Training Intention and Latent Profiles of Traditional Chinese Medicine (TCM) Nursing Competency: Associated Factors Identified via Latent Profile Analysis

Training Intention and Latent Profiles of Traditional Chinese Medicine (TCM) Nursing Competency: Associated Factors Identified via Latent Profile Analysis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Training Intention and Latent Profiles of Traditional Chinese Medicine (TCM) Nursing Competency: Associated Factors Identified via Latent Profile Analysis Ming Jin Jin, Yongping Lu Lu, Jing Yang, Zhiqiang Wang, Yating Shi, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8921882/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 11 You are reading this latest preprint version Abstract Objective To assess the overall level of self-assessed maturity of Traditional Chinese Medicine (TCM) nursing competency among clinical nurses, identify latent profiles, and examine factors associated with profile membership. Methods From January to October 2024, a cross-sectional survey was conducted at a tertiary Grade A hospital in China. Eligible nurses were recruited through hospital-wide questionnaire distribution and voluntary participation. A total of 457 valid questionnaires were analyzed. Data were collected using the “Traditional Chinese Medicine Nursing Competency Questionnaire” and a TCM-nursing staff training needs questionnaire. Latent profile analysis (LPA) was used to classify competency maturity profiles, and multinomial logistic regression was performed to identify associated factors. Results Overall maturity of TCM nursing competency was moderate. Scores were higher for TCM nursing techniques but lower for advanced clinical practice, indicating relatively limited advanced-practice and integrative application capacity. LPA supported a three-profile solution (entropy = 0.846): Developing (12.3%), Transitional (64.3%), and Mature (23.4%) profiles. Profiles differed in years of service, professional title, educational attainment, intention to participate in training, and perceived importance of training (p < 0.05). In multinomial logistic regression, lower educational attainment, weaker intention to participate in training, and lower perceived importance of training were associated with membership in lower-maturity profiles; professional title was significant in selected comparisons, whereas years of service was not independently associated after adjustment. Conclusions Self-assessed TCM nursing competency maturity among clinical nurses shows a stratified profile pattern dominated by a Transitional group, with advanced clinical practice as a relative weakness. Profile-informed, tiered development and targeted training—emphasizing contextualized practice and translation of competencies into clinical practice—may be warranted. These associations should be further verified in longitudinal or intervention studies. Traditional Chinese Medicine (TCM) nursing competency competency maturity latent profile analysis (LPA) clinical nurses cross-sectional study intention to participate in training training needs multinomial logistic regression Figures Figure 1 Introduction The trend of population aging and an increasing burden of noncommunicable diseases (NCDs) and other chronic health conditions requires health systems to prioritize long-term management and continuous, integrated care rather than episodic treatment of acute illness [ 1 ] . According to projections and statistics provided by the World Health Organization (WHO), the percentage of the aged population is on the rise and the number of deaths caused by NCDs is high [ 2 – 4 ] . One systematic review approximated that a considerable proportion (46%) of older individuals have multimorbidity (more than two chronic illnesses) [ 5 ] , and multimorbidity requires more person-centered integrated care [ 6 – 7 ] . Continuity of care is also highly dependent on nurses, who serve as a pillar of continuity, and their competence is directly associated with the quality of chronic disease management and the effectiveness of health promotion [ 8 – 9 ] . As such, defining and consolidating key nursing competencies in long-term care settings is a significant driver of chronic disease management and healthy aging. In the context of the Healthy China 2030 strategy, China has gone further to conserve and develop traditional Chinese medicine (TCM) [ 10 ] . One of the national strategic plans in the development of TCM (2016–2030) points to health promotion initiatives based on the concept of preventive treatment of disease [ 11 ] . The National Plan of the Development of Nursing Services (2021–2025) recommends competency-oriented training systems, and enhancement of standards and specifications regarding TCM nursing skills, procedures, and practice [ 12 – 14 ] . TCM nursing competency in geriatric, rehabilitation, and chronic disease care should encompass a solid theoretical foundation (including pattern differentiation), proficiency in TCM nursing techniques, and the ability to integrate these competencies into clinical practice. Nevertheless, there is heterogeneity in access to training, opportunities for clinical application, and transfer of training to practice among clinical nurses, implying substantial individual differences in competency levels [ 15 ] . Therefore, evaluation based solely on mean levels may be insufficient for guiding training-resource allocation. TCM nursing competency measurement models are beginning to emerge, such as an indicator system of core competencies among nurses using TCM specialty based on evidence synthesis [ 16 ] , and the creation and testing of a competency scale of clinical care in TCM hospitals among nurses [ 17 ] . However, most studies have relied on variable-centered approaches that emphasize average levels and may obscure latent subgroups [ 18 ] . Benner’s novice-to-expert model highlights that competency development is staged and context-dependent [ 19 ] , providing a theoretical rationale for stratification. Operationally, the present study defines TCM nursing competency maturity as a self-reported, stage-informed construct, intended to describe differences in developmental levels rather than to evaluate a formal, process-oriented maturity model. Person-centered latent profile analysis (LPA) can identify latent classes that differ in competency patterns within a population. Emerging evidence supports LPA for classifying nurses’ competencies and training needs and indicates that training experiences and educational attainment are associated with class membership [ 20 – 21 ] . However, in TCM nursing, empirical studies remain scarce—particularly those integrating training-motivation–related indicators with competency profiles. Accordingly, analytic strategies that foreground population heterogeneity are needed to identify TCM nursing competency profiles and explore their correlates. Competency development is also shaped by learning motivation. Self-determination theory posits that fulfilling needs for autonomy, competence, and relatedness fosters high-quality motivation and sustained engagement in learning [ 22 – 23 ] . Studies of nurses’ continuing professional development similarly suggest that intrinsic and extrinsic motivation, together with organizational support, jointly influence participation in continuing education [ 24 ] . On this basis, we expected that nurses across competency profiles might differ in intention to participate in training and in perceived importance of training, and that these factors could be associated with profile membership. We conducted LPA using indicators of basic knowledge, TCM nursing techniques, and advanced clinical practice to identify latent profiles of TCM nursing competency maturity among clinical nurses. We then used multinomial logistic regression to examine