In line with the poly-crisis theme addressed by ECER 2026, the increasing prevalence of burnout among pre-service teachers exemplifies how global education systems are simultaneously shaped by interconnected social, economic, and institutional pressures (Henderson, 2025). Burnout extends far beyond an individual psychological issue; it also reflects the  lack of capacity of teacher education systems to provide equitable, inclusive, and sustainable learning environments that prepare future teachers for complex professional realities (Goddard & O'Brien, 2006). When pre-service teachers experience exhaustion, disengagement, or diminished motivation, the implications are wide-ranging—affecting not only their professional identity formation and instructional qualities (Hong, 2010), but also long-term teacher retention, a challenge documented across many countries (Lee et al., 2023).

Particularly in China, there is a growing concern regarding pre-service teacher burnout, especially among pre-service teachers from low socioeconomic status (Low-SES) backgrounds. This specific group of pre-service teachers frequently faces substantial financial strain, often balancing part-time employment with demanding academic and pre-service training requirements (Zhao & Liu, 2019). Rigid program structures, extensive coursework, and high-performance expectations within teacher education universities contribute to sustained pressure (Ran et al., 2012). Social challenges—such as difficulties adapting to unfamiliar institutional cultures or limited peer support—further compound their vulnerability (Zhang, 2010). For Low-SES pre-service teachers, these cumulative burdens may heighten the risk of emotional exhaustion and reduce their confidence and commitment to the teaching profession.

Pressures experienced by the Chinese pre-service teachers illuminate systemic vulnerabilities within China’s teacher education landscape, which seem to mirror broader global challenges. Although in China, national initiatives such as the Publicly Funded Teacher Training Program and the Outstanding Teacher Training Program were introduced with the aim to reinforce teacher quality, expand pathways for disadvantaged pre-service teachers, there is evidence  that burnout has not decreased—and in some cases has intensified—among pre-service teachers (Xie & Xiao, 2022). This suggests an enduring mismatch between the resources these policies aim to provide and the demands actually faced by low-SES participants.

Understanding this mismatch requires conceptual and methodological approaches capable of capturing the interplay between individual characteristics, institutional arrangements, and policy environments. The Job Demands–Resources (JD-R) framework (Bakker, 2011) will be used for examining how financial strain, academic pressure, social exclusion, and family responsibilities (Johnson et al., 2014) interact with personal and institutional resources such as self-efficacy, optimism, belonging and program-level support (Zhang et al., 2010; Ke et al., 2023). Existing research has examined teacher burnout and resilience around the world. However, substantial gaps remain, as past studies in this topic often neglect equity-oriented perspectives, overlooking within-group heterogeneity and the influence of institutional environments on burnout trajectories.

To address these gaps, this study employs a person-centered approach to identify patterns of professional preparation demands and resources among Low-SES pre-service teachers across different programs and universities in China by applying a multilevel latent profile analysis (MLPA). This approach is suitable to reveal institutional and individual factors that co-configure burnout risk.

The research addresses the following questions:

        Q1: What are the profiles of perceived professional preparation demands and resources among Low-SES pre-service teachers across different teacher education programs and universities in China?

       Q2: How can education programs and universities be profiled according to the proportions of profiles of Low-SES pre-service teachers' perceived professional preparation demand and resources?

By situating China’s reforms within global debates on teacher shortages, social inequality, and workforce diversification (UNESCO, 2023; OECD, 2023), this research contributes further insights into how structural inequities shape teacher development. The findings aim to strengthen support for disadvantaged groups in teacher education, enhance psychological well-being and professional commitment, and promote the sustainability of the teaching profession amid intensifying global challenges.

