skewness0.4252.447−0.3060.6130.432−0.1390.059−0.0780.573−0.558−0.127−0.4941.828
Std. Error of Skewness0.1330.1330.1330.1330.1330.1330.1330.1330.1330.1330.1330.1330.133
Kurtosis−0.6176.588−0.137−0.201−0.692−0.592−0.330−0.104−0.507−0.119−0.9690.4093.244
Std. Error of Kurtosis0.2650.2650.2650.2650.2650.2650.2650.2650.2650.2650.2650.2650.265
Minimum0.0001.0003.2921.0001.0001.00017.0001.3001.0000.0000.0000.0000.900
Maximum9.0003.8579.2924.5885.0005.00058.0005.0005.00010.00010.00010.0003.300
Note: FAS—Financial Anxiety Scale; ABC—academic behavioral confidence; CTA—German-Cognitive Test Anxiety Scale; FSE—Fear of Experiencing Shame and Embarrassment; CAS—coping with academic stress; RQS—Resillience Questioniare Scale; SSE—Student Self-Efficacy; WBSJ—worries about balancing studies and a job; OC—online competence; CE—clinical experience; CNS—Natural Sciences Competence; ALG—A-level grade; IQR—interquartile range.
Table 3. ANOVA.
Table 3. ANOVA.
CasesSum of SquaresdfMean SquareFpη2ω2
SES X FAS15.07343.76817.391<0.0010.1740.163
Residuals71.7233310.217
SES X ABC20.98245.2464.3230.0020.0500.038
Residuals401.6833311.214
SES X CTA10.15042.5374.4820.0020.0510.040
Residuals187.4003310.566
SES X CAS16.02544.0065.837<0.0010.0660.054
Residuals227.1823310.686
SES X SSE6.66141.6653.9150.0040.0450.034
Residuals140.8033310.425
SES X RQS570.4454142.6112.3720.0520.0280.016
Residuals19,900.79333160.123
SES X WBSJ9.91042.4772.1060.0800.0250.013
Residuals389.4443311.177
SES X FSE5.41041.3531.4570.2150.0170.005
Residuals307.2123310.928
SES X PERF20.76545.1911.0670.3730.0130.0008
Residuals1610.8373314.867
Note: FAS—Financial Anxiety Scale; ABC—academic behavioral confidence; CTA—German-Cognitive Test Anxiety Scale; CAS—coping with academic stress; SSE—Student Self-Efficacy; RQS—Resilience Questionnaire Scale; WBSJ—worries about balancing studies and a job; FSE—Fear of Experiencing Shame and Embarrassment; PERF—Performance.
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Gellisch, M.; Bablok, M.; Brand-Saberi, B.; Schäfer, T. Social Diversity in Focus: Assessing the Impact of Socioeconomic Backgrounds and Work Experience on Psychological Well-Being and Academic Confidence Among German First-Year Medical Students. Educ. Sci. 2024, 14, 1173. https://doi.org/10.3390/educsci14111173

AMA Style

Gellisch M, Bablok M, Brand-Saberi B, Schäfer T. Social Diversity in Focus: Assessing the Impact of Socioeconomic Backgrounds and Work Experience on Psychological Well-Being and Academic Confidence Among German First-Year Medical Students. Education Sciences. 2024; 14(11):1173. https://doi.org/10.3390/educsci14111173

Chicago/Turabian Style

Gellisch, Morris, Martin Bablok, Beate Brand-Saberi, and Thorsten Schäfer. 2024. "Social Diversity in Focus: Assessing the Impact of Socioeconomic Backgrounds and Work Experience on Psychological Well-Being and Academic Confidence Among German First-Year Medical Students" Education Sciences 14, no. 11: 1173. https://doi.org/10.3390/educsci14111173

APA Style

Gellisch, M., Bablok, M., Brand-Saberi, B., & Schäfer, T. (2024). Social Diversity in Focus: Assessing the Impact of Socioeconomic Backgrounds and Work Experience on Psychological Well-Being and Academic Confidence Among German First-Year Medical Students. Education Sciences, 14(11), 1173. https://doi.org/10.3390/educsci14111173

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