The sources of suicidal ambivalence: favorable and risk factors for suicidal ideation among higher vocational college freshmen-a large sample survey.

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Learn more: PMC Disclaimer | PMC Copyright Notice BMC Psychiatry . 2026 Mar 14;26:301. doi: 10.1186/s12888-026-07963-9 Search in PMC Search in PubMed View in NLM Catalog Add to search The sources of suicidal ambivalence: favorable and risk factors for suicidal ideation among higher vocational college freshmen–a large sample survey Xi Lu Xi Lu 1 Hangzhou Polytechnic University, Hangzhou, Zhejiang 314423 China Find articles by Xi Lu 1 , Kee Jiar Yeo Kee Jiar Yeo 2 School of Education, Universiti Teknologi Malaysia, Johor Bahru, Johor 81310 Malaysia Find articles by Kee Jiar Yeo 2 , Ou Wu Ou Wu 3 Shulan International Medical College, Zhejiang Shuren University, Hangzhou, Zhejiang 310009 China 4 Zhejiang Chinese Medical University, Hangzhou, Zhejiang 310053 China Find articles by Ou Wu 3, 4, ✉ Author information Article notes Copyright and License information 1 Hangzhou Polytechnic University, Hangzhou, Zhejiang 314423 China 2 School of Education, Universiti Teknologi Malaysia, Johor Bahru, Johor 81310 Malaysia 3 Shulan International Medical College, Zhejiang Shuren University, Hangzhou, Zhejiang 310009 China 4 Zhejiang Chinese Medical University, Hangzhou, Zhejiang 310053 China ✉ Corresponding author. Received 2025 Apr 24; Accepted 2026 Mar 5; Collection date 2026. © The Author(s) 2026 Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/ . PMC Copyright notice PMCID: PMC13072652  PMID: 41832414 Abstract Background Suicidal ideation (SI) is a critical public health issue, particularly among college freshmen navigating significant life transitions. Higher vocational college students in China represent an understudied population facing unique academic, social, and employment pressures, which may elevate their suicide risk. This study aims to identify the prevalence of SI and investigate its psychological determinants within this specific group, with a focus on constructs derived from a revised mental health screening instrument. Methods A large-scale, cross-sectional survey was conducted among 5,028 first-year students at a higher vocational college in Eastern China. The University Personality Inventory (UPI) was analyzed using exploratory and confirmatory factor analyses (EFA/CFA) to derive a revised five-factor model. Structural equation modeling (SEM), hierarchical modeling, and bifactor modeling were employed to examine associations between psychological traits and SI. Results A new five-factor structure of the UPI was identified: “social avoidance”, “emotional vulnerability”, “physical symptoms”, “dependence with cognitive symptoms”, and “interpersonal sensitivity”. SEM revealed that “social avoidance” (β = 0.951) and “emotional vulnerability” (β = 0.511) were positively associated with SI. “Dependence with cognitive symptoms” (β = −0.351) and “interpersonal sensitivity” (β = −0.256) showed negative direct associations but positive indirect associations via mediating pathways. “Physical symptoms” were indirectly associated with SI through social avoidance and emotional vulnerability. The model explained 72.8% of the variance in SI. The 12-month prevalence of SI was 3.7%. Gender was a significant covariate, with females(5.3%) reporting higher SI rates than male(2.7%); age was not statistically significant. Conclusions The study presents a validated, shortened UPI scale tailored for higher vocational students. “Social avoidance” and “emotional vulnerability” emerged as key correlates of SI among vocational college freshmen. While “dependence with cognitive symptoms” and “interpersonal sensitivity” may serve as protective traits in some contexts, their indirect associations suggest complex psychological pathways. Interventions aimed at reducing SI in this population should focus on enhancing social connectedness, building emotional resilience, and fostering supportive environments that can leverage potential protective traits. Given the cross-sectional design, findings should be interpreted as correlational. Future longitudinal studies are needed to clarify causal mechanisms and inform targeted interventions. Supplementary Information The online version contains supplementary material available at 10.1186/s12888-026-07963-9. Keywords: Suicidal ideation, Higher vocational college students, University Personality Inventory (UPI), Social avoidance, Emotional vulnerability, Psychological factors, Structural equation modeling, Bifactor analysis Introduction In 2019, more than 700,000 people died by suicide, which accounted for one in every 100 deaths globally [ 1 ]. Suicide is the third leading cause of death for young people aged 15 to 29 worldwide [ 1 – 3 ]. Suicidal ideation (SI), as a major risk factor for suicide death [ 4 ], is a serious public health issue that affects many university students in the world [ 5 , 6 ]. College freshmen are particularly vulnerable to SI due to the transition from high school to college life, which involves academic, social, and personal challenges [ 7 – 9 ]. The prevalence of SI among college students is a pressing concern, particularly in the context of higher vocational colleges in Eastern China [ 10 ]. Moreover, at that time, the coronavirus disease 2019 (COVID-19) pandemic had added more stress and uncertainty to this population [ 11 – 13 ]. The influencing factors of SI occurrence are not entirely the same across different populations [ 14 , 15 ]. Several theories were established to elucidate the factors influencing SI across diverse groups, providing a multifaceted perspective on this complex issue. Interpersonal Theory of Suicide (IPTS), developed by Thomas Joiner, identifies perceived burdensomeness and thwarted belongingness as key precursors to suicidal ideation [ 16 , 17 ]. Integrated Motivational-Volitional Model (IMV), proposed by Rory O’Connor, emphasizes the role of defeat and entrapment as motivational factors that lead to suicidal thoughts [ 18 , 19 ]. Three-Step Theory (3ST), developed by E. David Klonsky and Alexis May, posits that suicidal ideation results from a combination of pain and hopelessness [ 20 , 21 ]. Understanding the risk factors for SI among university students is essential for developing targeted interventions. Several kinds of factors influence SI among university students have been found [ 22 ]. In this population, psychological factors such as depression, anxiety, and psychological distress are significant predictors of SI [ 22 – 27 ]. Sociodemographic factors, including gender, academic performance and year of study, also influence suicidal thoughts [ 6 , 28 – 30 ]. Social and environmental factors like feelings of isolation, homesickness, and lack of social support, which have been intensified by the COVID-19 pandemic, contribute to SI [ 31 – 35 ]. Substance abuse has been linked to higher rates of suicidal ideation among university students [ 36 – 39 ]. Cultural and attitudinal factors, including attitudes towards suicide and the perceived value of life, also play a role [ 22 , 40 , 41 ]. Despite extensive research, there remains a gap in understanding the unique predictors of SI among higher vocational college freshmen in Eastern China. Our previous report [ 42 ] (Wu et al., 2022) provided an early, single-campus prevalence estimate (5.2%) for SI among vocational-college freshmen, and the SI influencing risk factors using the 2015 five-factor UPI scoring key that had originally been derived from four-year university students in South-Central China (Zhang et al., 2015) [ 43 ]. Current understanding of SI emphasizes the multifaceted nature of mental health challenges, including factors such as emotional vulnerability and social avoidance [ 23 , 44 ]. Building on the theoretical foundations mentioned above, this study focuses specifically on two psychological constructs—social avoidance and emotional vulnerability—as primary predictors of suicidal ideation among higher vocational college freshmen. These constructs are deeply embedded in the core mechanisms described by IPTS, IMV and 3ST. For instance, social avoidance undermines the sense of belongingness and increases isolation, aligning with IPTS’s concept of thwarted belongingness. Emotional vulnerability, characterized by heightened sensitivity to stress and poor emotional regulation, contributes to psychological pain and perceived entrapment, as emphasized in both IMV and 3ST. Moreover, the transitional nature of college life, especially in vocational settings, amplifies the relevance of these factors. Students entering higher vocational colleges often face unique stressors—such as limited academic pathways, uncertain career prospects, and reduced social integration—which may intensify tendencies toward social withdrawal and emotional instability [ 45 , 46 ]. By focusing on these two predictors, the study aims to construct a targeted and theoretically grounded framework for understanding SI in this specific population. To clarify the theoretical foundation of this study, we constructed our conceptual framework by integrating key constructs from three major suicide theories: IPTS, IMV, and 3ST. Specifically, “social avoidance” was modeled in alignment with IPTS’s concept of thwarted belongingness, reflecting the role of diminished social connectedness in suicidal ideation. “Emotional vulnerability” was derived from IMV’s emphasis on entrapment and 3ST’s focus