associations of demographic characteristics, intention to participate in training, and perceived importance of training with profile membership, aiming to inform stratified training and training-resource allocation in health care institutions. By linking competency heterogeneity with motivation-related correlates, this study seeks to generate more actionable evidence to support stratified, precision-oriented decision-making for developing the TCM nursing workforce. Methods Ethics Approval and Informed Consent This cross-sectional study involved no intervention. The study was conducted in accordance with the ethical principles of respect, justice, and beneficence outlined in the Belmont Report. Participation was voluntary; participants were informed of the study objectives and their right to withdraw at any time. To protect privacy, the questionnaire was completed anonymously, and data were used only for academic research. The study protocol was approved by the Ethics Committee of Guangyuan Central Hospital (approval no. GYZXLL2024010). Written and verbal informed consent was obtained from all participants. The study was conducted in accordance with the Declaration of Helsinki. Study Design and Participants This questionnaire-based cross-sectional quantitative study was conducted from January to October 2024 at a tertiary Grade A hospital in Guangyuan City, China. With coordination from the Nursing Department, questionnaires were distributed hospital-wide to on-duty clinical nurses who met the eligibility criteria, and participation was voluntary; therefore, a convenience sample was obtained. Participants were recruited from multiple departments providing Traditional Chinese Medicine (TCM) nursing services and represented different job grades. Data were collected once within the same survey window. In total, 457 questionnaires were deemed valid and included in the analysis. Inclusion criteria were: (1) on-duty clinical nurses employed at the hospital who were able to comprehend and complete the questionnaire; (2) provided informed consent and participated voluntarily; (3) able to complete the questionnaire independently; and (4) engaged in frontline TCM nursing work, defined as working in a department that provides TCM nursing services and delivers TCM nursing interventions. Exclusion criteria were: (1) non-nursing personnel; (2) individuals who were not on duty and unable to participate because of leave or off-site training/further education; (3) continuous sick leave or maternity leave for ≥ 3 months; and (4) nurses undertaking a clinical placement/rotation in the department. Questionnaires were excluded as invalid if: (1) internal inconsistencies were identified upon screening; (2) the same response option was selected for all items; or (3) responses did not adhere to the questionnaire instructions. Measurement Instruments General Information Questionnaire A general information questionnaire was developed by the research team through a literature review and expert consultation to assess participants’ demographic and work-related characteristics. Items included sex, age, years of nursing experience, professional title, employment type, highest educational attainment, graduating institution, and whether the participant’s department provided TCM nursing services and offered TCM nursing techniques/interventions. Traditional Chinese Medicine Nursing Competence Questionnaire TCM nursing competency was assessed using the Traditional Chinese Medicine Nursing Competency Questionnaire developed by Wang Dan et al. (2018) [ 25 ] . The instrument comprises 37 items across three dimensions: basic knowledge of TCM nursing (12 items), TCM nursing techniques (18 items), and advanced TCM nursing clinical practice (7 items). Each item is rated on a 5-point Likert scale of mastery/familiarity (1 = very unfamiliar, 2 = unfamiliar, 3 = moderate, 4 = familiar, 5 = very familiar). Higher scores indicate greater TCM nursing competency. Total scores range from 37 to 185; dimension mean scores (1–5) may also be used for cross-dimension comparisons. The Cronbach’s α coefficients for the three dimensions were 0.91, 0.88, and 0.96, respectively, and the corresponding content validity index (CVI) values were 0.83, 0.91, and 0.80, respectively. Training Needs Questionnaire for TCM Nursing Staff in Public Hospitals The Training Needs Questionnaire of the TCM Nursing Staff in Public Hospital created by Ye Meixia et al. [ 26 ] was used to measure training needs. The questionnaire includes six domains: intention to participate in training, perceived importance, training frequency, modalities of theoretical training, modalities of skills training, and training content. A pilot survey and psychometric testing were conducted; the cumulative variance explained was 62.1%, and the overall Cronbach’s α was 0.84. Data Quality Control Quality control processes were applied both in the processes of administering questionnaires and data processing in order to improve accuracy, completeness, and analyzability of their data. A standard set of questions and scoring were tested before this questionnaire was distributed. It was self-completed and the respondents got standardized guidelines about the aim of the study and confidentiality in order to reduce social desirability and non response. Questionnaires were then filtered after found to contain a lot of missing data, bad internal consistency pattern, or profile of one-dimensional response across the whole set. Coding of data was done based on a standardized codebook and inputted into database with a form of two versus one or random check. It was subjected to logical range checks and consistency checks to ensure that the implausible values and possible outliers were checked with original records and corrected where necessary. Missing data were handled using available-case analysis where applicable. Common method bias (CMB) (Harman’s single-factor test) and internal consistency reliability (Cronbach’s α) were evaluated to support subsequent latent profile analysis (LPA) and regression modeling. Data Analysis Valid questionnaires (n = 457) were included in the analyses. Descriptive statistics and LPA were conducted. Item scores were summed according to scoring rules to obtain three continuous dimension scores representing basic knowledge, TCM nursing techniques, and advanced clinical practice. To assess CMB, Harman’s single-factor test was conducted; exploratory factor analysis of all items extracted six factors, and the first unrotated factor explained 40.284% of the variance (< 50%). Internal consistency reliability was assessed using Cronbach’s α in IBM SPSS Statistics (version 26.0). Continuous variables are presented as mean ± standard deviation (SD), and categorical variables as n (%). In Mplus (version 8.11), LPA was conducted by fitting 1- to 5-class models using the three dimension scores as indicators. Models were estimated using maximum likelihood (ML), with full information maximum likelihood (FIML) used to handle missing data. Multiple random starts were specified to reduce local maxima and obtain a stable best log-likelihood solution. Model selection considered Akaike information criterion (AIC), Bayesian information criterion (BIC), sample-size adjusted BIC (aBIC), entropy, the Lo–Mendell–Rubin adjusted likelihood ratio test (LMRT), and the bootstrap likelihood ratio test (BLRT), together with class proportions and substantive interpretability. Participants were assigned to profiles based on maximum posterior probability, and average posterior probabilities were examined. Between-profile differences were tested using χ² tests (two-sided, α = 0.05). Multinomial logistic regression was used to examine factors associated with profile membership, with the Developing profile serving as the reference group; results are