Who is stressed and who is driven? A latent profile analysis of career growth among early-career operating room nurses

Who is stressed and who is driven? 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A latent profile analysis of career growth among early-career operating room nurses Zhihao Han, Zheyi Jiang, Xiaoqin Ma This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8165900/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Background Early-career operating room nurses work in highly technical, high-demand environments and are at elevated risk of stress, stalled development and turnover. Yet little is known about how their career growth patterns differ, or which resource configurations support more favorable trajectories. Guided by Conservation of Resources (COR) theory, this study aimed to (1) identify latent profiles of career growth among early-career operating room nurses and (2) examine how condition, personal, energy and proximal psychological resources differentiate these profiles. Methods We conducted a multicenter cross-sectional online survey between March and August 2025 among early-career operating room nurses working in tertiary hospitals in Zhejiang Province, China. Using multi-stage cluster sampling, 516 nurses from 102 hospitals provided valid responses. Career growth was assessed using the Career Growth of Nurses Scale, and resources were organized a priori into object, condition, personal, energy and proximal psychological resource categories. Latent profile analysis was used to identify distinct career growth profiles. Multinomial logistic regression examined associations between resource variables and profile membership, with statistical significance set at α = 0.05. Results A three-profile solution provided the best fit to the data. We identified a Constrained and uneven profile (C1, n = 66, 12.8%), a Stable and balanced profile (C2, n = 238, 46.1%) and a Consistent and advancing profile (C3, n = 212, 41.1%). Compared with C1, nurses with formal employment contracts, working in higher-tier hospitals, reporting higher monthly income, better self-rated health and stronger interest in the nursing profession were more likely to be classified into C2 and C3. Higher levels of thriving at work, organizational commitment and perceived organizational career management further distinguished nurses in the more favorable profiles. Object resources showed no independent associations after adjustment. Conclusions Early-career operating room nurses exhibit heterogeneous patterns of career growth that cluster into constrained, stable and advancing profiles. COR-informed resource portfolios, particularly stable employment and hospital context, adequate income, good health and interest in nursing, and strong proximal psychological resources, are associated with more adaptive profiles. Nurse managers can use these findings to design resource-focused strategies and targeted development programs to support sustainable career growth and retention among early-career operating room nurses. Job stress career growth Latent profile analysis Conservation of resources theory Operating room nurses Figures Figure 1 Figure 2 Figure 3 Introduction Early-career operating room nurses practice in highly technical, time-sensitive environments where rotating shifts, precision teamwork, and rapid decision-making are routine [ 1 ]. Across seemingly similar settings, however, their trajectories of career growth diverge markedly. Recent empirical work continues to document elevated job stressors and strains in operating rooms, while also showing that supportive, well-structured workplaces can foster professional development and retention [ 2 , 3 ]. Against this backdrop, the present study takes an influence-factor perspective on “who grows” in early career practice and treats stress-related impediments and motivation-related facilitators as parallel inputs rather than a single continuum. To organize these inputs, we adopt Conservation of Resources (COR) theory as the guiding framework: COR posits that people strive to obtain, retain, and protect valued resources and experience strain when resources are threatened, lost, or fail to yield gains after investment; it further emphasizes that resources cluster in caravans and flow through contextual passageways [ 4 ]. In line with COR, we structure the study’s potential influences as object, condition, personal, and energy resources, and we additionally consider proximal psychological resources as state-like manifestations through which resource systems often register in work settings. We treat career growth as a latent outcome and examine how the foregoing influences differentiate latent profiles of development among early-career operating room nurses [ 5 ]. COR defines resources as “objects, personal characteristics, conditions, or energies” that are valued or serve as means to valued ends, a formulation we use to delineate each base’s function [ 4 ]. We distinguish four foundational resource types: object resources (tangible possessions; e.g., homeownership status), condition resources (enduring structures or states that confer access or protection; e.g., work-family support, organizational career management), personal resources (efficacious individual capacities; e.g., general self-efficacy, interest in the nursing profession), and energy resources (convertible means that enable investment; e.g., monthly income) as predictors [ 6 ]. To align with refined COR accounts in organizational settings, we separate the four foundational resource bases from a proximal psychological layer because models that treat state-like constructs (e.g., organizational commitment, thriving) as the near-behavior carriers of resource systems offer a clearer and more testable explanation of how resources differentiate developmental outcomes at work [ 5 , 6 ]. This theory-driven structure underpins Fig. 1 and motivates our analyses of how the four bases and proximal psychological resources differentiate latent profiles of nurse professional growth. Building on this COR-guided structure, we conceptualize career growth in early operating room practice as a categorical developmental phenomenon that can appear as distinct profiles, rather than a simple position on a single continuum. Conceptually, such profiles capture qualitatively different constellations of opportunities and constraints that young nurses face in their first years. Within COR, the four foundational resource bases “object, condition, personal, and energy” serve as predictors that may enable or hinder movement toward more favorable developmental configurations by supporting goal pursuit, safeguarding against loss, and facilitating further resource acquisition. Meanwhile, proximal psychological resources, organizational commitment and thriving at work, represent the state-like expressions through which resource systems register close to behavior and thus sit adjacent to the profiles in Fig. 1 . This perspective keeps the focus squarely on which resources differentiate who shows stronger growth, while leaving questions about how these resources operate over time for subsequent inquiry. Against this COR-guided backdrop, our purpose is twofold: first, to characterize heterogeneity in career development among early-career operating room nurses by identifying latent profiles of career growth; and second, to examine which resources operate as enablers or barriers that differentiate profile membership. As depicted in Fig. 1 , solid arrows indicate the associations tested in the present study (resource bases to profile membership), whereas dashed arrows mark our follow-up research agenda to evaluate whether proximal psychological resources mediate resource-growth linkages. Throughout, arrows denote statistical associations rather than causal effects; this influence-factor perspective keeps the focus on which resources distinguish who grows, while reserving temporal mechanisms for subsequent work. Methods Design and settings We conducted a multicenter, cross-sectional online survey between March and August 2025 among early-career operating room nurses employed in tertiary hospitals across Zhejiang Province, China. Hospitals from 11 prefecture-level cities were invited through nursing department liaisons. Recruitment used a WeChat QR code that redirected to a Wenjuanxing ( https://www.wjx.cn ) e-questionnaire; participation was voluntary and anonymous, with an electronic informed-consent screen gating entry to the survey. To minimize duplicate or incomplete submissions, the platform enforced one-submission limits and required complete responses prior to submission. Data were exported and verified by double entry before analysis. A total of 102 tertiary hospitals contributed valid responses. Participants We recruited early-career operating room nurses from tertiary hospitals across 11 cities in Zhejiang Province, China. Eligibility required holding a registered nurse license, currently working in an operating room for 3 months to 5 years, no psychiatric or cognitive disorders, and provision of electronic informed consent; interns/advanced-study nurses, staff not on duty (e.g., sick or maternity leave), intra-hospital transferees, or those unable to complete the survey for physical/mental reasons were excluded. To ensure data integrity, we further excluded incomplete submissions, responses with evident regularity/extreme patterns, or very short completion time (< 150 s). Recruitment was coordinated through nursing departments at participating hospitals; invitees accessed the survey via WeChat QR code linking to the Wenjuanxing platform, after viewing an electronic consent screen. The sample covered 11 cities. A total of 530 nurses completed the questionnaire; after excluding 14 questionnaires for very short/incomplete responses and evident patterned or extreme responding, 516 valid cases were retained. Measurements This study used a structured, multi-scale questionnaire comprising: Sociodemographic and Job Characteristics (researcher-developed); the Career Growth Scale for Nurses (CGNS); the General Self-Efficacy Scale (GSE); the Thriving at Work Scale (TWS); the Chinese Employee Organizational Commitment Scale (CEOCS); the Work-Family Support Scale (WFSS); and the Organizational Career Management Questionnaire (OCMQ). For each instrument, item composition, dimensions, scoring, and reliability are reported in the subsections below. The full structured questionnaire, exact item wordings, and response options are provided in Supplementary File 1. Sociodemographic and job characteristics Its were measured at baseline using a researcher-developed questionnaire that was finalized after a targeted literature review and two rounds of panel discussion. The item set captured sex, age (years), educational attainment, only-child status, marital status, parenthood (has children), hospital tier, homeownership status, specialty nurse certification (obtained), monthly income (CNY), professional rank, number of night shifts per month, operating room nursing tenure, employment type, self-rated health, and interest in the nursing profession. Two single-item constructs employed anchored scales: self-rated health (“Overall, how would you rate your health?”; 1 = Poor, 5 = Excellent) and interest in the nursing profession (“How interested are you in the nursing profession?”; 1 = Not at all interested, 5 = Extremely interested). To establish content adequacy, six subject-matter experts independently rated item relevance on a 4-point scale; the average scale-level content validity index (S-CVI/Ave) was 0.927, with per-item indices and rater details reported in Supplementary File 2. The retained item set was chosen for parsimony, coverage of the target constructs, and clarity of wording, in line with workforce survey conventions. Career growth It was assessed with the CGNS adapted from the employee career growth scale developed by Q X Weng and Y M Xi. [ 7 ] and validated in Chinese nurses. The CGNS comprises three dimensions: career goal progress (4 items), professional ability improvement (4 items), and career development opportunity (7 items). Responses were recorded on a 5-point Likert scale (1–5) and summed to a total score ranging 15–75, with higher scores indicating better career growth. In prior validation, internal consistency was Cronbach’s α = 0.93 for the total scale, and 0.84, 0.78, 0.89 for the subscales. Example items include “My current job brings me closer to my career goals” and “My current job provides good development opportunities”. General self-efficacy It was measured with the GSE, developed by S K Cheung and S Y Sun [ 8 ], which assesses a broad, situation-general belief in one’s capability to cope with challenges and attain goals. The instrument comprises 10 items and demonstrates a single-factor structure with no reverse-scored items. Responses were recorded on a 1–4 Likert-type scale (1 = not at all true, 4 = exactly true) and summed to a total score of 10–40, with higher values indicating stronger general self-efficacy. Internal consistency in the present sample was excellent Cronbach’s α = 0.94. Example items included: “If I try hard enough, I can always solve problems” and “Whatever happens to me, I can handle it well”. Thriving at work It was measured with the TWS, developed by L P Zeng et al. [ 9 ], which conceptualizes thriving as a positive psychological state jointly comprising vitality and learning. The instrument contains 10 items across two dimensions, vitality (5 items) and learning (5 items), that reflect felt energy and ongoing growth at work. Responses were recorded on a 1–7 Likert-type scale (1 = completely inconsistent, 7 = completely consistent) and summed to a total score ranging 10–70, with higher values indicating greater thriving. Internal consistency in the present sample was Cronbach’s α = 0.88 for the total scale, and 0.76, 0.81 for vitality and learning. Example items include “I proactively learn relevant knowledge at work” and “At work, I maintain a clear mind and flexible thinking”. Organizational commitment It was measured with the CEOCS, developed by W Q Ling et al. [ 10 ].The instrument comprises 25 items across five dimensions: affective commitment (5 items), normative commitment (5 items), ideal commitment (5 items), economic commitment (5 items), and opportunity commitment (5 items). Responses were recorded on a 4-point Likert scale (1 = strongly disagree, 4 = strongly agree), with all items positively keyed. We computed subscale scores and a total score (range 25–100), where higher values indicate stronger organizational commitment. Internal consistency in the present sample was excellent: Cronbach’s α = 0.97 for the total scale, and 0.88, 0.88, 0.87, 0.92, 0.95 for five subscales, respectively. Example items include “I am willing to make any contribution to the hospital’s development” and “I am willing to devote all my efforts to the hospital”. Work-family support It was measured with the WFSS, developed by Y X Li and N Zhao [ 11 ]. The instrument comprises 30 items across four dimensions: organizational support (8 items), leadership support (8 items), emotional support (7 items), and instrumental support (7 items), capturing multi-source support relevant to employees’ work-family interface. Responses were recorded on a 5-point Likert scale (1 = strongly disagree, 5 = strongly agree) with all items positively keyed; subscales were summed and aggregated to a total score ranging 30–150, where higher values indicate greater work-family support. Internal consistency in the present sample was excellent: Cronbach’s α = 0.98 for the total scale, and 0.95, 0.96, 0.93, 0.89 for organizational, leadership, emotional and instrumental support, respectively. Example items include “The organization recognizes our work achievements” and “The organization provides good welfare benefits”. Organizational career management It was measured with the OCMQ, revised by Y Xu et al. [ 12 ], which assesses employees’ perceptions of career management practices within the organization. The instrument comprises 16 items across four dimensions: promotion fairness (4 items), emphasis on training (4 items), provision of career information (4 items), and facilitating organizational development (4 items). Responses were recorded on a 4-point Likert scale (1 = not consistent, 4 = consistent) with all items positively keyed; subscales were summed and aggregated to a total score ranging 16–64, where higher values indicate more favorable organizational career management. Internal consistency in the present sample was excellent: Cronbach’s α = 0.96 for the total scale, and 0.83, 0.86, 0.89, 0.89 for promotion fairness, emphasis on training, provision of career information, and facilitating organizational development, respectively. Example items include “The hospital promotes nurses based on their overall abilities” and “The hospital arranges experienced staff to mentor nurses”. Data collection Data were collected between March and August 2025 using an anonymous, structured online questionnaire hosted on Wenjuanxing and disseminated via QR code/URL through hospital channels. At each participating tertiary hospital in Zhejiang Province, a designated nursing-department liaison, trained with a standardized administration script, coordinated local rollout within a one-week collection window and checked basic completeness. Prior to launch, a pilot with 20 early-career operating room nurses assessed clarity and feasibility; no items were revised, and the average completion time was approximately 5 minutes. Before accessing