on psychological pain, both of which underscore the impact of emotional dysregulation and perceived helplessness. These theoretical insights directly informed our hypotheses and guided the selection of latent variables in the structural equation model (SEM), which was designed to test both direct and indirect pathways to suicidal ideation. By synthesizing these frameworks, our study offers a comprehensive and theory-driven approach to understanding suicidal ideation among higher vocational college freshmen. Higher vocational colleges in China, offering three-year programs to cultivate technicians and skilled workers, play a crucial role in the nation’s higher education and economic development [ 42 , 47 , 48 ]. Although graduates are eligible for associate degrees, ensuring a supply of technical talent, recent global challenges like the COVID-19 pandemic have increased employment difficulties for these students, particularly affecting freshmen [ 42 , 47 , 48 ]. However, the study of suicidal ideation and related factors among these students, especially newcomers, remains largely unexplored [ 42 , 47 , 48 ]. Although the claim that higher vocational college freshmen are under-studied is supported by the limited literature available, few studies have explicitly compared mental health outcomes between students in traditional four-year universities and those in three-year vocational colleges [ 45 , 49 ]. This distinction is meaningful due to several structural and psychosocial differences. Vocational colleges in China typically emphasize technical training and practical skills, often attracting students with different academic backgrounds, socioeconomic profiles, and career expectations compared to their university counterparts. These students may face greater employment uncertainty, lower perceived social status, and fewer academic resources, all of which can contribute to heightened psychological stress and vulnerability to suicidal ideation [ 45 , 50 ]. Prior research has shown that university students tend to report higher levels of academic pressure and performance anxiety, while vocational students are more likely to experience stress related to job prospects and social integration [ 51 – 53 ]. Moreover, vocational college environments may offer less structured mental health support, and students may have fewer opportunities to engage in campus-wide social activities, increasing the risk of social avoidance and emotional vulnerability [ 54 ]. These contextual differences underscore the importance of examining suicidal ideation within vocational settings as a distinct phenomenon, rather than extrapolating findings from university populations [ 55 ]. The University Personality Inventory (UPI), a mental health screening tool widely used in East Asia, assesses various symptoms and personality traits associated with mental health, including those linked to SI [ 56 ]. It has also been extensively utilized in Chinese universities as a rapid and effective mental health screening tool [ 42 , 43 , 57 , 58 ]. However, recent findings indicate that the UPI’s accuracy may decrease for individuals with very low or very high stress levels, though it is effective for evaluating mild to moderate mental health issues [ 56 ]. And the UPI has been shown to have different factor structures based on different samples and methods of analysis [ 42 , 43 , 57 – 59 ]. Using the five-factor framework(Physical symptoms, Social avoidance, Cognitive symptoms, Interpersonal sensitivity, Emotional vulnerability) of UPI, the previous study discovered useful and insightful results, indicating that social avoidance and emotional vulnerability showed significant associations with SI in March 2020, when COVID-19 was in its early stages [ 42 ]. However, this five-factor framework of UPI is based on the situation of freshmen at a four-year university in South China in 2016 [ 42 , 43 ]. Considering that higher vocational colleges offer only three years of study and have different educational goals to some extent, the results may differ from those derived from the later situation of freshmen at three-year higher vocational colleges in East China [ 48 ]. Implementing targeted interventions based on the specific influencing factors of SI for subdivided groups is already a prevailing trend. Therefore, prior to conducting the exploratory and confirmatory factor analyses and the structural equation modeling (SEM) analysis in this study, a conceptual framework was developed (Fig. 1 ) based on prior research and established psychological theories [ 42 , 43 ]. The primary aim of this study is to identify a psychological model of SI based on a large sample of 5028 freshmen in higher vocational colleges, and then investigated the unique influencing factors of SI for freshmen in three-year higher vocational colleges accordingly. Fig. 1. Open in a new tab The conceptional framework It is important to emphasize that the constructs ultimately examined, though grounded in theory, were derived empirically: an exploratory factor analysis (EFA) of the University Personality Inventory (UPI) in our large sample of higher-vocational freshmen yielded a five-factor structure, two of which could be interpreted as social avoidance and emotional vulnerability (Fig. 2 ). Only after this data-driven identification did we note their striking conceptual overlap with core mechanisms proposed by the Interpersonal Theory of Suicide (IPTS: thwarted belongingness), the Integrated Motivational-Volitional Model (IMV: entrapment + emotional dysregulation), and the Three-Step Theory (3ST: psychological pain). Consequently, the present study uses a retroductive strategy: the factors were discovered inductively, and their theoretical meaning was subsequently aligned with existing suicide models to build the structural equation model and interpret the pathways. This approach preserves the exploratory integrity of the factor analysis while allowing a theoretically informed discussion of why these particular factors might predict suicidal ideation. Fig. 2. Open in a new tab Structural equation model (SEM) of the relationships between the five UPI factors (F1–F5), covariates (gender, age), and suicidal ideation (SI) ( n = 5028). Standardized path coefficients (β) are shown. Solid lines indicate significant paths ( P < 0.01), while the dotted line from F2 to SI indicates a non-significant path. F1: Social avoidance; F2: Physical symptoms; F3: Dependence with cognitive symptoms; F4: Emotional vulnerability; F5: Interpersonal sensitivity. The model explains 72.8% of the variance in SI. For clarity, item indicators (u1–u60) and their loadings are not displayed in this figure; detailed item-factor loadings are presented in Table 4 The present work extends—rather than duplicates—that analysis of our previous study in three ways. First, we recruited 5 028 first-year students from a different higher-vocational college in Eastern China, thereby testing the robustness of earlier findings in a new institutional context with a markedly larger sample. Second, instead of re-applying the 2015 key, we re-derived the UPI factor structure via exploratory and confirmatory analyses on three random split-half subsamples, yielding a new five-factor model with 20 items removed and 10 new loadings. Crucially, the naming and conceptual framing of all new emergent factors, including “social avoidance”, “emotional vulnerability”, “physical symptoms”, “dependence with cognitive symptoms”, and “interpersonal sensitivity”, were explicitly anchored in the three dominant contemporary suicide theories: the Interpersonal Theory of Suicide (IPTS), the Integrated Motivational-Volitional Model (IMV), and the Three-Step Theory (3ST). Only after this retroductive alignment did we embed the factors in a structural equation model that explains 72.8% of the variance in suicidal ideation while revealing nuanced direct and indirect pathways not detectable in the earlier bivariate design. Methods Participants This study was conducted in October 2021. An online questionnaire was distributed to all of the first-year students (5028 participants) of this higher vocational college in Hangzhou city, Zhejiang Province, Eastern China. All these higher vocational college freshmen finished senior high school (Grade 10–12) [ 48 ]. Embedded as a standing component of the college’s orientation program and framed as a resource that lets students voice their experiences while shaping future support services, the survey offers every incoming freshman a timely opportunity for self-reflection and connection—an approach that once again engaged all 5,028 students (2,964 males and 2,064 females) this year, resulting in an effective response rate of 100.00%. Demographic details such as age and sex were sourced from the participants” self-reported questionnaires. The 5028 students were randomly divided into three subgroups, including Sample-1( n = 1617; 986 males and 690 females), Sample-2( n = 1677; 998 males and 697 females) and Sample-3( n = 1675; 980 males and 695 females). Ethical considerations This study was approved by the Human Research Ethics Committee of the local institute and adhered to the Declaration of Helsinki. The survey was conducted in collaboration with the administrative and student affairs departments. UPI was provided for the first-year students to self-assess their own mental health status. Participation was framed as part of routine health and wellness onboarding, which helped normalize the process, reduce