reported as B, standard error (SE), Wald statistic, p value, odds ratio (OR), and 95% confidence interval (CI). Predictors were coded as years of experience (1–5 for 0–5, 6–10, 11–15, 16–20, and ≥ 21 years), professional title (1–3 for junior, intermediate, and associate senior or above), educational attainment (1–3 for technical secondary school, junior college, and bachelor’s degree or above), intention to participate in training (1–5 from very unwilling to very willing), and perceived importance of training (1–5 from not important at all to very important). Results Participant Characteristics A total of 457 nurses were included (Table 1 ). Participants were predominantly female (95.6%), with males accounting for 4.4%. Age was mainly 20–30 years (42.5%) and 31–40 years (36.8%), followed by 41–50 years (17.5%) and ≥ 51 years (3.3%). Years of work experience were 0–5 (26.0%), 6–10 (27.1%), 11–15 (25.6%), 16–20 (5.9%), and ≥ 21 years (15.3%). Professional title was junior (51.6%), intermediate (34.6%), and associate senior or above (13.8%); 74.2% held non-established positions. Educational attainment was bachelor’s degree or above (69.8%), junior college (27.4%), and secondary technical school (2.8%). For training attitudes, 59.7% reported being willing and 26.5% very willing to participate in TCM nursing training. Training was rated as important by 44.0% and very important by 39.6%. Perceived adequacy of prior TCM nursing training was reported as sufficient (54.0%), insufficient (22.8%), very insufficient (5.3%), or unclear (17.9%). Table 1 Participant Characteristics Variable Category Frequency (n) Percentage (%) Sex Male 20 4.4 Female 437 95.6 Age (years) 20–30 194 42.5 31–40 168 36.8 41–50 80 17.5 ≥ 51 15 3.3 Years of work experience (years) 0–5 119 26 6–10 124 27.1 11–15 117 25.6 16–20 27 5.9 ≥ 21 70 15.3 Professional title Junior 236 51.6 Intermediate 158 34.6 Associate senior and above 63 13.8 Employment type Established position 118 25.8 Non-established position 339 74.2 Education level Technical secondary school 13 2.8 Junior college 125 27.4 Bachelor’s degree or above 319 69.8 Willingness to receive training Very unwilling 20 4.4 Unwilling 12 2.6 Neutral 31 6.8 Willing 273 59.7 Very willing 121 26.5 Importance attached to training Very unimportant 5 1.1 Unimportant 8 1.8 Moderate 62 13.6 Important 201 44 Very important 181 39.6 Perceived sufficiency of received TCM nursing training Very insufficient 24 5.3 Insufficient 104 22.8 Unclear 82 17.9 Just sufficient 247 54 Reliability and Common Method Bias Assessment Internal consistency was assessed using Cronbach’s α in SPSS 26.0. The TCM nursing competency scale demonstrated high reliability across the three dimensions: Basic knowledge of TCM nursing (12 items; α = 0.944), TCM nursing techniques (18 items; α = 0.913), and Advanced TCM nursing clinical practice (7 items; α = 0.912). All α values exceeded 0.90, indicating satisfactory internal consistency. Harman single-factor test allowed evaluating common method bias (CMB) through performing an exploratory factor analysis on each and every item. Six factors were obtained and the original unrotated extract factored 40.284 percent of the variance which is less than 50 percent which was the criterion of the absence of any significant common method bias. Descriptive Statistics of Competence Dimensions Table 2 presents generally moderate scores across the three TCM nursing competency dimensions. The per-item mean and total score were 3.06 ± 0.86 and 36.67 ± 10.35 for Basic knowledge of TCM nursing; 3.21 ± 0.85 and 51.43 ± 13.56 for TCM nursing techniques; and 3.02 ± 0.91 and 21.17 ± 6.37 for Advanced TCM nursing clinical practice. Overall, TCM nursing techniques scored highest, followed by Basic knowledge of TCM nursing, whereas Advanced TCM nursing clinical practice scored lowest. Table 2 Descriptive Statistics of TCM Nursing Competency Dimensions Variable Mean ± SD (per item) Mean ± SD (total score) Basic knowledge of TCM nursing 3.06 ± 0.86 36.67 ± 10.35 TCM nursing techniques 3.21 ± 0.85 51.43 ± 13.56 Advanced TCM nursing clinical practice 3.02 ± 0.91 21.17 ± 6.37 Note: Values are mean ± SD. “Per-item” indicates the mean score per item (range: 1–5); “total score” indicates the summed dimension score. Latent Profile Analysis of Competence Maturity Model selection Latent profile analysis (LPA) was conducted using three continuous indicators of TCM nursing competency (Basic knowledge of TCM nursing, TCM nursing techniques, and Advanced TCM nursing clinical practice), fitting one- to five-profile models. Model selection was based on Akaike information criterion (AIC), Bayesian information criterion (BIC), sample-size adjusted BIC (aBIC), entropy, the Lo–Mendell–Rubin adjusted likelihood ratio test (LMRT), and the bootstrap likelihood ratio test (BLRT), together with profile proportions and interpretability. From the one- to five-class solutions, AIC, BIC, and aBIC generally decreased as the number of classes increased. The two-class model improved fit over the one-class model (AIC = 3222.344, BIC = 3263.591, aBIC = 3231.854) with entropy = 0.709 and significant LMRT (P = 0.019) and BLRT (P < 0.001). The three-class model further improved fit (AIC = 2982.889, BIC = 3040.635, aBIC = 2996.203), increased entropy to 0.846, and remained significant on LMRT (P = 0.003) and BLRT (P < 0.001). Class proportions were 64.3%, 12.3%, and 23.4%, supporting interpretability. Although the four-class model showed lower information criteria (AIC = 2934.380, BIC = 3008.624, aBIC = 2951.498), LMRT was not significant (P = 0.253) and a small class (5.7%) emerged. The five-class model continued to improve information criteria and entropy (0.897) but still produced a small class (7.2%) and may reflect overextraction with reduced interpretability. Therefore, the three-class model was selected as the optimal solution (Table 3 ). Table 3 Model Fit Indices for Latent Profile Analysis (LPA) Profile AIC BIC aBIC Entropy LMRT BLRT Proportion 1Class 3527.164 3551.912 3532.87 1 2Class 3222.344 3263.591 3231.854 0.709 0.019 <0.001 0.654/0.346 3Class 2982.889 3040.635 2996.203 0.846 0.003 <0.001 0.643/0.123/0.234 4Class 2934.38 3008.624 2951.498 0.821 0.253 <0.001 0.109/0.276/0.558/0.057 5Class 2576.542 2667.285 2597.464 0.897 0.032 <0.001 0.072/0.354/0.212/0.077/0.284 Note: AIC = Akaike information criterion; BIC = Bayesian information criterion; aBIC = sample-size adjusted BIC; LMRT = Lo–Mendell–Rubin adjusted likelihood ratio test; BLRT = bootstrap likelihood ratio test. Please format p values consistently (e.g., use “<0.001” rather than “<0.001”). Interpretation and naming of latent profiles Using the scores of the three dimensions of Traditional Chinese Medicine (TCM) nursing competence as continuous indicators, latent profile analysis (LPA) was performed to model heterogeneity in the sample. One- through five-class solutions were fitted sequentially to determine the optimal number of latent profiles. Model comparison was based on the Akaike information criterion (AIC), Bayesian information criterion (BIC), sample-size adjusted BIC (aBIC), entropy, and results from the Lo–Mendell–Rubin adjusted likelihood ratio test (LMRT) and bootstrap likelihood ratio test (BLRT), together with class proportions and substantive interpretability. As the number of classes increased from 1 to 5, AIC, BIC, and aBIC decreased overall. Relative to the 1-class solution, the 2-class solution showed improved fit (AIC = 3222.344, BIC = 3263.591, aBIC = 3231.854), with entropy = 0.709, LMRT p = 0.019, and BLRT p < 0.001. The 3-class solution yielded further decreases in the information criteria (AIC = 2982.889, BIC = 3040.635, aBIC = 2996.203), with entropy = 0.846, LMRT p = 0.003, and BLRT p < 0.001; class proportions were 64.3%, 12.3%, and 23.4%, respectively. Although the 4-class