the survey, participants viewed an electronic informed-consent page (purpose, confidentiality, voluntariness); only those who consented proceeded. No personal identifiers were collected. Platform-level anti-duplication was enabled and all items were mandatory to minimize missingness. A total of 530 questionnaire submissions were received. Following pre-specified quality checks, 516 valid questionnaires were retained; 14 submissions were excluded for two reasons: (i) incomplete or very short completion time (e.g., < 150 s), and (ii) evident patterned or inconsistent responding. The final sample covered 11 prefecture-level cities and 102 tertiary hospitals. After closure, data were exported from WenJuanxing and underwent double-entry verification with a third-person audit against the original files prior to analysis. Data analysis Data were entered in EpiData version 3.1 and analyzed in SPSS version 26.0 and Mplus version 8.3. Descriptive statistics were summarized as n (%) for categorical data and mean ± SD for continuous/ordinal data. Internal consistency of each scale and subscale was evaluated using Cronbach’s α. To identify career growth profiles, we conducted latent profile analysis (LPA) in Mplus using the CGNS total score as the profile indicator. Models with one to five classes were estimated via maximum likelihood with multiple random starts. Model fit was evaluated using the Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), sample-size-adjusted BIC (aBIC), the Lo-Mendell-Rubin adjusted likelihood-ratio test (LMR-LRT), the Bootstrap Likelihood Ratio Test (BLRT), entropy, and Average Posterior Probability (APP). Lower AIC/BIC/aBIC and higher entropy, together with significant LMR-LRT/BLRT, were preferred; when AIC/BIC decreased monotonically, we selected the elbow model. We required entropy > 0.80 and APP > 0.70; solutions with any class < 5% of the sample were discarded. Substantive interpretability was also considered when determining the optimal solution. After the profiles were determined, we examined influencing factors across profiles. Specifically, univariate analyses compared sociodemographic and job characteristics and the resource variables (GSE, CEOCS, TWS, WFSS, OCMQ) across latent classes. Variables with p < 0.05 in univariate tests were then entered into multivariable analyses (with career growth latent class as the dependent variable) to identify independent factors differentiating the profiles. All tests were two-sided with α = 0.05; p < 0.05 was considered statistically significant. Ethics approval The study protocol was reviewed and approved by the institutional ethics committee (Approval No. 2025-010). All procedures complied with the Declaration of Helsinki and relevant local regulations. Participation was voluntary. Before accessing the questionnaire, eligible nurses viewed an electronic informed-consent form describing the purpose, procedures, potential risks/benefits, confidentiality, and the right to discontinue at any time without penalty; only those who consented proceeded. The survey did not collect personal identifiers, responses were recorded anonymously, and data were used for research purposes only. De-identified datasets were stored on password-protected devices with access restricted to the study team. Results are reported in aggregate to protect participant confidentiality. Results Sample characteristics Participants were predominantly female (87.02%), with males accounting for 12.98%. Most were aged 26–30 years (48.26%) and held a bachelor’s degree (76.36%). The majority were non-only children (61.05%), married (50.58%), and childless (62.98%). Respondents mainly worked in tertiary hospitals, including Tertiary A (58.33%) and Tertiary B (41.67%) institutions. Over half had not obtained a specialty nurse certification (61.05%), and the most common monthly income was CNY 4,001–6,000 (34.30%). Nearly half held the title of Senior Nurse (45.74%) and worked fewer than five-night shifts per month (69.19%). In terms of tenure, most had 1–3 years of operating room nursing experience (44.96%), and half were formally employed (50.58%). Regarding subjective measures, 47.48% rated their health as excellent, and 48.45% reported being extremely interested in the nursing profession. Detailed sociodemographic and job characteristics are presented in Table 1 . Table 1 Sociodemographic and job characteristics of early-career operating room nurses (N = 516) Variable Classification Number Proportion (%) Sex Male 67 12.98 Female 449 87.02 Age (years) 20–25 106 20.54 26–30 249 48.26 ≥ 31 161 31.20 Educational attainment Secondary vocational school 16 3.10 Associate degree 35 6.78 Bachelor’s degree 394 76.36 Master’s degree 66 12.79 Doctoral degree 5 0.97 Only-child status Yes 201 38.95 No 315 61.05 Marital status Single 223 43.22 Married 261 50.58 Divorced 32 6.20 Widowed 0 0.00 Parenthood (has children) Yes 191 37.02 No 325 62.98 Hospital tier Tertiary A 301 58.33 Tertiary B 215 41.67 Homeownership status Yes 167 32.36 No 349 67.64 Specialty nurse certification (obtained) Yes 201 38.95 No 315 61.05 Monthly income (CNY) ≤ 4,000 64 12.40 4,001–6,000 177 34.30 6,001–8,000 175 33.91 8,001–10,000 63 12.21 >10,000 37 7.17 Professional rank Registered nurse 91 17.64 Senior nurse 236 45.74 Charge nurse 189 36.63 Number of night shifts per month 9 16 3.10 Operating room nursing tenure 3 months-1 year 96 18.60 1–3 years 232 44.96 3–5 years 188 36.43 Employment type Establishment post 261 50.58 Non-establishment post 255 49.42 Self-rated health Poor 11 2.13 Fair 19 3.68 Good 97 18.80 Very good 144 27.91 Excellent 245 47.48 Interest in the nursing profession Not at all interested 7 1.36 Slightly interested 18 3.49 Moderately interested 98 18.99 Very interested 143 27.71 Extremely interested 250 48.45 Note: Values in the “Proportion” column may not total 1.000 (100%) due to rounding. Participants scored 51.19 ± 9.98 on the CGNS, 32.11 ± 5.34 on the GSE, and 58.89 ± 8.13 on the TWS, and 79.67 ± 12.67 on the CEOCS. The mean total score for the WFSS was 124.40 ± 18.18. The OCMQ showed a mean total score of 53.32 ± 7.78. Detailed subdimension scores for each scale are presented in Table 2 . Table 2 Descriptive statistics of scale scores among early-career operating room nurses (N = 516) Variable Dimension Dimension score Item mean Total score CGNS Career goal progress 13.52 ± 2.85 3.38 ± 0.71 51.19 ± 9.98 Professional ability improvement 13.80 ± 2.73 3.45 ± 0.68 Career development opportunity 23.87 ± 5.38 3.41 ± 0.77 GSE / / 3.21 ± 0.53 32.11 ± 5.34 TWS Learning 29.54 ± 3.96 5.91 ± 0.79 58.89 ± 8.13 Vitality 29.35 ± 4.57 5.87 ± 0.91 CEOCS Affective commitment 15.72 ± 2.63 3.14 ± 0.53 79.67 ± 12.67 Normative commitment 16.03 ± 2.77 3.21 ± 0.55 Ideal commitment 16.09 ± 2.66 3.22 ± 0.53 Economic commitment 15.95 ± 2.91 3.19 ± 0.58 Opportunity commitment 15.89 ± 3.15 3.18 ± 0.63 WFSS Organizational support 41.00 ± 6.11 4.10 ± 0.61 124.40 ± 18.18 Leadership support 41.60 ± 6.40 4.16 ± 0.64 Emotional support 25.06 ± 3.90 4.18 ± 0.65 Instrumental support 16.74 ± 2.46 4.19 ± 0.61 OCMQ Promotion fairness 13.17 ± 1.97 3.29 ± 0.49 53.32 ± 7.78 Emphasis on training 13.32 ± 2.09 3.33 ± 0.52 Provision of career information 13.42 ± 2.12 3.36 ± 0.53 Facilitating organizational development 13.41 ± 2.05 3.35 ± 0.51 Note: Scores are presented as mean ± standard deviation (SD). Latent profile model selection for career growth Using the CGNS total score as indicators, we estimated one to five-class LPA solutions (Table 3 ). The one-class (null) model fit worst. With additional classes, AIC/BIC/aBIC decreased, entropy for the 2–5 class models was > 0.80, and both LMR-LRT and BLRT were significant ( p < 0.05), indicating that adding classes improved fit. Although the five-class solution further reduced AIC/BIC, it included a very small class (2.5%), violating our pre-specified ≥ 5% class-size rule. Compared with the three-class solution (class proportions 0.128/0.461/0.411, entropy 0.922), the four-class solution offered only marginal gains in information criteria and entropy (0.925) while splitting the medium-high range into two similarly sized classes (0.409/0.405) without clear substantive distinction. In line with parsimony, classification quality, the class-size criterion, and the COR-based expectation of low/medium/high strata in resource-related outcomes, we retained the three-class solution as the optimal representation of heterogeneity in career growth among early-career operating room nurses. Table 3 Fit indices for one- to five-class latent profile solutions of CGNS Class AIC BIC aBIC Entropy LMR( p ) BLRT( p ) Class proportions 1 20727.857 20855.240 20760.014 -- -- -- -- 2 18237.990 18433.311 18287.299 0.916 0.005 0.005 0.333, 0.667 3 17235.515 17498.773 17301.974 0.922 0.002 0.002 0.128, 0.461, 0.411 4 17010.702 17341.898 17094.312 0.925 0.024 0.025 0.109, 0.077, 0.409, 0.405 5 16335.328 16734.463 16436.089 0.951 < 0.001 < 0.001 0.025, 0.165, 0.310, 0.314, 0.186 Note: AIC = Akaike Information Criterion; BIC = Bayesian Information Criterion; aBIC = sample-size-adjusted BIC; Entropy ranges 0–1 with higher values indicating better classification. LMR-LRT ( p ) and BLRT ( p ) test whether the k-class model fits significantly better than the (k-1)-class model. Estimated class proportions are the model-estimated proportions of participants assigned to each class (most-likely class). The 1-class model does not yield Entropy or LMR/BLRT. Per our a priori rule, models with any class 0.95 for all classes, indicating high classification certainty, whereas off-diagonal entries (cross-class assignment) were uniformly low, consistent with the model’s entropy = 0.922. Together, these indices suggest clear separation among the three career growth profiles and stable most-likely class assignment, which we used for subsequent between-class comparisons and multivariable analyses. Table 4 APP for the three-class latent profile solution Latent profile Average posterior probability of membership in (column) C1 C2 C3 C1 0.989 0.011 0.000 C2 0.011 0.957 0.031 C3 0.000 0.036 0.964 Note: Entries are APP of class membership; Rows may not sum to 1.000 due to rounding. Figure 2 plotted CGNS item means. The three trajectories do not intersect and are generally smooth, indicating clear separation among profiles. The Constrained and uneven growth profile (C1; n = 66, 12.8%) shows uniformly lower item means with visible dips in the goal-progress and development-opportunity segments, whereas the ability-improvement segment is relatively flat. The Stable and balanced growth profile (C2; n = 238, 46.1%) exhibits minimal item-to-item variation across all segments, reflecting a steady, even pattern. The Consistent and advancing growth profile (C3; n = 212, 41.1%) maintains consistently higher means across items and a slight upward trend within the development-opportunity segment. The overall shapes align with the profile labels and support the retained three-profile solution. Univariate analysis of factors differentiating career growth profiles Among the three latent profiles (C1-C3) identified via LPA, statistically significant between-profile differences were observed for age (years), educational attainment, only-child status, marital status, parenthood, hospital tier, specialty nurse certification, monthly income (CNY), professional rank, number of night shifts per month, operating room nursing tenure, employment type, self-rated health, and interest in the nursing profession (all p 0.05). Detailed statistics are reported in Table 5 . Table 5 Univariate comparisons of sociodemographic and job characteristics across career growth profiles among early-career operating room nurses Variable and Classification C1 C2 C3 Test statistic p -value Sex Male 6 31 30 χ 2 = 1.141 0.565 Female 60 207 182 Age (years) 20–25 25 66 15 χ 2 = 58.039 <0.001 26–30 30 118 101 ≥ 31 11 54 96 Educational attainment Secondary vocational school 9 7 0 χ 2 = 105.434 <0.001 Associate degree 12 17 6 Bachelor’s degree 45 202 147 Master’s degree 0 12 54 Doctoral degree 0 0 5 Only-child status Yes 19 85 97 χ 2 = 8.042 0.017 No 47 153 115 Marital status Single 33 120 70 χ 2 = 17.483 0.002 Married 27 108 126 Divorced 6 10 16 Widowed 0 0 0 Parenthood (has children) Yes 21 72 98 χ 2 = 13.149 0.001 No 45 166 114 Hospital tier Tertiary A 32 107 162 χ 2 = 48.669 <0.001 Tertiary B 34 131 50 Homeownership status Yes 19 83 65 χ 2 = 1.351 0.509 No 47 155 147 Specialty nurse certification (obtained) Yes 15 72 114 χ 2 = 34.466 <0.001 No 51 166 98 Monthly income (CNY) ≤ 4,000 28 32 4 χ 2 = 137.566 10,000 2 9 26 Professional rank Registered nurse 31 49 11 χ 2 = 100.935 <0.001 Senior nurse 23 132 81 Charge nurse 12 57 120 Number of night shifts per month <5 30 145 182 χ 2 = 56.190 9 6 8 2 Operating room nursing tenure 3 months-1 year 27 52 17 χ 2 = 60.710 <0.001 1–3 years 23 124 85 3–5 years 16 62 110 Employment type Establishment post 29 96 136 χ 2 = 26.775 <0.001 Non-establishment post 37 142 76 Self-rated health Poor 6 5 0 χ 2 = 203.687 <0.001 Fair 10 9 0 Good 32 55 10 Very good 9 97 38 Excellent 9 72 164 Interest in the nursing profession Not at all interested 5 2 0 χ 2 = 210.371 <0.001 Slightly interested 11 7 0 Moderately interested 31 59 8 Very interested 7 96 40 Extremely interested 12 74 164 Note: C1 = Constrained and uneven growth (n = 66); C2 = Stable and balanced growth (n = 238); C3 = Consistent and advancing growth (n = 212); χ² = chi-square statistic. Results indicated that the three latent profiles of early-career operating room nurses differed significantly in several resource-related variables. Specifically, the TWS (F = 360.551, p < 0.001), the CEOCS (F = 862.409, p < 0.001), the WFSS (F = 384.959, p < 0.001), the GSE (F = 426.175, p < 0.001), and the OCMQ (F = 305.166, p < 0.001) all showed statistically significant between-profile differences. Detailed results are presented in Table 6 . Table 6 Univariate comparisons of resource-related variables across career growth profiles among early-career operating room nurses Variable C1 C2 C3 F p TWS 45.85 ± 7.29 57.07 ± 5.69 64.99 ± 3.79 360.551 < 0.001 CEOCS 32.27 ± 4.81 41.60 ± 3.38 51.95 ± 3.65 862.409 < 0.001 WFSS 52.56 ± 10.42 63.63 ± 5.62 74.93 ± 5.02 384.959 < 0.001 GSE 24.46 ± 4.51 30.17 ± 3.33 36.66 ± 2.72 426.175 < 0.001 OCMQ 42.77 ± 7.37 50.76 ± 5.46 59.48 ± 4.17 305.166 < 0.001 Note: C1 = Constrained and uneven growth (n = 66); C2 = Stable and balanced growth (n = 238); C3 = Consistent and advancing growth (n = 212). Multivariable analysis of factors associated with career growth profiles A multinomial logistic regression analysis was conducted with the three career growth profiles of early-career operating room nurses as the dependent variable, and the variables that reached statistical significance in univariate analyses as independent variables. The coding and value assignments of all independent variables are provided in Supplementary File 3. The results showed that hospital tier, monthly income (CNY), employment type, self-rated health, interest in the nursing profession, TWS, CEOCS, and OCMQ were all significant predictors of career growth profile membership ( p < 0.05). Compared with the C1, nurses with formal employment, better self-rated health, and higher scores in TWS, CEOCS, and OCMQ were more likely to belong to the C2. When comparing C1 and C3, nurses with higher monthly income, better self-rated health, and higher TWS and CEOCS scores were more likely to be classified into C3. When comparing C2 and C3, nurses employed in higher-tier hospitals, earning higher monthly income, expressing stronger interest in the nursing profession, and scoring higher on TWS and CEOCS were more likely to belong to the C3 profile. Detailed regression results are presented in Table 7 . Table 7 Multinomial logistic regression analysis of factors associated with career growth profiles among early-career operating room nurses Project OR 95% CI Wald χ 2 p C1 vs C2 Employment type Establishment post 0.410 [0.171,0.981] 4.009 0.045 Self-rated health Poor 0.171 [0.013,2.253] 1.802 0.180 Fair 0.078 [0.007,0.912] 4.133 0.042 Good 0.097 [0.015,0.607] 6.215 0.013 Very good 0.302 [0.045,2.043] 1.507 0.220 TWS 1.216 [1.056,1.400] 7.395 0.007 CEOCS 1.859 [1.505,2.295] 33.192 < 0.001 OCMQ 1.239 [1.053,1.458] 6.667 0.010 C1 vs C3 Monthly income (CNY) ≤ 4,000 0.050 [0.003,0.757] 4.669 0.031 4,001–6,000 0.470 [0.045,4.943] 0.395 0.530 6,001–8,000 1.553 [0.157,15.352] 0.142 0.706 8,001–10,000 2.568 [0.193,34.137] 0.511 0.475 Self-rated health Poor 0.001 [0, 0.001] 0.002 0.964 Fair 0.001 [0, 0.001] 0.002 0.965 Good 0.032 [0.004,0.253] 10.683 0.001 Very good 0.125 [0.016,0.999] 3.846 0.050 TWS 1.526 [1.250,1.862] 17.248 < 0.001 CEOCS 3.241 [2.481,4.234] 74.323 < 0.001 C2 vs C3 Hospital tier Tertiary A 3.238 [1.812, 5.788] 15.723 < 0.001 Monthly income (CNY) ≤ 4,000 0.066 [0.011, 0.404] 8.629 0.003 4,001–6,000 0.158 [0.039, 0.641] 6.682 0.010 6,001–8,000 0.322 [0.088, 1.178] 2.931 0.087 8,001–10,000 0.685 [0.159, 2.955] 0.258 0.612 Interest in the nursing profession Not at all interested / / / / Slightly interested / / / / Moderately interested 0.139 [0.034, 0.558] 7.733 0.005 Very interested 0.288 [0.085, 0.978] 3.981 0.046 TWS 1.255 [1.090, 1.444] 9.951 0.002 CEOCS 1.744 [1.480, 2.055] 44.072 < 0.001 Note: C1 = Constrained and uneven growth (n = 66); C2 = Stable and balanced growth (n = 238); C3 = Consistent and advancing growth (n = 212). Reference categories for categorical predictors were as follows: Hospital tier = Tertiary A; Monthly income (CNY) = > 10,000; Employment type = Non-establishment post; Self-rated health = Excellent; Interest in the nursing profession = Extremely interested. OR = odds ratio; CI = confidence interval; Wald χ² = Wald chi-square statistic. All tests were two-sided with p < 0.05 considered statistically significant. Building on the multinomial results in Table 7 , Fig. 3 locates the variables that remained significant within the COR taxonomy and indicates which a priori paths were empirically supported. Specifically, significant condition resources were employment type and hospital tier; the significant energy resource was monthly income (CNY); significant personal resources were self-rated health and interest in the nursing profession; and significant proximal psychological resources comprised TWS, CEOCS and OCMQ. In the figure, solid arrows denote the observed direct associations with career growth profiles, whereas dashed arrows represent the hypothesized indirect pathways via psychological resources to be tested in follow-up work. This classification follows COR’s distinction between condition, personal, and energy resources and the view that attitudinal/motivational states operate as proximal psychological resources that aggregate into resource clusters supporting development and resource gain processes [ 6 , 13 ]. Overall, the pattern in Fig. 3 partly validates our a priori framework (solid paths), while the mediating role of psychological resources (dashed paths) remains a target for future longitudinal/mediation analyses. Discussion This discussion situates our findings within a COR perspective and clarifies what they mean for early-career operating room nursing. We identified three distinct career growth profiles and found that condition resources (employment type, hospital tier), the energy resource (monthly income), personal resources (self-rated health, interest in the profession), and proximal psychological resources (thriving at work, organizational commitment, organizational career management) differentiated profile membership. Taken together, these results suggest that both structural endowments and psychologically proximal states matter for “who grows” in early career practice. Because our cross-sectional design estimates associations rather than causal effects, we interpret the observed links as differentiating correlates and outline a follow-up agenda to test indirect pathways through psychological resources over time. We now summarize the main findings with brief theoretical interpretation, translate them into actionable implications for managers and clinicians, and delineate limitations and directions for future research. Main findings Framed by COR, our pattern is best read as a resource-portfolio alignment: condition resources (employment type, hospital tier) and the energy resource (monthly income) create room for action, while psychological resources (thriving at work, organizational commitment, and organizational career management) translate that room into day-to-day behavior; personal resources (self-rated health, interest in the nursing profession) supply the motivational and health substrate that sustains growth. COR cautions that resource effects are dynamic and often unfold via indirect pathways, which our follow-up mediation agenda is designed to test [ 14 ]. Employment type and hospital tier function as context setters: where contracts are secure and institutions are higher-tier, nurses typically access clearer career ladders, training pipelines, and supervisory capacity, which facilitate the conversion of effort into growth-relevant experiences [ 15 ]. Contemporary evidence from China shows persistent heterogeneity in practice environments across tiers (e.g., staffing, training, governance), and qualitative work highlights how the scarcity of establishment posts in top hospitals shapes early-career opportunity structures [ 16 – 18 ]. Moreover, tiered management systems demonstrably improve competence and satisfaction, clarifying the mechanism through which “context” enables development. Read through COR, condition resources expand access and predictability, lowering threat of loss and stabilizing investment in growth [ 19 ]. Self-rated health and interest in the nursing profession provide the health and motivation substrate that sustains career growth behaviors. Evidence from healthcare workers links higher work engagement with better self-rated health and lower burnout, suggesting that individuals with stronger health/motivation baselines possess more slack capacity for development [ 20 – 22 ]. In Chinese nursing samples, career identity/interest repeatedly emerges as a correlate of engagement, consistent with COR’s view that personal resources resist loss spirals and enable later gains [ 23 ]. Thus, self-rated health and professional interest are not merely correlates but preconditions that make the uptake of developmental opportunities more likely. Monthly income functions as the energy resource that buffers depletion and underwrites learning and career behaviors [ 24 ]. Recent syntheses show that remuneration is central in nurses’ job-choice utilities, and retention-focused reviews place compensation among levers that stabilize the workforce by supporting satisfaction/engagement [ 25 , 26 ]. In COR terms, income alleviates the immediate threat of resource loss (financial strain), thereby increasing the feasibility of development that is intensive in time and effort. Conceptually, income should be treated as complementary, not substitutive: it interacts with condition resources by enabling access to ladders and training, and with psychological resources by lowering perceived loss threat so these states can emerge and persist [ 27 , 28 ]. Across measures, thriving at work, organizational commitment, and organizational career management share three features: they are malleable attitudinal-motivational states, sit close to day-to-day behavior, and are responsive to local management practices, hence they fit COR’s notion of proximal psychological resources that are most likely to change over short horizons [ 14 ]. In early-career operating room nursing, prior evidence shows that thriving is linked with lower burnout and intention to leave and with more positive work functioning; commitment remains a robust correlate of retention-relevant attitudes; and organizational career management is associated with greater nurse career growth through enhanced career management behaviors [ 29 – 31 ]. Framed by COR, these states provide the nearest channel through which condition resources, personal resources, and energy resources can plausibly be converted into growth-relevant actions and profile membership [ 14 ]. Because nurses in the more favorable growth profiles also scored higher on TWS, CEOCS, and OCMQ, our findings support a longitudinal study to test when and how these proximal psychological resources transmit contextual and income effects to career growth among early-career operating room nurses [ 14 ]. Practical implications We ground our practice guidance in three COR principles that align directly with our findings. First, the primacy-of-loss principle holds that losses loom larger than gains; in our data, risks associated with condition resources (employment type, hospital tier) and with the sufficiency of the energy resource (monthly income) indicate where loss prevention should begin [ 32 ]. Second, the resource-investment principle states that resources must be invested to protect against loss, recover losses, and build new resources; this principle maps onto our modifiable personal resources (self-rated health, interest in nursing) and onto the proximal psychological resources that convert opportunities into day-to-day behavior [ 32 ]. Third, the gain-paradox principle predicts that small gains matter most under high loss; for early-career operating room nurses, incremental improvements are therefore disproportionately valuable [ 32 ]. On this basis, we now translate these principles into concrete actions for clinical managers and units. Applying the primacy-of-loss principle and the resource-investment principle, we recommend stabilizing condition resources by securing contracts for early-career operating room nurses and making tier-linked, transparent ladders and training slots standard (e.g., structured residencies with protected preceptorship and clear supervisory capacity). Such structures reduce perceived threat of resource loss and increase predictability; contemporary reviews show nurse-residency/preceptorship models improve competence, confidence, satisfaction, and retention, while stronger practice environments are associated with lower burnout and higher engagement [ 33 – 35 ]. Guided by the gain-paradox principle, we propose raising personal resources via recovery baselines, interest-aligned rotations, and routine access to occupational-health support, which are small, credible gains that matter most in high-strain operating room settings. Evidence links better environments to reduced burnout and improved engagement [ 35 ]. For the energy resource, hospitals should underwrite learning with continuing professional development allowances (tuition/exam reimbursement) and protected learning time. Recent syntheses identify time and funding as enablers or barriers to nurses’ continuing professional development; workforce reviews also place compensation among levers that stabilize staffing, and evidence confirms that pay is a first-order attribute in job choice [ 36 ]. To turn the study’s pattern of condition resources, personal resources, and energy resources into daily growth, we focus on three psychological resources: thriving at work, organizational commitment, and organizational career management. In line with the primacy-of-loss principle, reduce uncertainty that drains resources by keeping schedules fair and predictable and access to training clear [ 37 ]. In line with the resource-investment principle, put time and supervision into meaningful work [ 38 ]. To enhance thriving at work, align tasks with individual strengths, keep workload manageable with recovery time, provide quick formative feedback, run short bedside teaching sessions, and give brief recognition during handover [ 30 ]. Build organizational commitment through supportive supervision, fair and predictable scheduling, team debriefs, and involvement in unit decisions [ 39 ]. Build organizational career management through an annual career planning meeting, visible role and promotion pathways, mentor pairing, and structured rotations that match interests [ 40 ]. In line with the gain-paradox principle, these small but credible improvements are especially valuable in high pressure operating room settings and help translate context and income into sustained growth for early-career operating room nurses. Limitations and future research directions This study has several limitations. First, the cross-sectional, self-report design precludes causal inference and raises the possibility of common-method variance. Second, several consequential contextual factors were not observed, such as staffing levels, workload intensity, supervisory ratios, and access to rotations, so omitted-variable bias cannot be ruled out. Third, although predictors were organized within the Conservation of Resources framework, measurement invariance across key subgroups was not formally examined. Future research should adopt multi-wave, multi-source designs that combine survey measures with objective indicators, and use cross-lagged or dynamic structural models to probe temporal ordering. A preregistered program should explicitly test mediation via psychological resources as proximal mechanisms through which condition, personal, and energy resources relate to career growth profiles. Finally, future studies should clarify the influence of object resources, such as tools, equipment, and information systems, because these were not retained as significant predictors in the present analysis. Conclusions Guided by COR theory, this study identified three distinct profiles of career growth among early-career operating room nurses, constrained and uneven, stable and balanced, and consistent and advancing, and showed that condition resources (employment type, hospital tier), the energy resource (monthly income), personal resources (self-rated health, interest in the nursing profession), and psychological resources (thriving at work, organizational commitment, organizational career management) differentiate profile membership. Object resources did not remain significant after adjustment. Practically, stabilizing employment and tier-linked development opportunities, underwriting continuing professional development and protected learning time, and supporting health and professional interest provide the platform on which psychological resources can convert opportunity into day-to-day growth. Because the design is cross sectional, the observed links are associative. Future work should use longitudinal and multi-source designs to test mediation through psychological resources and to examine whether investments in condition resources, personal resources and energy resources precede movements toward more favorable growth profiles. Declarations Ethics approval and consent to participate The study and consent procedure were approved by the ethics committee affiliated with Shaoxing Second Hospital (Approval No. 2025-010). Our study was conducted according to the principles of the Declaration of Helsinki and followed relevant guidelines and regulations. Informed consent was obtained from all participants before they took the survey. Consent for publication Not applicable. Competing interests The authors declare no competing interests. Funding The authors received no specific funding for this work. Author Contribution Zhihao Han: Conceptualization, Methodology, Writing-Original draft preparation. Zheyi Jiang: Conceptualization, Methodology, Investigation, Data curation. Xiaoqin Ma: Conceptualization, Methodology. Supervision, Validation, Writing Reviewing and Editing. All authors read and approved the final manuscript. Acknowledgement We are deeply grateful to the early-career operating room nurses from 102 hospitals in Zhejiang Province who generously contributed their time and experiences to this study. We also thank the head nurses, nursing managers, and on-site coordinators at each participating hospital for their support with recruitment and data collection. Data Availability The data used during this study are available from the corresponding authors on reasonable request. References Weller JM, Mahajan R, Fahey-Williams K, Webster CS. Teamwork matters: team situation awareness to build high-performing healthcare teams, a narrative review. Br J Anaesth. 2024;132(4):771–8. Li L, Huang CH, Lee YC, Wu HH. 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1","display":"","copyAsset":false,"role":"figure","size":104244,"visible":true,"origin":"","legend":"\u003cp\u003eCOR-based conceptual framework of career growth among early-career operating room nurses\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8165900/v1/c2e198c2a29308b1b60c4fc7.jpg"},{"id":98623269,"identity":"45ea1766-6117-471b-b4d7-9032e83eddaf","added_by":"auto","created_at":"2025-12-19 17:05:32","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":61848,"visible":true,"origin":"","legend":"\u003cp\u003eItem-level latent profile plot for the CGNS among early-career operating room nurses\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8165900/v1/55ba2a8ca91b2b0d994b5357.jpg"},{"id":98452846,"identity":"e169a791-ceb9-4ad3-9e3f-ead9dc15566a","added_by":"auto","created_at":"2025-12-17 17:47:42","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":92709,"visible":true,"origin":"","legend":"\u003cp\u003eCOR-based mapping of significant predictors to resource domains and hypothesized linkages to career growth profiles.\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8165900/v1/d37c15ddd07f1fcfa333d144.jpg"},{"id":98631362,"identity":"9b64d82e-aead-4912-9113-7d65729609a7","added_by":"auto","created_at":"2025-12-19 17:19:58","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1861390,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8165900/v1/cbc7d8e2-8bd0-4d5b-b02b-6593ed1486c5.pdf"},{"id":98452839,"identity":"d0466e5f-81f2-491d-9027-027da74eef62","added_by":"auto","created_at":"2025-12-17 17:47:42","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":39093,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFile1.docx","url":"https://assets-eu.researchsquare.com/files/rs-8165900/v1/55643dc9da0a9408250012e7.docx"},{"id":98452842,"identity":"4efed586-b055-4158-8f2b-12b65d30916f","added_by":"auto","created_at":"2025-12-17 17:47:42","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":33436,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFile2.docx","url":"https://assets-eu.researchsquare.com/files/rs-8165900/v1/54dbd4959e972711f8b5930f.docx"},{"id":98452847,"identity":"8ca111a0-43c3-4f22-be2b-65393303cbdc","added_by":"auto","created_at":"2025-12-17 17:47:42","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":31400,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFile3.docx","url":"https://assets-eu.researchsquare.com/files/rs-8165900/v1/9fe10bd78eabf63dcf209ee9.