stigma, and contribute to the improvement of campus support services. Recruitment information and access to the survey were provided through the online learning platform, where students were informed about the study purpose, the anonymous and voluntary nature of participation, and the estimated time commitment. Prior to accessing the questionnaire, all participants viewed an electronic informed‑consent screen and were required to select “Agree” to proceed. Students are asked to read the instructions, focusing on “items that they have often felt and experienced in the last year” and are asked to complete them independently. Measures The University Personality Inventory (UPI) was first developed in 1966 by the Japan National Association of University Health Service Centers to assess mental health status (Hirayama, 2011) [ 60 ]. It was later translated into Chinese and has since been widely used to evaluate the mental health of college students in China (Fan, 1993) [ 61 ]. The measurement of SI and related risk factors with online investigation were set as previously reported and shown in Table 1 [ 42 ]. As previously described, the primary UPI contains 56 items that describe whether an individual experienced one or more mental health symptoms and four “lie” scales: items 5, 20, 35, and 50 [ 42 , 58 ] (see Supplementary File S1 ). Table 1. The measurement of SI and related risk factors among the higher vocational college freshmen Measure Description University Personality Inventory (UPI) A 60-item self-report questionnaire that measures various symptoms and personality traits related to mental health, such as depression, anxiety, neuroticism, persecutory beliefs, obsessive-compulsive symptoms, physical symptoms, cognitive symptoms, emotional vulnerability, social avoidance, and interpersonal sensitivity. For each item, a score of 1 is given for “Yes”, and 0 was given for “No”. The higher the score, the poorer the mental and/or physical condition. Suicidal ideation Measured by one item response to “Have idea of wanting to die?” (item 25) in the UPI. Participants responding “yes” were considered to have suicidal ideation. Demographic data Obtained from a self-report questionnaire item that asked for age and sex. Open in a new tab Data management and analysis Based on tetrachoric correlation coefficients, an Exploratory factor analysis (EFA) for binary indicators was conducted with promax rotation to analyze the underlying structure of the UPI in Sample-1 [ 58 ]. Because previous studies showed inter-factor correlations in the factor structure of the UPI, we used promax rotation, which allows the factors to be correlated [ 58 ]. Factor loadings were then examined for the optimal model of the 55 items (items 5, 20, 25,35, and 50 excluded). The factor model was selected after deleting items with loadings lower than 0.40 on all factors [ 62 ] or cross loadings as well as theoretical interpretations (for details see Reference 3 and 4 [ 63 , 64 ]). Confirmatory factor analysis (CFA) was conducted to verify the dimensions in Sample-2 and Sample-3. Three practical fit indices were used to evaluate the model fit: the root mean square error of approximation (RMSEA), Tucker–Lewis index(TLI) and the comparative fit index (CFI) [ 58 ]. A TLI and CFI close to 1 indicate a good fit. An RMSEA < 0.05 indicates good fit [ 58 ]. And the internal consistency reliability was also tested. The data analysis was performed using Mplus 8.0 [ 65 , 66 ] and SPSS for Windows V22.0 (IBM Corp., Armonk, NY, USA) [ 42 ]. A structure equation modeling (SEM) was specified to test the conceptual framework. The predictor variables were correlated, as were the residuals for the outcome variable [ 65 ]. The model was specified and tested using Mplus 8.0 [ 65 , 66 ] with robust maximum likelihood estimation [ 67 ]. The results are reported as unstandardized (B) and standardized (β) regression coefficients, and the R-squared was reported for the outcome variable. Retroductive analytical strategy: from empirical factor discovery to theory alignment To ensure that the theoretical interpretation of the University Personality Inventory (UPI) factors did not bias the empirical identification of the latent structure, this study adopted a retroductive analytical strategy. Retroductive reasoning integrates inductive, data driven discovery with theory guided interpretation, allowing empirically derived constructs to be meaningfully situated within established psychological frameworks. Step 1: Inductive empirical identification of UPI factors The five factor structure was first identified purely through exploratory factor analysis (EFA) using tetrachoric correlations and promax rotation in Sample 1, without imposing any theoretical constraints or pre specified factor labels. Items with low or cross loadings were removed based solely on statistical criteria. The resulting structure was then validated through confirmatory factor analysis (CFA) in two independent subsamples (Sample 2 and Sample 3), demonstrating excellent model fit. This inductive process ensured that the factor structure reflected the psychological patterns present in higher vocational college freshmen, independent of prior theoretical assumptions. Step 2: Retroductive theoretical mapping Only after the empirical factor structure had been established did we apply a retroductive reasoning process to interpret the psychological meaning of each factor. Specifically, we examined how the emergent factors aligned with core constructs from three major suicide theories: the Interpersonal Theory of Suicide (IPTS), the Integrated Motivational–Volitional Model (IMV), and the Three Step Theory (3ST). This mapping process involved identifying conceptual overlaps between the empirically derived factors—such as social withdrawal, emotional instability, or cognitive dependence—and theoretical mechanisms such as thwarted belongingness, entrapment, psychological pain, and hopelessness. Step 3: Conceptual alignment matrix To enhance transparency and strengthen the theoretical grounding of the study, Table 2 presents a conceptual alignment matrix linking each UPI factor to the corresponding constructs in IPTS, IMV, and 3ST. This retroductive approach preserves the exploratory integrity of the factor analyses while enabling a theory driven interpretation of the pathways leading to suicidal ideation. Table 2. Mapping of the revised UPI five-factor structure to constructs in IPTS, IMV, and 3ST UPI Factor Empirical Definition IPTS IMV 3ST Interpretation Social avoidance Withdrawal from social interaction; pessimism; distrust; reduced interpersonal engagement Thwarted belongingness Social disconnection contributing to defeat/entrapment Reduced connectedness increasing psychological pain Core interpersonal pathway to suicidal ideation Emotional vulnerability Emotional instability; worry; irritability; poor affect regulation — Entrapment; motivational phase Psychological pain and hopelessness Affective pathway central to SI development Physical symptoms Somatic distress; fatigue; sleep problems — Contributes to defeat/entrapment Amplifies psychological pain Indirect risk via emotional vulnerability and social avoidance Dependence with cognitive symptoms Reliance on others; indecision; reduced autonomy; cognitive inefficiency Possible perceived burdensomeness Defeat; low perceived control — Dual role: may promote help-seeking or increase vulnerability Interpersonal sensitivity Fear of rejection; heightened self-consciousness; concern about others’ evaluations — Heightened threat perception — Dual role: may enhance social vigilance or exacerbate distress Open in a new tab Note: IPTS = Interpersonal Theory of Suicide; IMV = Integrated Motivational–Volitional Model; 3ST = Three-Step Theory; SI = suicidal ideation; Blank cells indicate no direct theoretical correspondence between the UPI factor and constructs in the respective suicide theory Results The mean (± standard deviation) age of the study participants was 18.43 ± 0.894 years (sample-1: 18.43 ± 0.895; sample-2: 18.43 ± 0.919 ; sample-3: 18.41 ± 0.919). Exploratory factor analysis EFA with promax rotation was applied in sample-1 ( n = 1617) to explore the dimensions of the inventory, based on tetrachoric correlation coefficients [ 43 ]. The EFA with categorical variables yielded five factors in sample-1. Table 3 presents the rotated factor loadings for this new five-factor model. 