solution continued to decrease the information criteria (AIC = 2934.380, BIC = 3008.624, aBIC = 2951.498), the LMRT was not significant (p = 0.253) and a small class (5.7%) emerged. The 5-class solution produced higher entropy (0.897) but also yielded a small class (7.2%). Collectively, the information criteria, entropy, LMRT and BLRT results, and class proportions supported the 3-class solution as the final model. Model fit indices and information criteria for all solutions are shown in Table 3 and Fig. 1 . Table 4 Table 4 . Classification probabilities for the three-profile solution C1 C2 C3 C1 0.950 0.016 0.034 C2 0.122 0.878 0.000 C3 0.091 0.000 0.909 Between-group differences in study variables A chi-square test (χ² test) was used to compare the distributions of study variables across the three competency-maturity profiles—Developing, Transitional, and Mature (Table 5 ). Statistically significant differences in distribution were observed for years of work experience (χ² = 33.544, p < 0.001), professional title (χ² = 74.069, p < 0.001), educational attainment (χ² = 50.898, p < 0.001), willingness to participate in training (χ² = 109.836, p < 0.001), and perceived importance of training (χ² = 83.340, p < 0.001). Specifically, the proportion of participants classified as the Mature profile increased with greater work experience and higher professional title. Higher educational attainment was more common in the Transitional and Mature profiles. In addition, the Mature profile had higher proportions selecting “willing/very willing” and “important/very important” for willingness to participate in training and perceived importance of training, respectively. No statistically significant between-profile differences were found for sex (χ² = 1.372, p = 0.504), age (χ² = 8.908, p = 0.179), employment type (χ² = 4.892, p = 0.087), or the number of TCM nursing training sessions received (χ² = 4.058, p = 0.669) (all p > 0.05). Table 5 Differences in demographic and training-related variables across competency-development types. Variable Category Initial competency development (n, %) Transitional development (n, %) Mature competency (n, %) χ² P value Sex Male 1 (1.8) 15 (5.1) 4 (3.7) 1.372 0.504 Female 55 (98.2) 279 (94.9) 103 (96.3) Age (years) 20–30 17 (30.4) 129 (43.9) 48 (44.9) 8.908 0.179 31–40 26 (46.4) 102 (34.7) 40 (37.4) 41–50 12 (21.4) 55 (18.7) 13 (12.1) ≥ 51 1 (1.8) 8 (2.7) 6 (5.6) Years of work experience (years) 0–5 17 (30.4) 88 (29.9) 14 (13.1) 33.544 < 0.001 6–10 17 (30.4) 84 (28.6) 23 (21.5) 11–15 18 (32.1) 69 (23.5) 30 (28.0) 16–20 0 (0.0) 14 (4.8) 13 (12.1) ≥ 21 4 (7.1) 39 (13.3) 27 (25.2) Professional title Junior 34 (60.7) 172 (58.5) 30 (28.0) 74.069 < 0.001 Intermediate 22 (39.3) 99 (33.7) 37 (34.6) Associate senior and above 0 (0.0) 23 (7.8) 40 (37.4) Employment type Established position 17 (30.4) 82 (27.9) 19 (17.8) 4.892 0.087 Non-established position 39 (69.6) 212 (72.1) 88 (82.2) Education level Technical secondary school 6 (10.7) 4 (1.4) 3 (2.8) 50.898 < 0.001 Junior college 32 (57.1) 75 (25.5) 18 (16.8) Bachelor’s degree or above 18 (32.1) 215 (73.1) 86 (80.4) Willingness to receive training Very unwilling 14 (25.0) 4 (1.4) 2 (1.9) 109.836 < 0.001 Unwilling 5 (8.9) 5 (1.7) 2 (1.9) Neutral 7 (12.5) 21 (7.1) 3 (2.8) Willing 23 (41.1) 200 (68.0) 50 (46.7) Very willing 7 (12.5) 64 (21.8) 50 (46.7) Importance attached to training Very unimportant 1 (1.8) 3 (1.0) 1 (0.9) 83.34 < 0.001 Unimportant 6 (10.7) 2 (0.7) 0 (0.0) Moderate 21 (37.5) 37 (12.6) 4 (3.7) Important 17 (30.4) 145 (49.3) 39 (36.4) Very important 11 (19.6) 107 (36.4) 63 (58.9) Perceived sufficiency of received TCM nursing training Very insufficient 3 (5.4) 13 (4.4) 8 (7.5) 4.058 0.669 Insufficient 15 (26.8) 66 (22.4) 23 (21.5) Unclear 13 (23.2) 52 (17.7) 17 (15.9) Just sufficient 25 (44.6) 163 (55.4) 59 (55.1) Factors associated with latent profile membership With the Developing profile as the reference category, a multinomial logistic regression model was fitted including years of work experience, professional title, educational attainment, willingness to participate in training, and perceived importance of training (Table 6 ). The regression coefficients indicated different association patterns for membership in the Transitional and Mature profiles. Educational attainment, willingness to participate in training, and perceived importance of training were statistically significant in both contrasts. For the Transitional profile versus the Developing profile, educational attainment was positively associated with Transitional membership (B = 1.943, odds ratio [OR] = 6.981, 95% confidence interval [CI] 3.708–13.144, p < 0.001). Willingness to participate in training (B = 0.964, OR = 2.623, 95% CI 1.878–3.663, p < 0.001) and perceived importance of training (B = 0.866, OR = 2.377, 95% CI 1.546–3.654, p < 0.001) were also positively associated, whereas years of work experience and professional title did not reach statistical significance. For the mature-competency profile versus the primary competency-development profile, professional title (B = 1.154, OR = 3.170, 95% CI 1.488–6.753, p = 0.003), educational attainment (B = 2.085, OR = 8.047, 95% CI 3.720–17.408, p < 0.001), willingness to participate in training (B = 1.221, OR = 3.390, 95% CI 2.052–5.601, p < 0.001), and perceived importance of training (B = 1.422, OR = 4.144, 95% CI 2.349–7.313, p 0.05). Overall, educational attainment, willingness to participate in training, and perceived importance of training were significant in both contrasts, with larger ORs in the mature-competency contrast; professional title was significant only in the mature-competency contrast. Table 6 Multinomial logistic regression predicting latent profile membership. Category Predictor B SE Wald P value OR 95% CI Transitional development type Intercept -10.543 1.622 42.267 < 0.001 — — Years of work experience 0.149 0.153 0.945 0.331 1.16 0.860–1.566 Professional title 0.148 0.351 0.178 0.673 1.16 0.582–2.309 Education level 1.943 0.323 36.24 < 0.001 6.981 3.708–13.144 Willingness to receive training 0.964 0.17 32.028 < 0.001 2.623 1.878–3.663 Importance attached to training 0.866 0.219 15.561 < 0.001 2.377 1.546–3.654 Mature competency type Intercept -17.51 2.007 76.079 < 0.001 — — Years of work experience 0.247 0.179 1.902 0.168 1.28 0.901–1.818 Professional title 1.154 0.386 8.943 0.003 3.17 1.488–6.753 Education level 2.085 0.394 28.058 < 0.001 8.047 3.720–17.408 Willingness to receive training 1.221 0.256 22.703 < 0.001 3.39 2.052–5.601 Importance attached to training 1.422 0.29 24.076 < 0.001 4.144 2.349–7.313 Discussion Main findings Based on 457 nurses from a Grade A tertiary hospital, latent profile analysis (LPA) identified three profiles of Traditional Chinese Medicine (TCM) nursing competency maturity: developing (12.3%), transitional (64.3%), and mature (23.4%). Classification quality was supported by an entropy of 0.846 and average posterior probabilities > 0.85, indicating a robust three-profile solution. Overall, these findings suggest that TCM nursing competency development may not progress linearly and that a large proportion of nurses may plateau at an intermediate stage. The high proportion of the transitional-development profile is similar to other past latent profile research on the nursing field where an intermediate category often forms the largest category [27–29]. Such distribution can be a competency level product and bottleneck in the progression between the command of basic knowledge/technical skills and their application into the clinical practice in a complex environment. Technical competence and foundational knowledge scores were also higher with descriptive analyses, and the scores in clinical practice competence were lower, indicating that practice integration should be viewed as an essential gap in