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Who is stressed and who is driven? A latent profile analysis of career growth among early-career operating room nurses","fulltext":[{"header":"Introduction","content":"\u003cp\u003eEarly-career operating room nurses practice in highly technical, time-sensitive environments where rotating shifts, precision teamwork, and rapid decision-making are routine [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Across seemingly similar settings, however, their trajectories of career growth diverge markedly. Recent empirical work continues to document elevated job stressors and strains in operating rooms, while also showing that supportive, well-structured workplaces can foster professional development and retention [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Against this backdrop, the present study takes an influence-factor perspective on \u0026ldquo;who grows\u0026rdquo; in early career practice and treats stress-related impediments and motivation-related facilitators as parallel inputs rather than a single continuum. To organize these inputs, we adopt Conservation of Resources (COR) theory as the guiding framework: COR posits that people strive to obtain, retain, and protect valued resources and experience strain when resources are threatened, lost, or fail to yield gains after investment; it further emphasizes that resources cluster in caravans and flow through contextual passageways [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. In line with COR, we structure the study\u0026rsquo;s potential influences as object, condition, personal, and energy resources, and we additionally consider proximal psychological resources as state-like manifestations through which resource systems often register in work settings. We treat career growth as a latent outcome and examine how the foregoing influences differentiate latent profiles of development among early-career operating room nurses [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eCOR defines resources as \u0026ldquo;objects, personal characteristics, conditions, or energies\u0026rdquo; that are valued or serve as means to valued ends, a formulation we use to delineate each base\u0026rsquo;s function [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. We distinguish four foundational resource types: object resources (tangible possessions; e.g., homeownership status), condition resources (enduring structures or states that confer access or protection; e.g., work-family support, organizational career management), personal resources (efficacious individual capacities; e.g., general self-efficacy, interest in the nursing profession), and energy resources (convertible means that enable investment; e.g., monthly income) as predictors [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. To align with refined COR accounts in organizational settings, we separate the four foundational resource bases from a proximal psychological layer because models that treat state-like constructs (e.g., organizational commitment, thriving) as the near-behavior carriers of resource systems offer a clearer and more testable explanation of how resources differentiate developmental outcomes at work [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. This theory-driven structure underpins Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and motivates our analyses of how the four bases and proximal psychological resources differentiate latent profiles of nurse professional growth.\u003c/p\u003e \u003cp\u003eBuilding on this COR-guided structure, we conceptualize career growth in early operating room practice as a categorical developmental phenomenon that can appear as distinct profiles, rather than a simple position on a single continuum. Conceptually, such profiles capture qualitatively different constellations of opportunities and constraints that young nurses face in their first years. Within COR, the four foundational resource bases \u0026ldquo;object, condition, personal, and energy\u0026rdquo; serve as predictors that may enable or hinder movement toward more favorable developmental configurations by supporting goal pursuit, safeguarding against loss, and facilitating further resource acquisition. Meanwhile, proximal psychological resources, organizational commitment and thriving at work, represent the state-like expressions through which resource systems register close to behavior and thus sit adjacent to the profiles in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. This perspective keeps the focus squarely on which resources differentiate who shows stronger growth, while leaving questions about how these resources operate over time for subsequent inquiry.\u003c/p\u003e \u003cp\u003eAgainst this COR-guided backdrop, our purpose is twofold: first, to characterize heterogeneity in career development among early-career operating room nurses by identifying latent profiles of career growth; and second, to examine which resources operate as enablers or barriers that differentiate profile membership. As depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, solid arrows indicate the associations tested in the present study (resource bases to profile membership), whereas dashed arrows mark our follow-up research agenda to evaluate whether proximal psychological resources mediate resource-growth linkages. Throughout, arrows denote statistical associations rather than causal effects; this influence-factor perspective keeps the focus on which resources distinguish who grows, while reserving temporal mechanisms for subsequent work.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eDesign and settings\u003c/h2\u003e \u003cp\u003e We conducted a multicenter, cross-sectional online survey between March and August 2025 among early-career operating room nurses employed in tertiary hospitals across Zhejiang Province, China. Hospitals from 11 prefecture-level cities were invited through nursing department liaisons. Recruitment used a WeChat QR code that redirected to a Wenjuanxing (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.wjx.cn\u003c/span\u003e\u003cspan address=\"https://www.wjx.cn\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) e-questionnaire; participation was voluntary and anonymous, with an electronic informed-consent screen gating entry to the survey. To minimize duplicate or incomplete submissions, the platform enforced one-submission limits and required complete responses prior to submission. Data were exported and verified by double entry before analysis. A total of 102 tertiary hospitals contributed valid responses.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eParticipants\u003c/h3\u003e\n\u003cp\u003eWe recruited early-career operating room nurses from tertiary hospitals across 11 cities in Zhejiang Province, China. Eligibility required holding a registered nurse license, currently working in an operating room for 3 months to 5 years, no psychiatric or cognitive disorders, and provision of electronic informed consent; interns/advanced-study nurses, staff not on duty (e.g., sick or maternity leave), intra-hospital transferees, or those unable to complete the survey for physical/mental reasons were excluded. To ensure data integrity, we further excluded incomplete submissions, responses with evident regularity/extreme patterns, or very short completion time (\u0026lt;\u0026thinsp;150 s).\u003c/p\u003e \u003cp\u003eRecruitment was coordinated through nursing departments at participating hospitals; invitees accessed the survey via WeChat QR code linking to the Wenjuanxing platform, after viewing an electronic consent screen. The sample covered 11 cities. A total of 530 nurses completed the questionnaire; after excluding 14 questionnaires for very short/incomplete responses and evident patterned or extreme responding, 516 valid cases were retained.\u003c/p\u003e\n\u003ch3\u003eMeasurements\u003c/h3\u003e\n\u003cp\u003eThis study used a structured, multi-scale questionnaire comprising: Sociodemographic and Job Characteristics (researcher-developed); the Career Growth Scale for Nurses (CGNS); the General Self-Efficacy Scale (GSE); the Thriving at Work Scale (TWS); the Chinese Employee Organizational Commitment Scale (CEOCS); the Work-Family Support Scale (WFSS); and the Organizational Career Management Questionnaire (OCMQ). For each instrument, item composition, dimensions, scoring, and reliability are reported in the subsections below. The full structured questionnaire, exact item wordings, and response options are provided in Supplementary File 1.\u003c/p\u003e\n\u003ch3\u003eSociodemographic and job characteristics\u003c/h3\u003e\n\u003cp\u003eIts were measured at baseline using a researcher-developed questionnaire that was finalized after a targeted literature review and two rounds of panel discussion. The item set captured sex, age (years), educational attainment, only-child status, marital status, parenthood (has children), hospital tier, homeownership status, specialty nurse certification (obtained), monthly income (CNY), professional rank, number of night shifts per month, operating room nursing tenure, employment type, self-rated health, and interest in the nursing profession. Two single-item constructs employed anchored scales: self-rated health (\u0026ldquo;Overall, how would you rate your health?\u0026rdquo;; 1\u0026thinsp;=\u0026thinsp;Poor, 5\u0026thinsp;=\u0026thinsp;Excellent) and interest in the nursing profession (\u0026ldquo;How interested are you in the nursing profession?\u0026rdquo;; 1\u0026thinsp;=\u0026thinsp;Not at all interested, 5\u0026thinsp;=\u0026thinsp;Extremely interested). To establish content adequacy, six subject-matter experts independently rated item relevance on a 4-point scale; the average scale-level content validity index (S-CVI/Ave) was 0.927, with per-item indices and rater details reported in Supplementary File 2. The retained item set was chosen for parsimony, coverage of the target constructs, and clarity of wording, in line with workforce survey conventions.\u003c/p\u003e\n\u003ch3\u003eCareer growth\u003c/h3\u003e\n\u003cp\u003eIt was assessed with the CGNS adapted from the employee career growth scale developed by Q X Weng and Y M Xi. [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] and validated in Chinese nurses. The CGNS comprises three dimensions: career goal progress (4 items), professional ability improvement (4 items), and career development opportunity (7 items). Responses were recorded on a 5-point Likert scale (1\u0026ndash;5) and summed to a total score ranging 15\u0026ndash;75, with higher scores indicating better career growth. In prior validation, internal consistency was Cronbach\u0026rsquo;s α\u0026thinsp;=\u0026thinsp;0.93 for the total scale, and 0.84, 0.78, 0.89 for the subscales. Example items include \u0026ldquo;My current job brings me closer to my career goals\u0026rdquo; and \u0026ldquo;My current job provides good development opportunities\u0026rdquo;.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eGeneral self-efficacy\u003c/h2\u003e \u003cp\u003eIt was measured with the GSE, developed by S K Cheung and S Y Sun [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], which assesses a broad, situation-general belief in one\u0026rsquo;s capability to cope with challenges and attain goals. The instrument comprises 10 items and demonstrates a single-factor structure with no reverse-scored items. Responses were recorded on a 1\u0026ndash;4 Likert-type scale (1\u0026thinsp;=\u0026thinsp;not at all true, 4\u0026thinsp;=\u0026thinsp;exactly true) and summed to a total score of 10\u0026ndash;40, with higher values indicating stronger general self-efficacy. Internal consistency in the present sample was excellent Cronbach\u0026rsquo;s α\u0026thinsp;=\u0026thinsp;0.94. Example items included: \u0026ldquo;If I try hard enough, I can always solve problems\u0026rdquo; and \u0026ldquo;Whatever happens to me, I can handle it well\u0026rdquo;.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eThriving at work\u003c/h3\u003e\n\u003cp\u003eIt was measured with the TWS, developed by L P Zeng et al. [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], which conceptualizes thriving as a positive psychological state jointly comprising vitality and learning. The instrument contains 10 items across two dimensions, vitality (5 items) and learning (5 items), that reflect felt energy and ongoing growth at work. Responses were recorded on a 1\u0026ndash;7 Likert-type scale (1\u0026thinsp;=\u0026thinsp;completely inconsistent, 7\u0026thinsp;=\u0026thinsp;completely consistent) and summed to a total score ranging 10\u0026ndash;70, with higher values indicating greater thriving. Internal consistency in the present sample was Cronbach\u0026rsquo;s α\u0026thinsp;=\u0026thinsp;0.88 for the total scale, and 0.76, 0.81 for vitality and learning. Example items include \u0026ldquo;I proactively learn relevant knowledge at work\u0026rdquo; and \u0026ldquo;At work, I maintain a clear mind and flexible thinking\u0026rdquo;.\u003c/p\u003e\n\u003ch3\u003eOrganizational commitment\u003c/h3\u003e\n\u003cp\u003eIt was measured with the CEOCS, developed by W Q Ling et al. [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].The instrument comprises 25 items across five dimensions: affective commitment (5 items), normative commitment (5 items), ideal commitment (5 items), economic commitment (5 items), and opportunity commitment (5 items). Responses were recorded on a 4-point Likert scale (1\u0026thinsp;=\u0026thinsp;strongly disagree, 4\u0026thinsp;=\u0026thinsp;strongly agree), with all items positively keyed. We computed subscale scores and a total score (range 25\u0026ndash;100), where higher values indicate stronger organizational commitment. Internal consistency in the present sample was excellent: Cronbach\u0026rsquo;s α\u0026thinsp;=\u0026thinsp;0.97 for the total scale, and 0.88, 0.88, 0.87, 0.92, 0.95 for five subscales, respectively. Example items include \u0026ldquo;I am willing to make any contribution to the hospital\u0026rsquo;s development\u0026rdquo; and \u0026ldquo;I am willing to devote all my efforts to the hospital\u0026rdquo;.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eWork-family support\u003c/h2\u003e \u003cp\u003eIt was measured with the WFSS, developed by Y X Li and N Zhao [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. The instrument comprises 30 items across four dimensions: organizational support (8 items), leadership support (8 items), emotional support (7 items), and instrumental support (7 items), capturing multi-source support relevant to employees\u0026rsquo; work-family interface. Responses were recorded on a 5-point Likert scale (1\u0026thinsp;=\u0026thinsp;strongly disagree, 5\u0026thinsp;=\u0026thinsp;strongly agree) with all items positively keyed; subscales were summed and aggregated to a total score ranging 30\u0026ndash;150, where higher values indicate greater work-family support. Internal consistency in the present sample was excellent: Cronbach\u0026rsquo;s α\u0026thinsp;=\u0026thinsp;0.98 for the total scale, and 0.95, 0.96, 0.93, 0.89 for organizational, leadership, emotional and instrumental support, respectively. Example items include \u0026ldquo;The organization recognizes our work achievements\u0026rdquo; and \u0026ldquo;The organization provides good welfare benefits\u0026rdquo;.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eOrganizational career management\u003c/h2\u003e \u003cp\u003eIt was measured with the OCMQ, revised by Y Xu et al. [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], which assesses employees\u0026rsquo; perceptions of career management practices within the organization. The instrument comprises 16 items across four dimensions: promotion fairness (4 items), emphasis on training (4 items), provision of career information (4 items), and facilitating organizational development (4 items). Responses were recorded on a 4-point Likert scale (1\u0026thinsp;=\u0026thinsp;not consistent, 4\u0026thinsp;=\u0026thinsp;consistent) with all items positively keyed; subscales were summed and aggregated to a total score ranging 16\u0026ndash;64, where higher values indicate more favorable organizational career management. Internal consistency in the present sample was excellent: Cronbach\u0026rsquo;s α\u0026thinsp;=\u0026thinsp;0.96 for the total scale, and 0.83, 0.86, 0.89, 0.89 for promotion fairness, emphasis on training, provision of career information, and facilitating organizational development, respectively. Example items include \u0026ldquo;The hospital promotes nurses based on their overall abilities\u0026rdquo; and \u0026ldquo;The hospital arranges experienced staff to mentor nurses\u0026rdquo;.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eData collection\u003c/h2\u003e \u003cp\u003eData were collected between March and August 2025 using an anonymous, structured online questionnaire hosted on Wenjuanxing and disseminated via QR code/URL through hospital channels. At each participating tertiary hospital in Zhejiang Province, a designated nursing-department liaison, trained with a standardized administration script, coordinated local rollout within a one-week collection window and checked basic completeness. Prior to launch, a pilot with 20 early-career operating room nurses assessed clarity and feasibility; no items were revised, and the average completion time was approximately 5 minutes. Before accessing the survey, participants viewed an electronic informed-consent page (purpose, confidentiality, voluntariness); only those who consented proceeded. No personal identifiers were collected. Platform-level anti-duplication was enabled and all items were mandatory to minimize missingness.