15 items were deleted due to low loadings and their cross loadings:3, 7, 8, 9, 12, 21, 31, 32, 36, 37, 42, 45, 47, 53 and 54. The remaining items were marked in bold (Table 3 ). Table 3. Exploratory factor analysis (EFA) results for the university personality inventory (UPI) in sample 1 (final factor loadings for retained items) Item Description F1: Social Avoidance F2: Physical Symptoms F3: Dependence with Cognitive Symptoms F4: Emotional Vulnerability F5: Interpersonal Sensitivity u1 Poor appetite – 0.600 – – – u2 Feel sick, stomachache – 0.868 – – – u4 Care about palpitation and pulse – 0.467 – – – u6 Full of dissatisfaction and complaints – – – 0.531 – u10 Do not like meeting others 0.642 – – – – u11 Feel that I am not myself 0.525 – – – – u12 Lack of enthusiasm and positivity 0.630# – 0.504# – – u13 Pessimistic 0.556 – – – – u14 Distracted – – 0.513 – – u15 Over-uneven in emotion – – – 0.658 – u16 Frequent insomnia – 0.478 – – – u17 Headache – 0.864 – – – u18 Ache in neck and shoulder – 0.701 – – – u19 Chest pain or feel oppressed – 0.773 – – – u22 Inclined to worry – – – 0.491 – u23 Restless – – – 0.522 – u24 Irritable – – – 0.773 – u26 No interest in anything 0.654 – – – – u27 Declining memory – – 0.543 – – u28 Lack of patience – – 0.636 – – u29 Lack of judgment – – 0.864 – – u30 Too dependent on others – – 0.729 – – u33 Feel hot and cold – 0.622 – – – u38 Lack of confidence – – 0.683 – – u39 Irresolute about anything – – 0.706 – – u40 Easily feel misunderstood – – – – 0.436 u41 Lack faith in others 0.600 – – – – u43 Unwilling to associate with others 0.683 – – – – u44 Feel self-abased – – 0.502 – – u46 Physically exhausted – 0.558 – – – u48 Dizzy when I stand up – 0.605 – – – u49 Have ever lost consciousness, cramp – 0.811 – – – u51 Over-rigid – – 0.457 – – u52 Cannot give up repeating things – – 0.439 – – u55 Sense weird smell from myself – – – – 0.400 u56 Suspect others say something bad about me – – – – 0.633 u57 Wary of others – – – – 0.933 u58 Care about others’ gaze – – – – 0.903 u59 Feel others despise me – – – – 0.651 u60 Sensitive emotions – – – – 0.417 Open in a new tab Notes: Only factor loadings ≥ 0.40 are shown and bolded; Items with loadings < 0.40 are indicated by “–”; Items not retained in the final factor structure are omitted for clarity; #: cross loadings; UPI = University Personality Inventory, EFA = Exploratory Factor Analysis; ux denotes the x-th item in the UPI, where u1corresponds to Item 1 (‘Poor appetite’) Validity by confirmatory factor analysis The structural validity of the new-five-factor UPI found by EFA in Sample-1 was verified by confirmatory factor analysis (CFA). After excluding the 15 items with low loadings and cross loadings, CFA was conducted on the new-five-factor model with the remaining 40 items in Sample-2(CFA1) and Sample-3(CFA2). The factor loadings for the new-five-factor model are shown in Table 4 . Intercorrelations between the five factors in the new-five-factor model ranged from 0.313 to 0.884. The confirmatory factor analysis on the validation data suggested good model fit for the new-five-factor model (for Sample-2, RMSEA = 0.028, CFI1 = 0.979, TLI1 = 0.978; for Sample-3 RMSEA = 0.030, CFI2 = 0.974, TLI2 = 0.972) (see Table 4 ). Table 4. Factor loadings, factor correlation and fit indices of CFA model Item CFA1 (sample-2) CFA2 (sample-3) Factor 1 10 Do not like meeting others 0.804 0.754 11 Feel that I am not myself 0.796 0.796 13 Pessimistic 0.876 0.858 26 No interest in anything 0.731 0.719 41 Lack faith in others 0.858 0.811 43 Unwilling to associate with others 0.797 0.777 Factor 2 1 Poor appetite 0.611 0.529 2 Feel sick, stomachache 0.728 0.707 4 Care about palpitation and pulse 0.457 0.559 16 Frequent insomnia 0.71 0.744 17 Headache 0.762 0.759 18 Ache in neck and shoulder 0.631 0.654 19 Chest pain or feel oppressed 0.756 0.756 33 Feel hot and cold 0.74 0.699 46 Physically exhausted 0.927 0.904 48 Dizzy when I stand up 0.676 0.698 49 Have ever lost consciousness, cramp 0.673 0.636 Factor 3 14 Distracted 0.832 0.842 27 Declining memory 0.773 0.785 28 Lack of patience 0.813 0.825 29 Lack of judgment 0.844 0.823 30 Too dependent on others 0.775 0.782 38 Lack of confidence 0.858 0.875 39 Irresolute about anything 0.848 0.815 44 Feel self-abased 0.877 0.848 51 Over-rigid 0.742 0.757 52 Cannot give up repeating things 0.856 0.812 Factor 4 6 Full of dissatisfaction and complaints 0.724 0.732 15 Over-uneven in emotion 0.863 0.889 22 Inclined to worry 0.668 0.662 23 Restless 0.906 0.912 24 Irritable 0.781 0.831 Factor 5 34 Concern about urination or sexual organs 0.493 0.507 40 Easily feel misunderstood 0.839 0.822 55 Sense weird smell from myself 0.602 0.696 56 Suspect others say something bad about me 0.73 0.834 57 Wary of others 0.796 0.777 58 Care about others” gaze 0.84 0.851 59 Feel others despise me 0.808 0.880 60 Sensitive emotions 0.856 0.881 Factor correlation Factor 2 WITH Factor 1 0.777 0.784 Factor 3 WITH Factor 1 0.863 0.884 Factor 2 0.748 0.741 Factor 4 WITH Factor 1 0.819 0.847 Factor 2 0.824 0.807 Factor 3 0.841 0.835 Factor 5 WITH Factor 1 0.803 Factor 2 0.711 0.837 Factor 3 0.847 0.724 Factor 4 0.884 0.835 Model fit RMSEA 0.028 0.030 90% C.I. (0.026,0.030) (0.028,0.031) CFI 0.979 0.974 TLI 0.978 0.972 Open in a new tab Note: RMSEA, root mean square error of approximatin; CFI, comparative fit index; TLI, Tucker–Lewis index; The internal consistency reliability The test for the internal consistency reliability of the new-five-factor model (Table 5 ) was done on the total 5028 college students. The alpha coefficients for the new-five factor UPI scale were 0.775 for Factor-1, 0.769 for Factor-2, 0.882 for Factor-3,0.754 for Factor-4, 0.711 for Factor-5 and 0.841 for this overall UPI scale. Table 5. Revised five-factor structure of the University Personality Inventory (UPI) in this study (vs. previous study): Item composition and theoretical interpretations Factor Item Numbers Item Descriptions Theoretical Interpretation F1. Social Avoidance 10, 11, 13, 26, 41, 43(while, Item10,11,41,43 were included in this factor in the previous study) “Do not like meeting others”, Feel that I am not myself”, Pessimistic”, No interest in anything”, Lack faith in others”, Unwilling to associate with others Reflects withdrawal from social interaction and reduced interpersonal engagement. F2. Physical Symptoms 1, 2, 4, 16, 17, 18, 19, 33, 46, 48, 49(while, Item 1,2,3,17,18,46,48,49 were included in this factor in the previous study) “Poor appetite”, Feel sick, stomachache”, Care about palpitation and pulse”, Frequent insomnia”, Headache”, Ache in neck and shoulder”, Chest pain or feel oppressed”, Feel hot and cold”, Physically exhausted”, Dizzy when I stand up”, Have ever lost consciousness, cramp Captures somatic complaints and fatigue-related distress. F3. Dependence with Cognitive Symptoms 14, 27, 28, 29, 30, 38, 39, 44, 51, 52(while, Item 29,30,38,39 were included in this factor of “Cognitive symptoms” in the previous study) “Distracted”, Declining memory”, Lack of patience”, Lack of judgment”, Too dependent on others”, Lack of confidence”, Irresolute about anything”, Feel self-abased”, Over-rigid”, Cannot give up repeating things Indicates interpersonal reliance and reduced autonomy. F4. Emotional Vulnerability 6, 15, 22, 23, 24(while, Item 6,15,21,24,28,60 were included in this factor in the previous study “Full of dissatisfaction and complaints”, Over-uneven in emotion”, Inclined to worry”, Restless”, Irritable Reflects emotional instability and poor regulation. F5. Interpersonal Sensitivity 34, 40, 55, 56, 57, 58, 59, 60(while, Item 57,58 were included in this factor in the previous study) “Concern about urination or sexual organs”, Easily feel misunderstood”, Sense weird smell from myself”, Suspect others say something bad about me”, Wary of others”, Care about others’ gaze”, Feel others despise me”, Sensitive emotions Captures heightened sensitivity to perceived social rejection or criticism. Open in a new tab The conceptional framework in this study Based on the information from the samples of this study and the results of our EFA and CFA, we derived five factors from the UPI(Fig. 2 ). Each factor was subsequently aligned with core constructs from three dominant suicide theories—IPTS, IMV, and 3ST—using a retroductive approach. Below are the definitions and theoretical anchors for each factor: “Social avoidance”(F1), “Physical symptoms”(F2), “Dependence with cognitive symptoms”(F3), “Emotional vulnerability”(F4), and “Interpersonal sensitivity”(F5). F1 is defined as withdrawal from social interaction and reduced interpersonal engagement, marked by pessimism, lack of interest in social engagement, and distrust of others. It reflects thwarted belongingness in IPTS and contributes to social isolation, a known precursor to suicidal ideation. Captures somatic complaints and fatigue-related distress. F2 is defined as somatic complaints including fatigue, headaches, and sleep disturbances, often indicative of underlying psychological distress. While not directly tied to suicide theories, it mediates effects through emotional vulnerability and social avoidance. F3 is defined as interpersonal reliance and reduced autonomy with cognitive impairments such as indecisiveness, poor memory, and lack of autonomy. It may reflect perceived burdensomeness (IPTS) or defeat (IMV), but also shows potential protective effects through help-seeking behavior and social reliance. F4 is defined as emotional instability and poor regulation, exemplified by heightened sensitivity to emotional stimuli, poor regulation of affect, and susceptibility to psychological pain. It aligns with entrapment in IMV and psychological pain in 3ST, both central mechanisms in the development of suicidal ideation.F5 is defined as heightened sensitivity to perceived social rejection or criticism, i.e., heightened awareness and concern about others’ perceptions, often accompanied by self-critical thoughts and fear of rejection. It may buffer suicidal ideation by fostering social connectedness, but can also exacerbate distress when coupled with low self-esteem or perceived rejection. All items comprising the five factors are presented in Table 5 [ 42 ] . Structural equational modeling for suicide ideation The SEM model specified in Fig. 2 was estimated and show a good model fit with CFI of 0.963, TLI of 0.961, RMSEA of 0.035 and Standardized Root Mean Square Residual (SRMR) of 0.101 (Fig. 2 ). The finding indicated the model explained 72.8% variance of the suicide ideation. In terms of path coefficients, “social avoidance” (β = 0.951, P < 0.01) and “emotional vulnerability”(β = 0.511, P < 0.01) showed a significant positive direct association with suicide ideation, which indicated that the higher level of social avoidance and emotional vulnerability, the stronger level of suicide ideation. “Dependence with cognitive symptomklfs” (β = -0.351, P < 0.01) and “Interpersonal sensitivity” (β = -0.256, P < 0.01) had significantly negative direct association with suicide ideation. There were no direct association from physical symptoms to suicidal ideation, but there were positive indirect association via ‘social avoidance’ (β = 0.243, P < 0.01) and ‘emotional vulnerability’ (β = 0.309, P < 0.01). There were also positive indirect associations from “Dependence with cognitive symptoms” and “Interpersonal sensitivity” to suicide ideation via “social avoidance” and “emotional vulnerability”, respectively (see Fig. 2 . And Table 6 ). Table 6. Path coefficients of the SEM model Two-Tailed Estimate S.E. Est./S.E. P-Value F1       ON F2 0.243 0.030 8.140 0.000 F3 0.503 0.036 14.159 0.000 F5 0.214 0.034 6.217 0.000 F4       ON F2 0.309 0.029 10.805 0.000 F3 0.260 0.034 7.661 0.000 F5 0.418 0.032 12.941 0.000 SI       ON F1 0.951 0.091 10.486 0.000 F2 -0.054 0.077 -0.703 0.482 F4 0.511 0.120 4.258 0.000 F3 -0.351 0.097 -3.636 0.000 F5 -0.256 0.096 -2.682 0.007 SI       ON AGE -0.029 0.039 -0.744 0.457 GENDER 0.150 0.032 4.721 0.000 Open in a new tab Indirect effects were tested using the WLSMV estimator in Mplus. Significant indirect pathways were observed for ‘dependence with cognitive symptoms’ and ‘interpersonal sensitivity’ via ‘social avoidance’ and ‘emotional vulnerability,’ despite negative direct effects. This produced suppression effects, with total effects remaining positive. Full estimates, standard errors, and confidence intervals are provided in Supplementary File S2 . The influence of the factor ‘age’ had no statistical significance, while ‘gender’ had statistical significance on suicidal ideation (see Fig. 2 ; Table 6 ). Multicollinearity diagnostics To evaluate potential multicollinearity among latent predictors, we computed Variance Inflation Factors (VIFs) based on the R² of each factor regressed on the remaining four factors. VIF values ranged from 3.09 to 5.61 (F1 = 4.83; F2 = 3.09; F3 = 5.22; F4 = 5.61; F5 = 4.54), all below the commonly used threshold of 10 (Supplementary File S3 ). These results indicate that multicollinearity was not a concern in the SEM model. Hierarchical model To further validate the structure and explanatory utility of the five-factor model, a second-order structural equation model (SEM) was estimated using the Weighted Least Squares with Mean and Variance adjustment (WLSMV) estimator (see Supplementary File S4 ). The model demonstrated good fit to the data: χ²(854) = 6033.11, p < 0.001; RMSEA = 0.035 (90% CI [0.034, 0.036]); CFI = 0.963; TLI = 0.961. Although the SRMR value (0.102) slightly exceeded the conventional threshold of 0.08, the overall fit indices indicated an acceptable approximation of the hypothesized model. The measurement model showed that all first-order latent constructs (F1–F5) were well defined by their respective indicators, with standardized loadings ranging from 0.50 to 0.92 (all P value s < 0.001) (see Supplementary File S4 ). At the second-order level, the general latent factor (F) loaded strongly on all five dimensions—Social Avoidance (F1), Physical Symptoms (F2), Dependence with Cognitive Symptoms (F3), Emotional Vulnerability (F4), and Interpersonal Sensitivity (F5)—with standardized loadings ranging from 0.82 (F2) to 0.93 (F4). In the structural model, the second-order factor was significantly associated with suicidal ideation (SI), with a standardized path coefficient of β = 0.76 (SE = 0.02, p < 0.001), explaining 60.4% of the variance in SI (R² = 0.604) (see Supplementary File S4 ). Among covariates, gender was a significant associated with SI (β = 0.15, p < 0.001), with females reporting higher SI scores, while age was not statistically significant (β = −0.03, p = 0.457). Bifactor model (see Supplementary File S5 ) A bifactor model was estimated using the WLSMV estimator for categorical indicators. Model fit indices indicated acceptable fit to the data: χ²(814) = 5186.46, p < 0.001; RMSEA = 0.033, 90% CI [0.032, 0.034]; CFI = 0.967; TLI = 0.964; SRMR = 0.099. All items loaded significantly on the general factor (G), with standardized loadings ranging from 0.41 to 0.85 (M = 0.686), suggesting strong general-factor saturation. In contrast, loadings on group-specific factors (F1–F5) were notably smaller (range = 0.12 to 0.57, M = 0.347), indicating that item variance was primarily driven by the general factor. Indices of general-factor dominance supported unidimensionality: the explained common variance (ECV) was 0.779 and omega-hierarchical (ωₕ) was 0.973, demonstrating that the general factor accounted for the majority of reliable variance across items. In the structural model examining associations with suicidal ideation (SI), the general factor was significantly associated with SI (β = 0.323, p < 0.001). Among group-specific factors, F1 (β = 0.097, p < 0.001) and F5 (β = −0.058, p = 0.030) were statistically significant; F2–F4 were not. The model explained 12.4% of the variance in SI (R² = 0.124), with the general factor alone accounting for approximately 84% of the total explained variance. Gender was a significant covariate (β = 0.067, p < 0.001), with females reporting higher SI scores, while age was not significant (β = −0.011, p = 0.520). Prevalence of suicidal ideation From the online investigation using traditional version of University Personality Inventory (UPI) scale, among all 5028 participants, the 12-month prevalence of suicidal ideation in the total sample was 3.7% (2.7% in men, 5.3% in women). There was a significant sex difference in suicidal ideation prevalence (x 2 = 23.129, P value < 0.001) among this population. Discussion The present study aimed to explore the incidence and determinants of suicidal ideation (SI) among first-year students at higher vocational colleges in Eastern China, with a particular focus on the roles of social avoidance and emotional vulnerability. The findings revealed several critical insights that contribute to the understanding of mental health challenges faced by this population. A new five-factor model of the UPI The study developed a new five-factor model of the UPI. EFA was carried out with categorical variables on Sample-1(see Table 3 ) and found five UPI factors for Chinese higher vocational college students: F1: Social avoidance, F2: Physical symptoms, F3: Dependence with cognitive symptoms, F4: Emotional vulnerability, and F5: Interpersonal sensitivity (see Table 5 ). CFA confirmed this structure in Sample-2 and Sample-3 with good fit (see Table 4 ). Alpha coefficients also showed good reliability of this new structure. Although our research results also yielded a five-factor model, and the names of each factor are similar to those in the previous study, the items included in each factor differ significantly [ 42 ]. Overall, the content of each factor in the five-factor model obtained this time is more extensive than in the previous study. Differences in factor items and naming rationale Compared to the factor “cognitive symptoms” in the previous study, the factor “Dependence with cognitive symptoms” in this study includes all four items from the former [ 42 ]: “Lack of judgment”, “Too dependent on others”, “Lack of confidence”, and “Irresolute about anything”. Additionally, the factor “Dependence with cognitive symptoms” in this study includes the items: “Distracted”, “Declining memory”, “Lack of patience”, “Feel self-abased”, “Over-rigid, and “Cannot give up repeating things”. In fact, the same four items from both factors can be summarized with one word: dependency (lacking autonomy/insecure/indecisiveness) [ 42 ]. Although the additional six items can be described as cognitive impairment, they still reflect dependency (lacking autonomy/insecure/indecisiveness). Therefore, we named this factor, which includes these 10 items, “Dependence with cognitive symptoms”. The items included in the factor “Interpersonal sensitivity” are significantly different between the previous study and this study [ 42 ]. While the two items “Wary of others” and “Care about others” gaze” are the same, this study’s factor of “Interpersonal sensitivity” includes additional items: “Concern about urination or sexual organs”, “Easily feel misunderstood”, “Sense a weird smell from myself”, “Suspect others say something bad about me”, “Feel others despise me”, and “Sensitive emotions.” These six new items, which describe interpersonal interactions, are more self-directed and reflect a lack of confidence or self-criticism in interpersonal sensitivity. In the previous study, the two items in this factor mainly pointed towards others. The addition of these new items has enriched the content of the “Interpersonal sensitivity” factor. In the “Emotional vulnerability” factor, there are three items that are the same in this study and the previous study: “Full of dissatisfaction and complaints”, “Over-uneven in emotion”, and “Irritable” [ 42 ]. In this study, this factor also includes the items “Inclined to worry” and “Restless”, while in the previous study, this factor also included “Intolerance”, “Lack of patience”, and “Sensitive emotions.” “Inclined to worry, Restless” expresses a broad sense of anxiety, while “Intolerance, Lack of patience, Sensitive