Research Design This study employs a cross-sectional design using quantitative surveys utilizing multilevel latent profile analysis (MLPA) to examine burnout profiles among Low-SES pre-service teachers. Participants The sample includes 2080 Low-SES pre-service teachers from seven teacher education universities in China. These participants belong to three program types: PFTTP, OTTP, and general teacher education programs (see general explanation above). Instruments Six validated measures were used: Perceived professional preparation demands: Stress Scale (Johnson et al., 2014) - 24 items measuring economic, academic, social, and family demands (Cronbach's α > 0.70) Personal Resources: Psychological Capital Scale (Zhang et al., 2010) - 26 items assessing self-efficacy, optimism, and hope (Cronbach's α = 0.90) Perceived professional preparation resource: Institutional support: University Equality and Inclusion Climate Scale (Ke et al., 2023) - 15 items measuring financial assistance, developmental support, and social inclusion (Cronbach's α = 0.931) Inclusion Perception Scale (Janse et al., 2014) and Fairness Perception Scale (Lizzio et al., 2007) - 32 items total assessing belonging, authenticity, and fairness perceptions (Cronbach's α > 0.70) Burnout: College Students' Learning Burnout Scale (Lian, 2005) - 20 items measuring emotional exhaustion, inappropriate behavior, and low achievement (Cronbach's α = 0.865) All instruments use 5-point Likert scales and have demonstrated validity in Chinese contexts. Data Analysis Data were analyzed using Mplus 8.3 (Muthén & Muthén, 1998–2017). First, descriptive statistics and bivariate correlations were used to assess professional preparation demands, resources, and burnout. Two confirmatory factor analyses (CFAs) followed—one for demands and resources, one for burnout outcomes—to generate factor scores for further analysis. Both models fit well (demands/resources: RMSEA = 0.027, CFI = 0.931; burnout: RMSEA = 0.053, CFI = 0.933). Intraclass correlation coefficients (ICCs)> 0.03 indicated significant program-level variance, validating the use of multilevel analysis. A two-level multilevel latent profile analysis (MLPA) was then performed. At the teacher level, models with 1 to 6 profiles were tested using 10 factor indicators; at the university-program level, 1 to 4 profiles were examined based on the distribution of teacher-level profiles. Models were estimated using maximum likelihood with robust standard errors (MLR) and evaluated using AIC, BIC, adjusted BIC, entropy, and the Lo-Mendell-Rubin test (Li et al.,2025). Finally, demographic covariates such as grade, gender, and place of residence were added, along with burnout outcomes. The R3STEP procedure was chosen to test links between covariates and teacher-level profiles. The BCH method was opted to compare burnout across profiles, with Bonferroni adjustment. At the program level, logistic regression was performed to assess the impact of university type, and model constraints tested burnout differences.

We identified four distinct profiles at the teacher level with a three-tier classification using ±0.5 as a threshold is advised. The high-level range (≥ 0.5) indicates a positive state, suggesting abundant resources or strong demand. The medium level (-0.5 to 0.5) indicates a neutral state and a balance between resources and demands. The low level (<- 0.5) indicates a negative state with severe resource scarcity or low demand. A 0.5 cutoff matches the typical interpretation of standardized data (such as Z-scores) and clearly separates trends, making high, medium, and low levels distinct both statistically and in practice. Low-resource, medium-demand profile (profile 1) accounts for 17.40% of the total sample. This profile scores lowest on all resource dimensions, while scoring moderately high on several demand dimensions. This combination of relatively high demands and low resources exposes this group to high risk. Medium-resource, medium-demand profile (profile 2) accounts for 26.87%. This profile generally scores at medium levels across all demand dimensions, with particularly low demand in finance. Although their resource score is not outstanding, their medium demand remains relatively stable. Medium-resource, high-demand profile (profile 3) accounts for 11.87%. This profile has the highest scores in demand dimensions. It also scores medium in several resource dimensions. This suggests that while this group has protective mechanisms, they still face high pressure. The high-resource, low-demand profile (profile 4), comprising 43.85%, is the most represented group. This profile is high in resources and scores lowest in demand dimensions, which suggest that pre-service teachers in this group are well-supported and face relatively fewer challenges in their environment. At the university program level, two distinct climates are identified: one showing a diverse profile distribution and another demonstrating a concentrated, high-resource, low-demand profile. Burnout Differentiation Expected findings indicate significant variations in burnout across profiles, with the high-resource, high-demand profile showing the highest burnout levels (M = 0.661). The medium-resource, low-demand profile exhibit the lowest burnout (M = -0.463). Demographic Patterns Female, rural, and higher-grade students are found to face greater resource constraints and a higher risk of burnout. Male, urban, and lower-grade students are likely to occupy more favorable resource configurations. Our findings seem to support gender, urban vs rural, and education grade gaps related to demands, resources, and wellbeing. Theoretical and Practical Implications Our findings challenge obvious assumptions about the relationship between resources and burnout, demonstrating that high resource levels do not automatically prevent burnout when demands are elevated. The results can inform tailored support strategies: immediate intervention for high-demand groups, resource enhancement for low-resource profiles, and demand management for medium-resource, high-demand individuals, whenever possible. The study extends the applicability of the JD-R model by revealing heterogeneous patterns within disadvantaged teacher populations. It provides evidence-based guidance for policy refinement in Chinese teacher education, with implications for international equity initiatives in teacher preparation.

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