\u003c/p\u003e \u003cp\u003eA total of 530 questionnaire submissions were received. Following pre-specified quality checks, 516 valid questionnaires were retained; 14 submissions were excluded for two reasons: (i) incomplete or very short completion time (e.g., \u0026lt; 150 s), and (ii) evident patterned or inconsistent responding. The final sample covered 11 prefecture-level cities and 102 tertiary hospitals. After closure, data were exported from WenJuanxing and underwent double-entry verification with a third-person audit against the original files prior to analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eData analysis\u003c/h2\u003e \u003cp\u003eData were entered in EpiData version 3.1 and analyzed in SPSS version 26.0 and Mplus version 8.3. Descriptive statistics were summarized as n (%) for categorical data and mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD for continuous/ordinal data. Internal consistency of each scale and subscale was evaluated using Cronbach\u0026rsquo;s α.\u003c/p\u003e \u003cp\u003eTo identify career growth profiles, we conducted latent profile analysis (LPA) in Mplus using the CGNS total score as the profile indicator. Models with one to five classes were estimated via maximum likelihood with multiple random starts. Model fit was evaluated using the Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), sample-size-adjusted BIC (aBIC), the Lo-Mendell-Rubin adjusted likelihood-ratio test (LMR-LRT), the Bootstrap Likelihood Ratio Test (BLRT), entropy, and Average Posterior Probability (APP). Lower AIC/BIC/aBIC and higher entropy, together with significant LMR-LRT/BLRT, were preferred; when AIC/BIC decreased monotonically, we selected the elbow model. We required entropy\u0026thinsp;\u0026gt;\u0026thinsp;0.80 and APP\u0026thinsp;\u0026gt;\u0026thinsp;0.70; solutions with any class\u0026thinsp;\u0026lt;\u0026thinsp;5% of the sample were discarded. Substantive interpretability was also considered when determining the optimal solution.\u003c/p\u003e \u003cp\u003eAfter the profiles were determined, we examined influencing factors across profiles. Specifically, univariate analyses compared sociodemographic and job characteristics and the resource variables (GSE, CEOCS, TWS, WFSS, OCMQ) across latent classes. Variables with \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 in univariate tests were then entered into multivariable analyses (with career growth latent class as the dependent variable) to identify independent factors differentiating the profiles. All tests were two-sided with α\u0026thinsp;=\u0026thinsp;0.05; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eEthics approval\u003c/h2\u003e \u003cp\u003eThe study protocol was reviewed and approved by the institutional ethics committee (Approval No. 2025-010). All procedures complied with the Declaration of Helsinki and relevant local regulations. Participation was voluntary. Before accessing the questionnaire, eligible nurses viewed an electronic informed-consent form describing the purpose, procedures, potential risks/benefits, confidentiality, and the right to discontinue at any time without penalty; only those who consented proceeded. The survey did not collect personal identifiers, responses were recorded anonymously, and data were used for research purposes only. De-identified datasets were stored on password-protected devices with access restricted to the study team. Results are reported in aggregate to protect participant confidentiality.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eSample characteristics\u003c/h2\u003e \u003cp\u003eParticipants were predominantly female (87.02%), with males accounting for 12.98%. Most were aged 26\u0026ndash;30 years (48.26%) and held a bachelor\u0026rsquo;s degree (76.36%). The majority were non-only children (61.05%), married (50.58%), and childless (62.98%). Respondents mainly worked in tertiary hospitals, including Tertiary A (58.33%) and Tertiary B (41.67%) institutions. Over half had not obtained a specialty nurse certification (61.05%), and the most common monthly income was CNY 4,001\u0026ndash;6,000 (34.30%). Nearly half held the title of Senior Nurse (45.74%) and worked fewer than five-night shifts per month (69.19%). In terms of tenure, most had 1\u0026ndash;3 years of operating room nursing experience (44.96%), and half were formally employed (50.58%). Regarding subjective measures, 47.48% rated their health as excellent, and 48.45% reported being extremely interested in the nursing profession. Detailed sociodemographic and job characteristics are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSociodemographic and job characteristics of early-career operating room nurses (N\u0026thinsp;=\u0026thinsp;516)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eClassification\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNumber\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eProportion (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12.98\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e449\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e87.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20\u0026ndash;25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20.54\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26\u0026ndash;30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e249\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e48.26\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e161\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e31.20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eEducational attainment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSecondary vocational school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAssociate degree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.78\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBachelor\u0026rsquo;s degree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e394\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e76.36\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMaster\u0026rsquo;s degree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12.79\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDoctoral degree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eOnly-child status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e201\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e38.95\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e315\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e61.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eMarital status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSingle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e223\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e43.22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e261\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e50.58\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDivorced\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWidowed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eParenthood (has children)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e191\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e37.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e325\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e62.98\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eHospital tier\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTertiary A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e301\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e58.33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTertiary B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e215\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e41.67\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eHomeownership status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e167\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e32.36\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e349\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e67.64\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSpecialty nurse certification (obtained)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e201\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e38.95\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e315\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e61.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eMonthly income (CNY)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;4,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12.40\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4,001\u0026ndash;6,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e177\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e34.30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6,001\u0026ndash;8,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e175\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e33.91\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8,001\u0026ndash;10,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12.21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;10,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eProfessional rank\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRegistered nurse\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17.64\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSenior nurse\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e236\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e45.74\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCharge nurse\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e189\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e36.63\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eNumber of night shifts per month\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e357\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e69.19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5\u0026ndash;9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e27.71\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eOperating room nursing tenure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 months-1 year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18.60\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u0026ndash;3 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e232\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e44.96\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u0026ndash;5 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e188\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e36.43\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eEmployment type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEstablishment post\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e261\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e50.58\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon-establishment post\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e255\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e49.42\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eSelf-rated health\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePoor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFair\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.68\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGood\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18.80\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVery good\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e27.91\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExcellent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e245\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e47.48\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eInterest in the nursing profession\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNot at all interested\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.36\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSlightly interested\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.49\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModerately interested\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18.99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVery interested\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e27.71\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExtremely interested\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e48.45\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eNote: Values in the \u0026ldquo;Proportion\u0026rdquo; column may not total 1.000 (100%) due to rounding.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eParticipants scored 51.19\u0026thinsp;\u0026plusmn;\u0026thinsp;9.98 on the CGNS, 32.11\u0026thinsp;\u0026plusmn;\u0026thinsp;5.34 on the GSE, and 58.89\u0026thinsp;\u0026plusmn;\u0026thinsp;8.13 on the TWS, and 79.67\u0026thinsp;\u0026plusmn;\u0026thinsp;12.67 on the CEOCS. The mean total score for the WFSS was 124.40\u0026thinsp;\u0026plusmn;\u0026thinsp;18.18. The OCMQ showed a mean total score of 53.32\u0026thinsp;\u0026plusmn;\u0026thinsp;7.78. Detailed subdimension scores for each scale are presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescriptive statistics of scale scores among early-career operating room nurses (N\u0026thinsp;=\u0026thinsp;516)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDimension\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDimension score\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eItem mean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTotal score\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCGNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCareer goal progress\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.52\u0026thinsp;\u0026plusmn;\u0026thinsp;2.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e3.38\u0026thinsp;\u0026plusmn;\u0026thinsp;0.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e51.19\u0026thinsp;\u0026plusmn;\u0026thinsp;9.98\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProfessional ability improvement\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.80\u0026thinsp;\u0026plusmn;\u0026thinsp;2.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e3.45\u0026thinsp;\u0026plusmn;\u0026thinsp;0.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCareer development opportunity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23.87\u0026thinsp;\u0026plusmn;\u0026thinsp;5.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e3.41\u0026thinsp;\u0026plusmn;\u0026thinsp;0.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGSE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e3.21\u0026thinsp;\u0026plusmn;\u0026thinsp;0.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e32.11\u0026thinsp;\u0026plusmn;\u0026thinsp;5.34\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTWS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLearning\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29.54\u0026thinsp;\u0026plusmn;\u0026thinsp;3.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e5.91\u0026thinsp;\u0026plusmn;\u0026thinsp;0.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e58.89\u0026thinsp;\u0026plusmn;\u0026thinsp;8.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVitality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29.35\u0026thinsp;\u0026plusmn;\u0026thinsp;4.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e5.87\u0026thinsp;\u0026plusmn;\u0026thinsp;0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCEOCS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAffective commitment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.72\u0026thinsp;\u0026plusmn;\u0026thinsp;2.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e3.14\u0026thinsp;\u0026plusmn;\u0026thinsp;0.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e79.67\u0026thinsp;\u0026plusmn;\u0026thinsp;12.67\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNormative commitment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.03\u0026thinsp;\u0026plusmn;\u0026thinsp;2.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e3.21\u0026thinsp;\u0026plusmn;\u0026thinsp;0.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIdeal commitment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.09\u0026thinsp;\u0026plusmn;\u0026thinsp;2.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e3.22\u0026thinsp;\u0026plusmn;\u0026thinsp;0.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEconomic commitment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.95\u0026thinsp;\u0026plusmn;\u0026thinsp;2.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e3.19\u0026thinsp;\u0026plusmn;\u0026thinsp;0.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOpportunity commitment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.89\u0026thinsp;\u0026plusmn;\u0026thinsp;3.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e3.18\u0026thinsp;\u0026plusmn;\u0026thinsp;0.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWFSS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOrganizational support\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41.00\u0026thinsp;\u0026plusmn;\u0026thinsp;6.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e4.10\u0026thinsp;\u0026plusmn;\u0026thinsp;0.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e124.40\u0026thinsp;\u0026plusmn;\u0026thinsp;18.18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLeadership support\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41.60\u0026thinsp;\u0026plusmn;\u0026thinsp;6.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e4.16\u0026thinsp;\u0026plusmn;\u0026thinsp;0.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEmotional support\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25.06\u0026thinsp;\u0026plusmn;\u0026thinsp;3.