emotions” involves intolerance, impatience, and emotional sensitivity towards external environment. The former two items are more self-directed, while the latter three are more directed towards others. At previous study, the factor “social avoidance” include the items: “Do not like meeting others”, “ Feel that I am not myself”, “Lack faith in others ”, “ Unwilling to associate with others”. Compared with the previous study, in this study, the factor “social avoidance” includes two additional items: “Pessimistic” and “No interest in anything”, which enhance the connotation of the “social avoidance” factor [ 42 ]. Social avoidance refers to the behavior of individuals of actively avoiding various social occasions [ 68 ]. While some items clearly reflect overt social withdrawal, others—such as “pessimistic” and “no interest in anything”—may appear more affective or motivational in nature and overlap conceptually with some symptoms of depression. However, we argue that these items represent internal precursors or emotional correlates of social avoidance, rather than unrelated constructs. “Pessimistic” and “lack of enthusiasm and positivity” reflect a diminished expectation of meaningful or rewarding social interaction. This aligns with the concept of learned social helplessness, where individuals disengage from social environments due to anticipated failure or rejection. “Feel that I am not myself” captures a sense of dissociation or alienation, which often precedes social withdrawal. Individuals experiencing this symptom may feel disconnected from their social identity, leading to avoidance of interpersonal engagement. “No interest in anything” reflects anhedonia and motivational depletion, which are known to reduce initiative for social contact. This item has been shown in prior research to correlate strongly with social isolation and reduced peer interaction [ 69 , 70 ]. Empirically, these items loaded robustly onto the same latent factor in both EFA and CFA analyses, suggesting they share a common underlying dimension. Theoretically, we interpret this dimension as social disengagement driven by emotional and cognitive withdrawal, consistent with the construct of “thwarted belongingness” in the Interpersonal Theory of Suicide (IPTS). This is particularly salient in the context of vocational college freshmen facing transitional stressors. The higher an individuals’ social avoidance tendency is, the more they are unable to integrate into a group or the more likely they are to be excluded from a group, resulting in painful experiences and higher levels of loneliness, social anxiety, and Internet addiction [ 71 – 73 ]. Structural equation model The SEM model’s fit indices (CFI = 0.963, TLI = 0.961, RMSEA = 0.035, SRMR = 0.101) indicate a robust model fit. These indices suggest that the model accurately represents the data. The SEM model accounted for 72.8% of the variance in suicidal ideation, indicating strong associations among the included variables. However, due to the cross-sectional design, these associations should not be interpreted as evidence of causality. This study’s results, using the SEM model, revealed that “social avoidance” (β = 0.951, P < 0.01) was the factor most strongly associated with suicidal ideation, which aligns with many previous studies [ 74 , 75 ]. Although the standardized path coefficient from social avoidance to suicidal ideation was high (β = 0.951), supplementary diagnostics did not indicate problematic multicollinearity or construct redundancy. Inter‑factor correlations remained below 0.90, and all VIF values were below 6, well within commonly accepted thresholds. These results suggest that the strong association is not a statistical artifact. Conceptually, the UPI social‑avoidance factor captures withdrawal, pessimism, and reduced interpersonal engagement, whereas suicidal ideation was measured using a single, distinct item (“Have idea of wanting to die?”). Thus, although the constructs are related, they are not operationally overlapping. The magnitude of the coefficient may instead reflect the substantive psychological reality of vocational college freshmen, for whom social disconnection and difficulty integrating into new peer environments may strongly amplify vulnerability to suicidal thoughts. Nonetheless, we acknowledge this unusually strong effect and encourage future studies to examine its stability across different populations and measurement tools. Social avoidance, which is often accompanied by social isolation, is associated with an increased risk of suicidal ideation [ 76 ]. Research has shown that social isolation and lack of social support are significant risk factors for suicidal thoughts and behaviors [ 31 , 76 ]. Conversely, social support and connections with others can act as protective factors against these risks [ 31 , 77 ]. The SEM model in this study also revealed that “emotional vulnerability” (β = 0.511, P < 0.01) has a significant positive association with suicidal ideation. This means that individuals who are more emotionally vulnerable are more likely to have suicidal thoughts among these vocational college students. Some research indicated that emotional dysregulation and vulnerability could indeed increase the risk of suicidal ideation [ 78 , 79 ]. However, some studies noted that emotional vulnerability alone didn’t directly cause suicidal ideation and often interacted with other factors such as hopelessness, depression, and lack of social support [ 78 , 79 ]. This study found that females had higher levels of suicidal ideation than males which is consistent with previous research that there was a gender difference in suicide ideation, with females being more likely to report suicidal thoughts than males [ 80 , 81 ]. This may be due to biological, psychological, or social factors that influence gender differences in suicide risk, such as hormonal fluctuations, emotional expressiveness, interpersonal stressors, or gender norms [ 81 , 82 ]. This study found no significant effect of age on suicide ideation. This suggests that the factors influencing suicide ideation are consistent across different age groups. In this study, “physical symptoms” do not have a direct association with suicide ideation but have positive indirect effects through social avoidance (β = 0.243) and emotional vulnerability (β = 0.309). This means that “physical symptoms” may correlate with increased social avoidance and emotional vulnerability, which in turn may correlate with increased suicide ideation. Regarding the items included in the factor of “physical symptoms”, the similarities between this study and the previous study are that the factor of “physical symptoms” in both studies includes “Poor appetite”, “Feel sick, stomachache”, “Headache”, “Ache in neck and shoulder”, “Physically exhausted”, “Dizzy when I stand up”, and “Have ever lost consciousness, cramp.” The differences between this study and the previous study regarding the items included in the factor of “physical symptoms” are that the previous study’s factor of “physical symptoms” also included the item “Easily have diarrhea or constipation”, while this study’s factor of “physical symptoms” includes additional items such as “Care about palpitation and pulse”, “Frequent insomnia”, “Chest pain or feel oppressed”, and “Feel hot and cold.” When individuals experience physical symptoms, especially those that are chronic or severe, they may feel self-conscious or anxious about their condition [ 83 ]. This can result in them avoiding social interactions to prevent embarrassment or discomfort [ 84 ]. Our research findings indicate that “dependence with cognitive symptoms” and “interpersonal sensitivity” were directly significantly negatively associated with the increased risk of suicidal ideation among vocational college students, possibly indicating the protective effect. Despite their negative direct effects, they exert positive indirect effects on suicidal ideation through “social avoidance” and “emotional vulnerability”. Considering potential collinearity issues, this study also constructed hierarchical and bifactor models. We will subsequently discuss the dual role of “dependence with cognitive symptoms” and “interpersonal sensitivity” in influencing suicidal ideation. Hierarchical model The hierarchical SEM results provide strong empirical support for the validity and utility of the five-factor model derived from the University Personality Inventory (UPI). The robust loadings of the second-order factor on all five dimensions suggest that these psychological traits are interrelated and collectively contribute to a broader latent vulnerability construct. The high path coefficient (β = 0.76) linking this general factor to suicidal ideation underscores the cumulative impact of these traits on mental health risk. Importantly, the model explained over 60% of the variance in suicidal ideation, which is notably high for psychological models and highlights the predictive strength of this framework. This finding reinforces the importance of considering multidimensional psychological vulnerability—rather than isolated symptoms—when assessing suicide risk among vocational college students. The significant gender effect aligns with existing literature indicating that females are more likely to report suicidal ideation, possibly due to greater emotional expressiveness or sensitivity to interpersonal stressors. The non-significant effect of age suggests that the psychological mechanisms underlying