competency development. The big transitional category also indicates that although nurses might have already basic theoretical and procedural basis, stable competencies in clinical reasoning, integration of syndrome differentiation-informed nursing trajectories, and context-sensitive decision-making in complicated situations are not established yet on a regular basis [ 27 ] . Therefore, the transitional-development profile can become an important level in establishing a TCM nursing talent path: its scale can have an impact on the level of service provision, and the characteristics of the ways nurses are able to move up to the mature-competency profile can affect access to advanced TCM nursing capacity. Associations of educational attainment and years of work experience with competence maturity The primary competency-development profile was used as a reference in doing multinomial logistic regression and to determine the factors based on the maturity profiles. In relation to the reference profile, the annual levels of educational attainment, eagerness to train, and perceived significance of training were positively correlated with membership in both transitional-development and mature-competency profiles, but again the association with educational attainment is the strongest. Only the mature-competency profile showed a statistically significant difference in professional title whilst the years of work experience did not have any significant relationship with membership of profile in any of the contrasts. All these findings, together, indicate that inputs on competence maturation can rely more on education and learning-related input than tenure does. More education can increase knowledge assimilation, facts and reasoning on matters of abstract theories, and promote progression to greater degrees of maturity [ 27 , 30 ] . The correlations in relation to professional title could build up the effect of more role and task complexity; the nurses who receive higher titles have more possibilities to acquire the responsibilities of preceptorship (training junior nurses), quality assurance and improvement activities, technical procedures in the specialty, and complex management cases obtain more intensive and more qualified practice opportunities, and it makes them competent in maturation [ 31 – 33 ] . This absence of association with the number of years of work experience is also in agreement with systematic reviews that competence development is not progressive with tenure, but rather is contingent on the structured learning and quality practice [ 27 , 34 ] . In that regard, years of on-the-job experience might not be sufficient to measure quality of development, and competence might change or level off between career phases. Interpreting Training Motivation Through Self-Determination Theory (SDT) The high levels of predictive validity of intention to participate in training and perceived relevance of training in competency maturity imply that the correlations of maturity lie not only in the training provision, but better in the ability of nurses to attribute learning value and the quality of motivation. The fact that the three profiles have no substantial differences in the number of individuals who participate further suggests that in comparable organizational settings, the volume of training is not enough to clarify the difference in maturity. Under the SDT approach, intention and perceived importance may be regarded as predictors of motivational internalization. When participation contributes to the fulfillment of the basic psychological needs of nurses to attain autonomy, competence and the associated relatedness, then nurses would tend to develop autonomous motivation, continue to learn, and translate training into practice in clinical environments; when participation is influenced by mandates, then participation may likely be based on the achievement of requirements leading to a limited growth in competency [ 32 , 35 – 36 ] . The investigation of continuing professional development (CPD) also indicates that motivation, organizational support, working load, and the relevance and accessibility of courses are all the factors that collectively determine continuing education results [ 33 – 35 , 37 ] . In this regard, interventions need to focus on the transfer of training to establish a close-loop of implementation, which incorporates goal-oriented feedback, deliberate practice, and reflective practices [ 32 ] . Developing Tiered Training Pathways, Competency-Oriented Assessment, and an Evidence-Based TCM Nursing System Classification of profiles based on profile allows differentiated interventions to be practiced by the nursing management and education with a focus to plan competency-development pathways that can be transferred across. In the case of the novice profile, training must focus on standardized background knowledge and core technical skills, and with scenario-centered education and a preceptorship model to reinforce the practice of protocol adherence. The transitional one that includes the highest number of nurses in the shift should redirect the training to incorporate technical abilities in complicated scenarios, decision-making in pattern differentiation-guided nursing (bianzheng shihu) and risk management. Weak areas of high frequencies can be tackled by means of an intentional practice, competency checklists, and goal-focused feedback to address the performance levels [ 27 , 31 – 32 ] . In the case of the mature profile, it should be focused on specialty and development of role-based competence through systematic development of skills in specialty care in the outpatient environment, clinical education, nursing management, translation of evidence, and quality enhancement, which are the role models and facilitators [ 33 , 38 ] . This assessment system ought to combine scale-based assessments with objective evidence that involves psychometric testing of instruments to achieve a sense of validity [ 30 , 38 ] . It must also include structured clinical competency tests (e.g. objective structured clinical examinations [OSCEs]) and process and outcome quality measures (e.g. clinical questions, evidence summary and implementation plans) to eliminate the common-method bias and minimise the chance of a mismatch between motivation and competence. On the policy level, in compliance with nationalized needs to enhance TCM nursing service capacity and development of its workforce [ 39 ] , the creation and execution of the evidence-based consensus statement or clinical guidelines can promote the standardization of training and standardized clinical practice [ 38 , 40 ] . Unfortunately, alongside it, the promotion of the creation of TCM nursing outpatient clinics and specialty posts may be the role platforms of the mature profile and extend the spillover effects [ 33 , 41 ] . Future studies can use longitudinal or Interventional research designs to the ability of learning motivation and learning behaviors to shape competency maturity as well as to test how these two factors interact with training exposure. Conclusion This study was a systematic methodology study to assess the Traditional Chinese Medicine (TCM) nursing competency maturity of clinical nurses in Class A tertiary hospitals (the highest tier of tertiary hospitals in China) using a questionnaire survey that has been executed through the use of a cross-sectional survey designs and has shown significant differences in terms of nursing knowledge, technical skills, and clinical practice competence. In general, there was moderate competency maturity. The competence of technical skills was relatively higher, and the competence in clinical practice was relatively less, and there was an indication of a developmental illusion in the aspect of transitioning through the execution of technical