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e4.18\u0026thinsp;\u0026plusmn;\u0026thinsp;0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInstrumental support\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.74\u0026thinsp;\u0026plusmn;\u0026thinsp;2.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e4.19\u0026thinsp;\u0026plusmn;\u0026thinsp;0.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOCMQ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePromotion fairness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.17\u0026thinsp;\u0026plusmn;\u0026thinsp;1.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e3.29\u0026thinsp;\u0026plusmn;\u0026thinsp;0.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e53.32\u0026thinsp;\u0026plusmn;\u0026thinsp;7.78\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEmphasis on training\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.32\u0026thinsp;\u0026plusmn;\u0026thinsp;2.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e3.33\u0026thinsp;\u0026plusmn;\u0026thinsp;0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProvision of career information\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.42\u0026thinsp;\u0026plusmn;\u0026thinsp;2.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e3.36\u0026thinsp;\u0026plusmn;\u0026thinsp;0.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFacilitating organizational development\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.41\u0026thinsp;\u0026plusmn;\u0026thinsp;2.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e3.35\u0026thinsp;\u0026plusmn;\u0026thinsp;0.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eNote: Scores are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD).\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eLatent profile model selection for career growth\u003c/h2\u003e \u003cp\u003eUsing the CGNS total score as indicators, we estimated one to five-class LPA solutions (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The one-class (null) model fit worst. With additional classes, AIC/BIC/aBIC decreased, entropy for the 2\u0026ndash;5 class models was \u0026gt;\u0026thinsp;0.80, and both LMR-LRT and BLRT were significant (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), indicating that adding classes improved fit. Although the five-class solution further reduced AIC/BIC, it included a very small class (2.5%), violating our pre-specified\u0026thinsp;\u0026ge;\u0026thinsp;5% class-size rule. Compared with the three-class solution (class proportions 0.128/0.461/0.411, entropy 0.922), the four-class solution offered only marginal gains in information criteria and entropy (0.925) while splitting the medium-high range into two similarly sized classes (0.409/0.405) without clear substantive distinction. In line with parsimony, classification quality, the class-size criterion, and the COR-based expectation of low/medium/high strata in resource-related outcomes, we retained the three-class solution as the optimal representation of heterogeneity in career growth among early-career operating room nurses.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eFit indices for one- to five-class latent profile solutions of CGNS\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClass\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAIC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBIC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eaBIC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEntropy\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLMR(\u003cem\u003ep\u003c/em\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eBLRT(\u003cem\u003ep\u003c/em\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eClass proportions\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e20727.857\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20855.240\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20760.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18237.990\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18433.311\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18287.299\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.916\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.333, 0.667\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e17235.515\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17498.773\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17301.974\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.922\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.128, 0.461, 0.411\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e17010.702\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17341.898\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17094.312\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.925\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.109, 0.077, 0.409, 0.405\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e16335.328\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16734.463\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16436.089\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.951\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.025, 0.165, 0.310, 0.314, 0.186\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eNote: AIC\u0026thinsp;=\u0026thinsp;Akaike Information Criterion; BIC\u0026thinsp;=\u0026thinsp;Bayesian Information Criterion; aBIC\u0026thinsp;=\u0026thinsp;sample-size-adjusted BIC; Entropy ranges 0\u0026ndash;1 with higher values indicating better classification. LMR-LRT (\u003cem\u003ep\u003c/em\u003e) and BLRT (\u003cem\u003ep\u003c/em\u003e) test whether the k-class model fits significantly better than the (k-1)-class model. Estimated class proportions are the model-estimated proportions of participants assigned to each class (most-likely class). The 1-class model does not yield Entropy or LMR/BLRT. Per our a priori rule, models with any class\u0026thinsp;\u0026lt;\u0026thinsp;5% were considered inadmissible.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e reports the APP matrix for the retained three-class solution. Diagonal entries (within-class assignment probabilities) were \u0026gt;\u0026thinsp;0.95 for all classes, indicating high classification certainty, whereas off-diagonal entries (cross-class assignment) were uniformly low, consistent with the model\u0026rsquo;s entropy\u0026thinsp;=\u0026thinsp;0.922. Together, these indices suggest clear separation among the three career growth profiles and stable most-likely class assignment, which we used for subsequent between-class comparisons and multivariable analyses.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAPP for the three-class latent profile solution\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eLatent profile\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eAverage posterior probability of membership in (column)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eC1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eC2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC3\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.989\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.957\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.031\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.964\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eNote: Entries are APP of class membership; Rows may not sum to 1.000 due to rounding.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e plotted CGNS item means. The three trajectories do not intersect and are generally smooth, indicating clear separation among profiles. The Constrained and uneven growth profile (C1; n\u0026thinsp;=\u0026thinsp;66, 12.8%) shows uniformly lower item means with visible dips in the goal-progress and development-opportunity segments, whereas the ability-improvement segment is relatively flat. The Stable and balanced growth profile (C2; n\u0026thinsp;=\u0026thinsp;238, 46.1%) exhibits minimal item-to-item variation across all segments, reflecting a steady, even pattern. The Consistent and advancing growth profile (C3; n\u0026thinsp;=\u0026thinsp;212, 41.1%) maintains consistently higher means across items and a slight upward trend within the development-opportunity segment. The overall shapes align with the profile labels and support the retained three-profile solution.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eUnivariate analysis of factors differentiating career growth profiles\u003c/h2\u003e \u003cp\u003eAmong the three latent profiles (C1-C3) identified via LPA, statistically significant between-profile differences were observed for age (years), educational attainment, only-child status, marital status, parenthood, hospital tier, specialty nurse certification, monthly income (CNY), professional rank, number of night shifts per month, operating room nursing tenure, employment type, self-rated health, and interest in the nursing profession (all \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). In contrast, sex and homeownership status did not differ across profiles (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Detailed statistics are reported in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eUnivariate comparisons of sociodemographic and job characteristics across career growth profiles among early-career operating room nurses\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eVariable and Classification\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eC1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eC3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTest statistic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003eχ\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;1.141\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.565\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e207\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e182\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20\u0026ndash;25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003eχ\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;58.039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26\u0026ndash;30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e118\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eEducational attainment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSecondary vocational school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003eχ\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;105.434\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAssociate degree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBachelor\u0026rsquo;s degree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e202\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e147\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMaster\u0026rsquo;s degree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDoctoral degree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eOnly-child status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003eχ\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;8.042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e153\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e115\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eMarital status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSingle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003eχ\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;17.483\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e126\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDivorced\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWidowed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eParenthood (has children)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003eχ\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;13.149\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e166\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e114\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eHospital tier\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTertiary A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e107\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e162\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003eχ\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;48.669\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTertiary B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eHomeownership status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003eχ\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;1.351\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.509\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e155\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e147\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSpecialty nurse certification (obtained)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e114\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003eχ\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;34.466\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e166\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eMonthly income (CNY)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;4,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003eχ\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;137.566\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4,001\u0026ndash;6,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6,001\u0026ndash;8,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8,001\u0026ndash;10,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;10,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eProfessional rank\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRegistered nurse\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003eχ\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;100.935\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSenior nurse\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e132\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCharge nurse\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eNumber of night shifts per month\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e145\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e182\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003eχ\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;56.190\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5\u0026ndash;9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eOperating room nursing tenure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 months-1 year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003eχ\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;60.710\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u0026ndash;3 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e124\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u0026ndash;5 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eEmployment type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEstablishment post\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e136\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003eχ\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;26.775\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon-establishment post\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e142\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eSelf-rated health\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePoor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003eχ\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;203.687\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFair\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGood\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVery good\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExcellent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e164\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eInterest in the nursing profession\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNot at all interested\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003eχ\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;210.371\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSlightly interested\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModerately interested\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVery interested\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExtremely interested\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e164\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eNote: C1\u0026thinsp;=\u0026thinsp;Constrained and uneven growth (n\u0026thinsp;=\u0026thinsp;66); C2\u0026thinsp;=\u0026thinsp;Stable and balanced growth (n\u0026thinsp;=\u0026thinsp;238); C3\u0026thinsp;=\u0026thinsp;Consistent and advancing growth (n\u0026thinsp;=\u0026thinsp;212); χ\u0026sup2; = chi-square statistic.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eResults indicated that the three latent profiles of early-career operating room nurses differed significantly in several resource-related variables. Specifically, the TWS (F\u0026thinsp;=\u0026thinsp;360.551, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), the CEOCS (F\u0026thinsp;=\u0026thinsp;862.409, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), the WFSS (F\u0026thinsp;=\u0026thinsp;384.959, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), the GSE (F\u0026thinsp;=\u0026thinsp;426.175, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and the OCMQ (F\u0026thinsp;=\u0026thinsp;305.166, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) all showed statistically significant between-profile differences. Detailed results are presented in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eUnivariate comparisons of resource-related variables across career growth profiles among early-career operating room nurses\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eC1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eC2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTWS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e45.85\u0026thinsp;\u0026plusmn;\u0026thinsp;7.