suicidal ideation are consistent across the narrow age range of first-year vocational college students. The significant negative direct effects of F3 and F5 in the correlated model (Fig. 2 ) can now be understood as follows: After accounting for this general vulnerability (‘F’), the unique, specific aspects of “Dependence with cognitive symptoms " and “Interpersonal Sensitivity” may have the possibility to be a protective effect. In other words, for two students with the same high level of general psychological distress, the one who also has traits of dependency (e.g., seeks support) or interpersonal sensitivity (e.g., values relationships) may be less likely to develop suicidal ideation. Their specific traits provide a buffer against the general risk. The relationship between dependence (which can encompass both substance dependence and dependence on people) and suicidal ideation appears complex and multifaceted [ 85 – 87 ]. In this study, “dependence with cognitive symptoms” is inclined towards dependence on other people, such as interpersonal dependence. Some studies have shown that cognitive impairments, such as poor memory, judgment, or concentration, are associated with increased suicidal ideation in the elderly [ 88 – 91 ]. Other studies have revealed that, specifically for individuals with high levels of self-criticism, interpersonal dependency is positively associated with suicidal ideation in early adulthood [ 92 , 93 ]. The dependency of young people is different from that of the elderly. Dependency in the elderly may make them feel useless and burdensome, while the dependency of young people may manifest more as a need for guidance from others, thus providing opportunities for our intervention [ 94 – 100 ]. The observed negative direct association suggests the “Dependence with cognitive symptoms” of the students at this school may be a possible protective factor for SI, indicating that the students who acknowledge a need for support may indeed receive substantial help from environmental circumstances such as their parents, their teachers, or a significant degree of mutual assistance among peers [ 101 , 102 ]. Conversely, the positive indirect associations via social avoidance and emotional vulnerability suggest an alternative pathway where feelings of dependence are linked to poorer outcomes. This pattern is consistent with theoretical models. For instance, feelings of embarrassment or shame about one’s reliance on others could be associated with increased social withdrawal [ 103 ]. This increased social avoidance, in turn, raises the risk of suicide ideation. Furthermore, dependence on others for cognitive support may correlate with heightened emotional vulnerability, potentially linking it to greater sensitivity to stress and emotional disturbances, which can indirectly increase suicide ideation [ 79 , 104 ]. These findings align with theories positing that perceived burdensomeness and thwarted belongingness are mediators in the relationship between psychological distress and suicidal ideation [ 79 , 104 , 105 ]. Additionally, emotional regulation and perceived social support play crucial roles in managing stress and reducing suicidal thoughts [ 79 , 104 , 105 ]. If dependency becomes a risk factor for SI, it also may suggest that the students receive less interpersonal support in real-world settings [ 102 , 106 ]. It is plausible that the nature of the dependency—whether it leads to connectedness or perceived burden—determines its role as a protective or risk factor. Similarly, the relationship between interpersonal sensitivity and suicidal ideation can be complex. On one hand, its negative direct association suggests that sensitivity to others may be linked to stronger social connections and empathy, which can be protective [ 107 , 108 ]. Strong social support networks can provide emotional support and a sense of belonging, which are crucial for mental well-being [ 90 , 109 ] .On the other hand, its positive indirect association suggests that for some, this excessive sensitivity could also be associated with emotional exhaustion, feelings of inadequacy, and a heightened vulnerability to perceiving social rejection and then suicidal ideation [ 110 , 111 ]. Suicidal ideation involves thoughts about self-harm or suicide, which can arise from feelings of hopelessness, emotional distress, or overwhelming interpersonal conflicts [ 112 ]. Individuals with high interpersonal sensitivity may be more prone to experiencing these feelings due to their heightened awareness of social dynamics and potential rejection or criticism [ 113 , 114 ]. This duality underscores that these traits are not inherently good or bad; their impact is likely determined by how they manifest within an individual’s specific social context and coping resources. Bifactor model The bifactor model offered a nuanced and statistically robust understanding of the latent structure underlying suicidal ideation (SI), revealing a dominant general factor (‘G’) alongside five specific factors (F1–F5) that captured unique variance. This modeling approach allowed us to disentangle shared psychological vulnerability from domain-specific influences, offering a more precise and interpretable representation of the data. Notably, the bifactor model addressed multicollinearity concerns observed in correlated factors models, as the general factor absorbed much of the shared variance among predictors. Even after accounting for this dominant influence, F1 (Social Avoidance) and F5 (Interpersonal Sensitivity) retained significantly associated with SI—showing a positive association, and F5 a negative one. Dependence (F3) and Emotional Vulnerability (F4) did not show unique effects beyond ‘G’, indicating their influence is largely subsumed under general psychological distress. These findings support a dual-factor framework for conceptualizing SI. That is General Vulnerability (‘G’) (referring to all five factors) contribute to a shared psychological distress dimension, which is a major risk factor for SI. High scores on any factor signal elevated general vulnerability. The unique variance of F1 (Social Avoidance) promotes SI, even beyond general distress—highlighting the isolating impact of avoidance behaviors. The unique variance of F5 (Interpersonal Sensitivity) is negatively associated with SI, suggesting that sensitivity to others may foster connection and support-seeking. F3 (Dependence), while not statistically significant in isolation, may still represent a latent potential for protective social engagement. This dual-factor perspective aligns with transdiagnostic theories of psychopathology, which view SI as a multifaceted construct with both universal and individualized risk dimensions. This refined understanding translates into actionable strategies. Begin by identifying students with high general psychological distress (e.g., those with elevated five-factor UPI scores), and then assess their specific profiles. Students with high F1 scores combined with high distress are at the highest risk; those with high F5 or F3 scores alongside high distress may indicate potential protective strengths. We can reduce general vulnerability through campus-wide programs—stress-management workshops, resilience training, and emotional-regulation modules such as mindfulness. For students high in F1, implement interventions that decrease avoidance: social-skills training, graded exposure, and structured peer engagement. For students high in F3 or F5, leverage their interpersonal strengths by encouraging help-seeking behavior, mentoring opportunities, and the cultivation of supportive relationships. Further discussion of the dual roles of dependence and interpersonal sensitivity in suicidal ideation The present findings indicate that “dependence with cognitive symptoms” and “interpersonal sensitivity” exert both negative direct effects and positive indirect effects on suicidal ideation, suggesting that these traits may function as protective or risk factors depending on contextual conditions. Dependence may reduce suicidal ideation when adequate social support is available, as individuals who rely on others may be more likely to seek help, disclose distress, or receive emotional reassurance. However, in environments where support is limited or inconsistent, dependence may heighten feelings of helplessness or entrapment, thereby increasing vulnerability to suicidal thoughts through emotional dysregulation. Similarly, interpersonal sensitivity may promote adaptive social vigilance and motivate individuals to maintain harmonious relationships when self-esteem is stable and peer interactions are positive. Yet, when self-esteem is low or interpersonal feedback is perceived as threatening, heightened sensitivity may amplify rejection fears and negative self-evaluation, indirectly increasing suicidal ideation through emotional vulnerability. These dual pathways highlight the importance of assessing contextual moderators—such as perceived social support, self-esteem, and campus climate—when interpreting risk profiles. Interventions may therefore benefit from strengthening supportive networks, enhancing self-worth, and teaching adaptive interpersonal coping strategies to ensure that these traits function as protective rather than risk-enhancing factors. Strengths of this study This study has unique significance. First, the study presents a new five-factor model of the University Personality Inventory (UPI). The revised inventory shortens the original 