tasks to high-level clinical practice in complex scenarios. Latent profile analysis revealed three competency-maturity profiles that had low-high gradient distributions, the transitional development profile was the major group and this denotes that majority of the nurses are at a critical transition point of competency development. There were considerable differences in profiles with regard to years of service, professional title, education attainment, and attitudes towards training including that of willingness to take part in training and perceived importance of training contributed stably to profile differentiation. Specifically, multinomial logistic regression indicated that educational attainment and training related attitude factor were always predictive of membership of higher-maturity profiles and those involving professional title only significant correlates against the mature competency profile, but years of service was not significant in multivariate models. Such results suggest that accumulating tenure alone cannot account for competency promotion, and must be interpreted in relation to educational qualifications, job position and training motivation. On the basis of such findings, managers ought to incorporate profile-based, tiered development and more precisely allocate training resources: In the case of the novice profile, they should focus on the augmentation of foundational knowledge and the essential technical processes; in transitional profile, they should focus on the competency of clinical practice by training them on more scenarios and facilitating training transfer; and in the mature profile, they should focus on the further training and preceptorship and specialization. Training willingness and value endorsement training motivation factors should too be included in the design of training strategies to improve investment to learning as well as the effectiveness of training transfer. Strengths, Limitations, and Future Directions Strengths and limitations A key strength of this study is the use of latent profile analysis (LPA) to characterize unobserved heterogeneity in nurses’ Traditional Chinese Medicine (TCM) nursing competency maturity. LPA is more resource-allocation theoretical and tiers-based developmental on competency configurations than traditional mean based comparisons because it more directly identifies different developmental-stage specific competency configurations. Incorporating motivational variables into profile comparisons and multinomial logistic regression, including willingness to undergo training and perceived importance of training, we were able to test competency-motivation relationship quantitatively and in a way that is consistent with reality as perceived in decision-making as far as training management in the hospital is concerned. A standardized questionnaire with a strict data quality control procedure was utilized in the study, and the internal consistency was satisfactory, and the common method variance (CMV) was tested, and the CMV test did not indicate any serious problem. Managerial implications of the findings can also be well spelled out because profile-specific deficits as well as its description can be mapped into practical training pathways and career development options. There are a number of restrictions that should be considered. To start with, the cross-sectional research design restricts the use of causal inference and is incapable of establishing the causal pathway or dynamic feedback between the attitudes and competency maturity due to training. Second, the sample was represented by a single tertiary hospital, and the need to be able to generalize the findings to take the other regions and subsets with regard to the availability of TCM nursing services has to be verified. Third, training attitudes and competency were measured using a self-report, which could be biased by the response (e.g., social desirability bias). In spite of the fact that CMV was checked, residual bias cannot be completely dismissed. Fourth, due to the higher levels of total scores used in the LPA, item-level differences might have been lost, which could possibly undermine within-profile heterogeneity of competency structure. Lastly, the regression models mainly comprised of demographic and training-attitude variables and failed to exhaust other important determinants, including organizational support, job situation, and the real training exposure. Future directions Future studies must be done in inter-regional and inter-hospital multicenter studies. It is also necessary to involve institutions of different degrees of TCM nursing services delivery to justify the structure of the profile, its proportions distribution as well as stability and contribute to the generalizability. Longitudinal or cohort designs must be used to outline profile changes and development patterns, as well as to estimate how well educational education, professional job title, role exposure and training motivation advance competency. Regarding measurement, the addition of objective assessment data of competency rather than self-report and optimization of exposure variables in regards to participation in training, training quality, and practice opportunities are encouraged, which can enhance mechanistic interpretation and decrease single-method bias. As an intervention issue, profile-based tiered training interventions are to be planned and piloted, especially those based on the lack of advanced clinical practice competence. These programs are supposed to focus on scenario based training, feedback loops and training transfer; and measure post-training competency change, sustainability, and results of clinical application.Mechanistically, more systematic model development and testing around the pathway of “training value endorsement–learning investment–training transfer” may provide stronger evidence to inform hospital strategies for tiered development, career progression, and training resource allocation. Abbreviations AIC Akaike information criterion BIC Bayesian information criterion BLRT bootstrap likelihood ratio test CI confidence interval CMB common method bias CMV common method variance CPD continuing professional development CVI content validity index FIML full information maximum likelihood LMRT Lo–Mendell–Rubin adjusted likelihood ratio test LPA latent profile analysis ML maximum likelihood OR odds ratio SD standard deviation SDT self-determination theory SE standard error SPSS Statistical Package for the Social Sciences (IBM SPSS Statistics) TCM traditional Chinese medicine WHO World Health Organization Declarations Ethics approval and consent to participate The study protocol was approved by the Ethics Committee of Guangyuan Central Hospital (approval no. GYZXLL2024010). This cross-sectional study involved no intervention and was conducted in accordance with the Declaration of Helsinki and the Belmont Report principles. Participation was voluntary and the questionnaire was completed anonymously. Written and verbal informed consent was obtained from all participants. Consent for publication Not applicable. Availability of data and materials The datasets generated and/or analysed during the current study are available from the corresponding author on reasonable request. The data are not publicly available due to the need to protect participants’ privacy. Competing interests The authors declare that they have no competing interests. Funding This work was supported by the National Natural Science Foundation of China (Grant No. 52503166) and the Science and Technology Department of Sichuan Province (Grant No. 2024NSFSC1023). Authors’ contributions MJ conceived the study and led the research design. YPL and JY contributed to the study design and coordinated the implementation of the study. YS and XZ were responsible for data cleaning and manuscript formatting/layout. MJ, JY, and YL conducted the statistical analyses and interpreted the data. ZW provided overall supervision, critically revised the manuscript for important intellectual content, and gave final approval of the version to be published. MJ drafted the initial manuscript. All authors read and approved the final manuscript. Acknowledgements The authors thank the Nursing Department of Guangyuan Central Hospital for its support in coordinating questionnaire distribution and all nurses who participated in the survey. Authors’ information Not applicable. References Singh B, Kumar A. Ageing, multimorbidity, and quality of life: a mediation analysis using longitudinal ageing study in India. Front Public Health. 