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e57.07\u0026thinsp;\u0026plusmn;\u0026thinsp;5.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e64.99\u0026thinsp;\u0026plusmn;\u0026thinsp;3.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e360.551\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCEOCS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e32.27\u0026thinsp;\u0026plusmn;\u0026thinsp;4.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e41.60\u0026thinsp;\u0026plusmn;\u0026thinsp;3.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e51.95\u0026thinsp;\u0026plusmn;\u0026thinsp;3.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e862.409\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWFSS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e52.56\u0026thinsp;\u0026plusmn;\u0026thinsp;10.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e63.63\u0026thinsp;\u0026plusmn;\u0026thinsp;5.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e74.93\u0026thinsp;\u0026plusmn;\u0026thinsp;5.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e384.959\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGSE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e24.46\u0026thinsp;\u0026plusmn;\u0026thinsp;4.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e30.17\u0026thinsp;\u0026plusmn;\u0026thinsp;3.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e36.66\u0026thinsp;\u0026plusmn;\u0026thinsp;2.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e426.175\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOCMQ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e42.77\u0026thinsp;\u0026plusmn;\u0026thinsp;7.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e50.76\u0026thinsp;\u0026plusmn;\u0026thinsp;5.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e59.48\u0026thinsp;\u0026plusmn;\u0026thinsp;4.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e305.166\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eNote: C1\u0026thinsp;=\u0026thinsp;Constrained and uneven growth (n\u0026thinsp;=\u0026thinsp;66); C2\u0026thinsp;=\u0026thinsp;Stable and balanced growth (n\u0026thinsp;=\u0026thinsp;238); C3\u0026thinsp;=\u0026thinsp;Consistent and advancing growth (n\u0026thinsp;=\u0026thinsp;212).\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eMultivariable analysis of factors associated with career growth profiles\u003c/h2\u003e \u003cp\u003e A multinomial logistic regression analysis was conducted with the three career growth profiles of early-career operating room nurses as the dependent variable, and the variables that reached statistical significance in univariate analyses as independent variables. The coding and value assignments of all independent variables are provided in Supplementary File 3. The results showed that hospital tier, monthly income (CNY), employment type, self-rated health, interest in the nursing profession, TWS, CEOCS, and OCMQ were all significant predictors of career growth profile membership (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003eCompared with the C1, nurses with formal employment, better self-rated health, and higher scores in TWS, CEOCS, and OCMQ were more likely to belong to the C2. When comparing C1 and C3, nurses with higher monthly income, better self-rated health, and higher TWS and CEOCS scores were more likely to be classified into C3. When comparing C2 and C3, nurses employed in higher-tier hospitals, earning higher monthly income, expressing stronger interest in the nursing profession, and scoring higher on TWS and CEOCS were more likely to belong to the C3 profile. Detailed regression results are presented in Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultinomial logistic regression analysis of factors associated with career growth profiles among early-career operating room nurses\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProject\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWald χ\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eC1 vs C2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEmployment type\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEstablishment post\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.410\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[0.171,0.981]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.045\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSelf-rated health\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.171\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[0.013,2.253]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.802\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.180\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFair\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.078\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[0.007,0.912]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.133\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.042\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGood\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.097\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[0.015,0.607]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.215\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVery good\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.302\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[0.045,2.043]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.507\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.220\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTWS\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.216\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[1.056,1.400]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.395\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCEOCS\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.859\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[1.505,2.295]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e33.192\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOCMQ\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.239\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[1.053,1.458]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.667\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eC1 vs C3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMonthly income (CNY)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;4,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.050\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[0.003,0.757]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.669\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.031\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4,001\u0026ndash;6,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.470\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[0.045,4.943]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.395\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.530\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6,001\u0026ndash;8,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.553\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[0.157,15.352]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.142\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.706\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8,001\u0026ndash;10,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.568\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[0.193,34.137]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.511\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.475\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSelf-rated health\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[0, 0.001]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.964\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFair\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[0, 0.001]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.965\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGood\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[0.004,0.253]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.683\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVery good\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[0.016,0.999]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.846\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.050\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTWS\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.526\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[1.250,1.862]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17.248\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCEOCS\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.241\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[2.481,4.234]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e74.323\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eC2 vs C3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHospital tier\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTertiary A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.238\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[1.812, 5.788]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15.723\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMonthly income (CNY)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;4,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.066\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[0.011, 0.404]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.629\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4,001\u0026ndash;6,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.158\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[0.039, 0.641]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.682\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6,001\u0026ndash;8,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.322\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[0.088, 1.178]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.931\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.087\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8,001\u0026ndash;10,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.685\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[0.159, 2.955]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.258\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.612\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eInterest in the nursing profession\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNot at all interested\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSlightly interested\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerately interested\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.139\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[0.034, 0.558]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.733\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVery interested\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.288\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[0.085, 0.978]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.981\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.046\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTWS\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.255\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[1.090, 1.444]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.951\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCEOCS\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.744\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[1.480, 2.055]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e44.072\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eNote: C1\u0026thinsp;=\u0026thinsp;Constrained and uneven growth (n\u0026thinsp;=\u0026thinsp;66); C2\u0026thinsp;=\u0026thinsp;Stable and balanced growth (n\u0026thinsp;=\u0026thinsp;238); C3\u0026thinsp;=\u0026thinsp;Consistent and advancing growth (n\u0026thinsp;=\u0026thinsp;212). Reference categories for categorical predictors were as follows: Hospital tier\u0026thinsp;=\u0026thinsp;Tertiary A; Monthly income (CNY)\u0026thinsp;=\u0026thinsp;\u0026gt;\u0026thinsp;10,000; Employment type\u0026thinsp;=\u0026thinsp;Non-establishment post; Self-rated health\u0026thinsp;=\u0026thinsp;Excellent; Interest in the nursing profession\u0026thinsp;=\u0026thinsp;Extremely interested. OR\u0026thinsp;=\u0026thinsp;odds ratio; CI\u0026thinsp;=\u0026thinsp;confidence interval; Wald χ\u0026sup2; = Wald chi-square statistic. All tests were two-sided with \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 considered statistically significant.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eBuilding on the multinomial results in Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e locates the variables that remained significant within the COR taxonomy and indicates which a priori paths were empirically supported. Specifically, significant condition resources were employment type and hospital tier; the significant energy resource was monthly income (CNY); significant personal resources were self-rated health and interest in the nursing profession; and significant proximal psychological resources comprised TWS, CEOCS and OCMQ. In the figure, solid arrows denote the observed direct associations with career growth profiles, whereas dashed arrows represent the hypothesized indirect pathways via psychological resources to be tested in follow-up work. This classification follows COR\u0026rsquo;s distinction between condition, personal, and energy resources and the view that attitudinal/motivational states operate as proximal psychological resources that aggregate into resource clusters supporting development and resource gain processes [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Overall, the pattern in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e partly validates our a priori framework (solid paths), while the mediating role of psychological resources (dashed paths) remains a target for future longitudinal/mediation analyses.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis discussion situates our findings within a COR perspective and clarifies what they mean for early-career operating room nursing. We identified three distinct career growth profiles and found that condition resources (employment type, hospital tier), the energy resource (monthly income), personal resources (self-rated health, interest in the profession), and proximal psychological resources (thriving at work, organizational commitment, organizational career management) differentiated profile membership. Taken together, these results suggest that both structural endowments and psychologically proximal states matter for \u0026ldquo;who grows\u0026rdquo; in early career practice. Because our cross-sectional design estimates associations rather than causal effects, we interpret the observed links as differentiating correlates and outline a follow-up agenda to test indirect pathways through psychological resources over time. We now summarize the main findings with brief theoretical interpretation, translate them into actionable implications for managers and clinicians, and delineate limitations and directions for future research.\u003c/p\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eMain findings\u003c/h2\u003e \u003cp\u003e Framed by COR, our pattern is best read as a resource-portfolio alignment: condition resources (employment type, hospital tier) and the energy resource (monthly income) create room for action, while psychological resources (thriving at work, organizational commitment, and organizational career management) translate that room into day-to-day behavior; personal resources (self-rated health, interest in the nursing profession) supply the motivational and health substrate that sustains growth. COR cautions that resource effects are dynamic and often unfold via indirect pathways, which our follow-up mediation agenda is designed to test [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eEmployment type and hospital tier function as context setters: where contracts are secure and institutions are higher-tier, nurses typically access clearer career ladders, training pipelines, and supervisory capacity, which facilitate the conversion of effort into growth-relevant experiences [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Contemporary evidence from China shows persistent heterogeneity in practice environments across tiers (e.g., staffing, training, governance), and qualitative work highlights how the scarcity of establishment posts in top hospitals shapes early-career opportunity structures [\u003cspan additionalcitationids=\"CR17\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Moreover, tiered management systems demonstrably improve competence and satisfaction, clarifying the mechanism through which \u0026ldquo;context\u0026rdquo; enables development. Read through COR, condition resources expand access and predictability, lowering threat of loss and stabilizing investment in growth [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSelf-rated health and interest in the nursing profession provide the health and motivation substrate that sustains career growth behaviors. Evidence from healthcare workers links higher work engagement with better self-rated health and lower burnout, suggesting that individuals with stronger health/motivation baselines possess more slack capacity for development [\u003cspan additionalcitationids=\"CR21\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. In Chinese nursing samples, career identity/interest repeatedly emerges as a correlate of engagement, consistent with COR\u0026rsquo;s view that personal resources resist loss spirals and enable later gains [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Thus, self-rated health and professional interest are not merely correlates but preconditions that make the uptake of developmental opportunities more likely.