60 items to 40 and is designed to enhance early detection and intervention for suicidal ideation. Its key innovation lies in developing a UPI model specifically for higher vocational college students, thereby filling a research gap in the mental health of this population. By integrating inductive factor discovery with theory‑guided interpretation, the study maintains both empirical rigor and theoretical coherence. This retroductive approach ensured that theoretical interpretations did not bias factor extraction, while still allowing the final model to be grounded in established suicide frameworks. Second, the study employed an abductive analysis to identify and validate two core factors—“social avoidance” and “emotional vulnerability”—within a vocational student cohort. These data-driven constructs were successfully integrated within the frameworks of the Interpersonal Theory of Suicide (IPTS), the Integrated Motivational-Volitional (IMV) model, and the Three-Step Theory (3ST). Third, the study complicated the traditional binary view of risk and protective factors, demonstrating their context-dependent nature. Constructs such as “dependence with cognitive symptoms” and “interpersonal sensitivity” may exhibit a dual role, functioning as both possible risk factors (through indirect pathways) and potential protective buffers (through direct pathways). This challenges a solely pathological interpretation of these traits. The use of bifactor modeling was instrumental in resolving collinearity and clarifying this complex latent structure, providing a robust psychometric foundation for our interpretations. Fourth, the findings offer direct and actionable insights for clinical practice and policy. We recommend developing UPI-based risk profiles centered on “social avoidance” and “emotional vulnerability” for early identification of at-risk individuals. The possibly protective traits like interpersonal sensitivity could be leveraged as assets. For instance, students high in this trait could be trained in peer-mentoring programs, effectively transforming a potential risk factor into a protective mechanism. We advocate for policies that address the structural stressors faced by vocational students, focusing on systemic solutions designed to reduce perceived burdensomeness and enhance belongingness. Limitations of this study There are some limitations in this study. First, the generalizability of the present findings should be interpreted with caution. The study was conducted within a single higher vocational college in Eastern China, which limits the extent to which the results can be applied to other educational settings or regions. Vocational college students often experience distinct developmental and contextual pressures—including strong employment‑oriented curricula, early job‑market expectations, a technical‑training orientation, and unique peer environments—that may shape their psychological symptom patterns and stress responses differently from four‑year university students. These characteristics may also influence how the UPI factors operate and how risk pathways toward suicidal ideation manifest. As such, caution is warranted when extending the revised UPI model to students in other institutional types or geographic contexts. Second, the cross-sectional nature of our data prohibits any causal inferences. The relationships identified, including the mediation pathways, should be interpreted as associations that are consistent with theoretical models, but not as evidence of causation. Longitudinal or experimental designs are needed to establish temporal precedence and causality. Second, this research used a self-report measure of suicidal ideation, which may be subject to reporting biases or social desirability effects, potentially underestimating or overestimating the prevalence and predictors of suicidal ideation. Third, the SEM model accounted for 72.8% of the variance in suicidal ideation, indicating strong associations among the included variables. However, due to the cross-sectional design, these associations should not be interpreted as evidence of causality. The use of a binary item to measure SI may have contributed to inflated model fit due to shared method variance and restricted response variability. Additionally, the strong path coefficient from social avoidance (β = 0.951) may reflect conceptual overlap or measurement proximity between social avoidance and suicidal ideation. Future directions Future studies should consider using multi-item SI scales and alternative modeling techniques (e.g., logistic SEM or latent class analysis) to validate these findings and assess the generalizability of the model [ 115 ]. Also, in the future, longitudinal studies are needed to determine temporal precedence and causal mechanisms. Additional methods, such as clinical interviews or observer ratings, should be used to provide a more comprehensive assessment of suicidal ideation and behavior. To strengthen external validity, future research should adopt multi‑center designs and recruit more diverse samples across regions, institutional types, and cultural contexts. Comparative studies involving four‑year universities, secondary vocational schools, and vocational colleges in other provinces or countries would help determine whether the factor structure and predictive pathways identified here remain stable across populations. Further work should also broaden demographic representation and examine additional determinants of suicidal ideation, including socioeconomic status, cultural factors, and the availability of mental‑health services. Such efforts will not only clarify the generalizability of the revised UPI model but also inform the development and evaluation of targeted interventions. Conclusions This large-scale study provides a validated, shortened 40-item University Personality Inventory (UPI) tailored specifically for higher vocational college freshmen in China, addressing a critical research gap for this understudied population. Our findings reveal that suicidal ideation (SI) among these students is a complex phenomenon associated with a multifaceted psychological structure. Through retroductive analysis grounded in the Interpersonal Theory of Suicide (IPTS), the Integrated Motivational-Volitional (IMV) model, and the Three-Step Theory (3ST), we identified “social avoidance” and “emotional vulnerability” as the most salient correlates of SI. Notably, constructs such as “dependence with cognitive symptoms” and “interpersonal sensitivity” exhibited dual roles, showing direct protective associations but indirect risk associations through emotional and social pathways. This complexity, further clarified by bifactor modeling, underscores that psychological traits are not inherently risk or protective factors; their impact is context-dependent, particularly on the availability of social support and individual coping resources. The findings advocate for a shift from generic mental health screening toward targeted interventions that foster social connectedness, build emotional resilience, and leverage potentially protective traits like interpersonal sensitivity through peer-support programs. Future longitudinal research is essential to establish causal mechanisms and to develop and evaluate interventions that address the unique structural and psychological pressures faced by vocational college students. Supplementary Information Below is the link to the electronic supplementary material. Supplementary Material 1 (205.5KB, pdf) Acknowledgements The authors would like to thank the young people who participated in the study. Abbreviations SI Suicidal ideation UPI University Personality Inventory EFA Exploratory factor analysis CFA Confirmatory factor analysis SEM Structural equation modeling IPTS Interpersonal Theory of Suicide IMV Integrated Motivational–Volitional Model 3ST Three-Step Theory RMSEA Root mean square error of approximation CFI Comparative fit index TLI Tucker–Lewis index SRMR Standardized root mean square residual COVID-19 Coronavirus disease 2019 VIF Variance inflation factor WLSMV Weighted least squares mean and variance adjusted estimator Author contributions X.L. contributed to the conceptualization, data curation, formal analysis, investigation, methodology, project administration, resources, software, validation, visualization, and writing of the original draft. K.J.Y. contributed to the conceptualization, methodology, validation, and writing (review and editing). O.W. contributed to the conceptualization, funding acquisition, investigation, methodology, project administration, resources, supervision, validation, and writing (review and editing). All authors have read and approved the final manuscript. Funding This work is supported by Zhejiang Provincial Philosophy and Social Sciences Planning Project (Grant No 24NDJC194YB). Data availability The datasets generated and analyzed during the current study are not publicly available due to participants’ privacy, funding policy or other restrictions but are available from the corresponding author on reasonable request. Declarations Ethics approval and consent to participate The study was conducted in accordance with the Declaration of Helsinki. Zhejiang Federation of Humanities and Social Sciences approved the study. Ethics approval from the research ethics committee of the local institution were received (approval number:202501058). Consent for publication Not applicable. 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