2025;13:1562479. 10.3389/fpubh.2025.1562479 . World Health Organization. Ageing and health [Internet]. 2025 [cited 7 Feb 2026]. Available from: https://www.who.int/news-room/fact-sheets/detail/ageing-and-health World Health Organization. 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Analysis","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe trend of population aging and an increasing burden of noncommunicable diseases (NCDs) and other chronic health conditions requires health systems to prioritize long-term management and continuous, integrated care rather than episodic treatment of acute illness \u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. According to projections and statistics provided by the World Health Organization (WHO), the percentage of the aged population is on the rise and the number of deaths caused by NCDs is high \u003csup\u003e[\u003cspan additionalcitationids=\"CR3\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e. One systematic review approximated that a considerable proportion (46%) of older individuals have multimorbidity (more than two chronic illnesses) \u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e, and multimorbidity requires more person-centered integrated care \u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e. Continuity of care is also highly dependent on nurses, who serve as a pillar of continuity, and their competence is directly associated with the quality of chronic disease management and the effectiveness of health promotion \u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. As such, defining and consolidating key nursing competencies in long-term care settings is a significant driver of chronic disease management and healthy aging.\u003c/p\u003e \u003cp\u003eIn the context of the Healthy China 2030 strategy, China has gone further to conserve and develop traditional Chinese medicine (TCM) \u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e. One of the national strategic plans in the development of TCM (2016\u0026ndash;2030) points to health promotion initiatives based on the concept of preventive treatment of disease \u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e. The National Plan of the Development of Nursing Services (2021\u0026ndash;2025) recommends competency-oriented training systems, and enhancement of standards and specifications regarding TCM nursing skills, procedures, and practice \u003csup\u003e[\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e. TCM nursing competency in geriatric, rehabilitation, and chronic disease care should encompass a solid theoretical foundation (including pattern differentiation), proficiency in TCM nursing techniques, and the ability to integrate these competencies into clinical practice. Nevertheless, there is heterogeneity in access to training, opportunities for clinical application, and transfer of training to practice among clinical nurses, implying substantial individual differences in competency levels \u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e. Therefore, evaluation based solely on mean levels may be insufficient for guiding training-resource allocation.\u003c/p\u003e \u003cp\u003eTCM nursing competency measurement models are beginning to emerge, such as an indicator system of core competencies among nurses using TCM specialty based on evidence synthesis \u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e, and the creation and testing of a competency scale of clinical care in TCM hospitals among nurses \u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e. However, most studies have relied on variable-centered approaches that emphasize average levels and may obscure latent subgroups \u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e. Benner\u0026rsquo;s novice-to-expert model highlights that competency development is staged and context-dependent \u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e, providing a theoretical rationale for stratification. Operationally, the present study defines TCM nursing competency maturity as a self-reported, stage-informed construct, intended to describe differences in developmental levels rather than to evaluate a formal, process-oriented maturity model. Person-centered latent profile analysis (LPA) can identify latent classes that differ in competency patterns within a population. Emerging evidence supports LPA for classifying nurses\u0026rsquo; competencies and training needs and indicates that training experiences and educational attainment are associated with class membership \u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e. However, in TCM nursing, empirical studies remain scarce\u0026mdash;particularly those integrating training-motivation\u0026ndash;related indicators with competency profiles. Accordingly, analytic strategies that foreground population heterogeneity are needed to identify TCM nursing competency profiles and explore their correlates.\u003c/p\u003e \u003cp\u003eCompetency development is also shaped by learning motivation. Self-determination theory posits that fulfilling needs for autonomy, competence, and relatedness fosters high-quality motivation and sustained engagement in learning \u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e. Studies of nurses\u0026rsquo; continuing professional development similarly suggest that intrinsic and extrinsic motivation, together with organizational support, jointly influence participation in continuing education \u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e. On this basis, we expected that nurses across competency profiles might differ in intention to participate in training and in perceived importance of training, and that these factors could be associated with profile membership. We conducted LPA using indicators of basic knowledge, TCM nursing techniques, and advanced clinical practice to identify latent profiles of TCM nursing competency maturity among clinical nurses. We then used multinomial logistic regression to examine associations of demographic characteristics, intention to participate in training, and perceived importance of training with profile membership, aiming to inform stratified training and training-resource allocation in health care institutions. By linking competency heterogeneity with motivation-related correlates, this study seeks to generate more actionable evidence to support stratified, precision-oriented decision-making for developing the TCM nursing workforce.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eEthics Approval and Informed Consent\u003c/h2\u003e \u003cp\u003eThis cross-sectional study involved no intervention. The study was conducted in accordance with the ethical principles of respect, justice, and beneficence outlined in the Belmont Report. Participation was voluntary; participants were informed of the study objectives and their right to withdraw at any time. To protect privacy, the questionnaire was completed anonymously, and data were used only for academic research. The study protocol was approved by the Ethics Committee of Guangyuan Central Hospital (approval no. GYZXLL2024010). Written and verbal informed consent was obtained from all participants. The study was conducted in accordance with the Declaration of Helsinki.