\u003c/p\u003e \u003cp\u003eMonthly income functions as the energy resource that buffers depletion and underwrites learning and career behaviors [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Recent syntheses show that remuneration is central in nurses\u0026rsquo; job-choice utilities, and retention-focused reviews place compensation among levers that stabilize the workforce by supporting satisfaction/engagement [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. In COR terms, income alleviates the immediate threat of resource loss (financial strain), thereby increasing the feasibility of development that is intensive in time and effort. Conceptually, income should be treated as complementary, not substitutive: it interacts with condition resources by enabling access to ladders and training, and with psychological resources by lowering perceived loss threat so these states can emerge and persist [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAcross measures, thriving at work, organizational commitment, and organizational career management share three features: they are malleable attitudinal-motivational states, sit close to day-to-day behavior, and are responsive to local management practices, hence they fit COR\u0026rsquo;s notion of proximal psychological resources that are most likely to change over short horizons [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. In early-career operating room nursing, prior evidence shows that thriving is linked with lower burnout and intention to leave and with more positive work functioning; commitment remains a robust correlate of retention-relevant attitudes; and organizational career management is associated with greater nurse career growth through enhanced career management behaviors [\u003cspan additionalcitationids=\"CR30\" citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Framed by COR, these states provide the nearest channel through which condition resources, personal resources, and energy resources can plausibly be converted into growth-relevant actions and profile membership [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Because nurses in the more favorable growth profiles also scored higher on TWS, CEOCS, and OCMQ, our findings support a longitudinal study to test when and how these proximal psychological resources transmit contextual and income effects to career growth among early-career operating room nurses [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003ePractical implications\u003c/h2\u003e \u003cp\u003eWe ground our practice guidance in three COR principles that align directly with our findings. First, the primacy-of-loss principle holds that losses loom larger than gains; in our data, risks associated with condition resources (employment type, hospital tier) and with the sufficiency of the energy resource (monthly income) indicate where loss prevention should begin [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Second, the resource-investment principle states that resources must be invested to protect against loss, recover losses, and build new resources; this principle maps onto our modifiable personal resources (self-rated health, interest in nursing) and onto the proximal psychological resources that convert opportunities into day-to-day behavior [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Third, the gain-paradox principle predicts that small gains matter most under high loss; for early-career operating room nurses, incremental improvements are therefore disproportionately valuable [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. On this basis, we now translate these principles into concrete actions for clinical managers and units.\u003c/p\u003e \u003cp\u003eApplying the primacy-of-loss principle and the resource-investment principle, we recommend stabilizing condition resources by securing contracts for early-career operating room nurses and making tier-linked, transparent ladders and training slots standard (e.g., structured residencies with protected preceptorship and clear supervisory capacity). Such structures reduce perceived threat of resource loss and increase predictability; contemporary reviews show nurse-residency/preceptorship models improve competence, confidence, satisfaction, and retention, while stronger practice environments are associated with lower burnout and higher engagement [\u003cspan additionalcitationids=\"CR34\" citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Guided by the gain-paradox principle, we propose raising personal resources via recovery baselines, interest-aligned rotations, and routine access to occupational-health support, which are small, credible gains that matter most in high-strain operating room settings. Evidence links better environments to reduced burnout and improved engagement [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. For the energy resource, hospitals should underwrite learning with continuing professional development allowances (tuition/exam reimbursement) and protected learning time. Recent syntheses identify time and funding as enablers or barriers to nurses\u0026rsquo; continuing professional development; workforce reviews also place compensation among levers that stabilize staffing, and evidence confirms that pay is a first-order attribute in job choice [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTo turn the study\u0026rsquo;s pattern of condition resources, personal resources, and energy resources into daily growth, we focus on three psychological resources: thriving at work, organizational commitment, and organizational career management. In line with the primacy-of-loss principle, reduce uncertainty that drains resources by keeping schedules fair and predictable and access to training clear [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. In line with the resource-investment principle, put time and supervision into meaningful work [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. To enhance thriving at work, align tasks with individual strengths, keep workload manageable with recovery time, provide quick formative feedback, run short bedside teaching sessions, and give brief recognition during handover [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Build organizational commitment through supportive supervision, fair and predictable scheduling, team debriefs, and involvement in unit decisions [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Build organizational career management through an annual career planning meeting, visible role and promotion pathways, mentor pairing, and structured rotations that match interests [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. In line with the gain-paradox principle, these small but credible improvements are especially valuable in high pressure operating room settings and help translate context and income into sustained growth for early-career operating room nurses.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003eLimitations and future research directions\u003c/h2\u003e \u003cp\u003eThis study has several limitations. First, the cross-sectional, self-report design precludes causal inference and raises the possibility of common-method variance. Second, several consequential contextual factors were not observed, such as staffing levels, workload intensity, supervisory ratios, and access to rotations, so omitted-variable bias cannot be ruled out. Third, although predictors were organized within the Conservation of Resources framework, measurement invariance across key subgroups was not formally examined. Future research should adopt multi-wave, multi-source designs that combine survey measures with objective indicators, and use cross-lagged or dynamic structural models to probe temporal ordering. A preregistered program should explicitly test mediation via psychological resources as proximal mechanisms through which condition, personal, and energy resources relate to career growth profiles. Finally, future studies should clarify the influence of object resources, such as tools, equipment, and information systems, because these were not retained as significant predictors in the present analysis.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003e Guided by COR theory, this study identified three distinct profiles of career growth among early-career operating room nurses, constrained and uneven, stable and balanced, and consistent and advancing, and showed that condition resources (employment type, hospital tier), the energy resource (monthly income), personal resources (self-rated health, interest in the nursing profession), and psychological resources (thriving at work, organizational commitment, organizational career management) differentiate profile membership. Object resources did not remain significant after adjustment. Practically, stabilizing employment and tier-linked development opportunities, underwriting continuing professional development and protected learning time, and supporting health and professional interest provide the platform on which psychological resources can convert opportunity into day-to-day growth. Because the design is cross sectional, the observed links are associative. Future work should use longitudinal and multi-source designs to test mediation through psychological resources and to examine whether investments in condition resources, personal resources and energy resources precede movements toward more favorable growth profiles.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eEthics approval and consent to participate\u003c/h2\u003e \u003cp\u003eThe study and consent procedure were approved by the ethics committee affiliated with Shaoxing Second Hospital (Approval No. 2025-010). Our study was conducted according to the principles of the Declaration of Helsinki and followed relevant guidelines and regulations. Informed consent was obtained from all participants before they took the survey.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent for publication\u003c/strong\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eCompeting interests\u003c/strong\u003e \u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThe authors received no specific funding for this work.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eZhihao Han: Conceptualization, Methodology, Writing-Original draft preparation. Zheyi Jiang: Conceptualization, Methodology, Investigation, Data curation. Xiaoqin Ma: Conceptualization, Methodology. Supervision, Validation, Writing Reviewing and Editing. All authors read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003e We are deeply grateful to the early-career operating room nurses from 102 hospitals in Zhejiang Province who generously contributed their time and experiences to this study. We also thank the head nurses, nursing managers, and on-site coordinators at each participating hospital for their support with recruitment and data collection.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe data used during this study are available from the corresponding authors on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWeller JM, Mahajan R, Fahey-Williams K, Webster CS. Teamwork matters: team situation awareness to build high-performing healthcare teams, a narrative review. 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Worldviews Evidence-Based Nurs. 2025;22(2):e70009.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-nursing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nurs","sideBox":"Learn more about [BMC Nursing](http://bmcnurs.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/nurs/default.aspx","title":"BMC Nursing","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Job stress, career growth, Latent profile analysis, Conservation of resources theory, Operating room nurses","lastPublishedDoi":"10.21203/rs.3.rs-8165900/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8165900/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eEarly-career operating room nurses work in highly technical, high-demand environments and are at elevated risk of stress, stalled development and turnover. Yet little is known about how their career growth patterns differ, or which resource configurations support more favorable trajectories. Guided by Conservation of Resources (COR) theory, this study aimed to (1) identify latent profiles of career growth among early-career operating room nurses and (2) examine how condition, personal, energy and proximal psychological resources differentiate these profiles.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003e We conducted a multicenter cross-sectional online survey between March and August 2025 among early-career operating room nurses working in tertiary hospitals in Zhejiang Province, China. Using multi-stage cluster sampling, 516 nurses from 102 hospitals provided valid responses. Career growth was assessed using the Career Growth of Nurses Scale, and resources were organized a priori into object, condition, personal, energy and proximal psychological resource categories. Latent profile analysis was used to identify distinct career growth profiles. Multinomial logistic regression examined associations between resource variables and profile membership, with statistical significance set at α\u0026thinsp;=\u0026thinsp;0.05.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA three-profile solution provided the best fit to the data. We identified a Constrained and uneven profile (C1, n\u0026thinsp;=\u0026thinsp;66, 12.8%), a Stable and balanced profile (C2, n\u0026thinsp;=\u0026thinsp;238, 46.1%) and a Consistent and advancing profile (C3, n\u0026thinsp;=\u0026thinsp;212, 41.1%). Compared with C1, nurses with formal employment contracts, working in higher-tier hospitals, reporting higher monthly income, better self-rated health and stronger interest in the nursing profession were more likely to be classified into C2 and C3. Higher levels of thriving at work, organizational commitment and perceived organizational career management further distinguished nurses in the more favorable profiles. Object resources showed no independent associations after adjustment.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eEarly-career operating room nurses exhibit heterogeneous patterns of career growth that cluster into constrained, stable and advancing profiles. COR-informed resource portfolios, particularly stable employment and hospital context, adequate income, good health and interest in nursing, and strong proximal psychological resources, are associated with more adaptive profiles. Nurse managers can use these findings to design resource-focused strategies and targeted development programs to support sustainable career growth and retention among early-career operating room nurses.\u003c/p\u003e","manuscriptTitle":"Who is stressed and who is driven? A latent profile analysis of career growth among early-career operating room nurses","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-17 17:47:29","doi":"10.21203/rs.3.rs-8165900/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-03-17T04:42:15+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-13T02:40:20+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"109255383974917476123016345546230256760","date":"2025-12-14T10:29:10+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-13T08:39:57+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"23423543700315398747268798333644359997","date":"2025-12-13T04:00:19+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-12-12T09:30:16+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-11-21T10:03:38+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-11-21T06:29:16+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-11-21T06:27:33+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Nursing","date":"2025-11-20T14:52:46+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-nursing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nurs","sideBox":"Learn more about [BMC Nursing](http://bmcnurs.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/nurs/default.aspx","title":"BMC Nursing","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"8e42e467-4428-4e17-bda9-850f8955f7fe","owner":[],"postedDate":"December 17th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-19T09:23:39+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-17 17:47:29","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8165900","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8165900","identity":"rs-8165900","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}