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eStudy Design and Participants\u003c/h3\u003e\n\u003cp\u003eThis questionnaire-based cross-sectional quantitative study was conducted from January to October 2024 at a tertiary Grade A hospital in Guangyuan City, China. With coordination from the Nursing Department, questionnaires were distributed hospital-wide to on-duty clinical nurses who met the eligibility criteria, and participation was voluntary; therefore, a convenience sample was obtained. Participants were recruited from multiple departments providing Traditional Chinese Medicine (TCM) nursing services and represented different job grades. Data were collected once within the same survey window. In total, 457 questionnaires were deemed valid and included in the analysis.\u003c/p\u003e \u003cp\u003eInclusion criteria were: (1) on-duty clinical nurses employed at the hospital who were able to comprehend and complete the questionnaire; (2) provided informed consent and participated voluntarily; (3) able to complete the questionnaire independently; and (4) engaged in frontline TCM nursing work, defined as working in a department that provides TCM nursing services and delivers TCM nursing interventions.\u003c/p\u003e \u003cp\u003eExclusion criteria were: (1) non-nursing personnel; (2) individuals who were not on duty and unable to participate because of leave or off-site training/further education; (3) continuous sick leave or maternity leave for \u0026ge;\u0026thinsp;3 months; and (4) nurses undertaking a clinical placement/rotation in the department.\u003c/p\u003e \u003cp\u003eQuestionnaires were excluded as invalid if: (1) internal inconsistencies were identified upon screening; (2) the same response option was selected for all items; or (3) responses did not adhere to the questionnaire instructions.\u003c/p\u003e\n\u003ch3\u003eMeasurement Instruments\u003c/h3\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eGeneral Information Questionnaire\u003c/h2\u003e \u003cp\u003eA general information questionnaire was developed by the research team through a literature review and expert consultation to assess participants\u0026rsquo; demographic and work-related characteristics. Items included sex, age, years of nursing experience, professional title, employment type, highest educational attainment, graduating institution, and whether the participant\u0026rsquo;s department provided TCM nursing services and offered TCM nursing techniques/interventions.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eTraditional Chinese Medicine Nursing Competence Questionnaire\u003c/h3\u003e\n\u003cp\u003eTCM nursing competency was assessed using the Traditional Chinese Medicine Nursing Competency Questionnaire developed by Wang Dan et al. (2018) \u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e. The instrument comprises 37 items across three dimensions: basic knowledge of TCM nursing (12 items), TCM nursing techniques (18 items), and advanced TCM nursing clinical practice (7 items). Each item is rated on a 5-point Likert scale of mastery/familiarity (1\u0026thinsp;=\u0026thinsp;very unfamiliar, 2\u0026thinsp;=\u0026thinsp;unfamiliar, 3\u0026thinsp;=\u0026thinsp;moderate, 4\u0026thinsp;=\u0026thinsp;familiar, 5\u0026thinsp;=\u0026thinsp;very familiar). Higher scores indicate greater TCM nursing competency. Total scores range from 37 to 185; dimension mean scores (1\u0026ndash;5) may also be used for cross-dimension comparisons. The Cronbach\u0026rsquo;s α coefficients for the three dimensions were 0.91, 0.88, and 0.96, respectively, and the corresponding content validity index (CVI) values were 0.83, 0.91, and 0.80, respectively.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eTraining Needs Questionnaire for TCM Nursing Staff in Public Hospitals\u003c/h2\u003e \u003cp\u003eThe Training Needs Questionnaire of the TCM Nursing Staff in Public Hospital created by Ye Meixia et al. \u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e was used to measure training needs. The questionnaire includes six domains: intention to participate in training, perceived importance, training frequency, modalities of theoretical training, modalities of skills training, and training content. A pilot survey and psychometric testing were conducted; the cumulative variance explained was 62.1%, and the overall Cronbach\u0026rsquo;s α was 0.84.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eData Quality Control\u003c/h3\u003e\n\u003cp\u003eQuality control processes were applied both in the processes of administering questionnaires and data processing in order to improve accuracy, completeness, and analyzability of their data. A standard set of questions and scoring were tested before this questionnaire was distributed. It was self-completed and the respondents got standardized guidelines about the aim of the study and confidentiality in order to reduce social desirability and non response. Questionnaires were then filtered after found to contain a lot of missing data, bad internal consistency pattern, or profile of one-dimensional response across the whole set. Coding of data was done based on a standardized codebook and inputted into database with a form of two versus one or random check. It was subjected to logical range checks and consistency checks to ensure that the implausible values and possible outliers were checked with original records and corrected where necessary. Missing data were handled using available-case analysis where applicable. Common method bias (CMB) (Harman\u0026rsquo;s single-factor test) and internal consistency reliability (Cronbach\u0026rsquo;s α) were evaluated to support subsequent latent profile analysis (LPA) and regression modeling.\u003c/p\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eData Analysis\u003c/h2\u003e \u003cp\u003eValid questionnaires (n\u0026thinsp;=\u0026thinsp;457) were included in the analyses. Descriptive statistics and LPA were conducted. Item scores were summed according to scoring rules to obtain three continuous dimension scores representing basic knowledge, TCM nursing techniques, and advanced clinical practice. To assess CMB, Harman\u0026rsquo;s single-factor test was conducted; exploratory factor analysis of all items extracted six factors, and the first unrotated factor explained 40.284% of the variance (\u0026lt;\u0026thinsp;50%). Internal consistency reliability was assessed using Cronbach\u0026rsquo;s α in IBM SPSS Statistics (version 26.0). Continuous variables are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD), and categorical variables as n (%).\u003c/p\u003e \u003cp\u003eIn Mplus (version 8.11), LPA was conducted by fitting 1- to 5-class models using the three dimension scores as indicators. Models were estimated using maximum likelihood (ML), with full information maximum likelihood (FIML) used to handle missing data. Multiple random starts were specified to reduce local maxima and obtain a stable best log-likelihood solution. Model selection considered Akaike information criterion (AIC), Bayesian information criterion (BIC), sample-size adjusted BIC (aBIC), entropy, the Lo\u0026ndash;Mendell\u0026ndash;Rubin adjusted likelihood ratio test (LMRT), and the bootstrap likelihood ratio test (BLRT), together with class proportions and substantive interpretability. Participants were assigned to profiles based on maximum posterior probability, and average posterior probabilities were examined.\u003c/p\u003e \u003cp\u003eBetween-profile differences were tested using χ\u0026sup2; tests (two-sided, α\u0026thinsp;=\u0026thinsp;0.05). Multinomial logistic regression was used to examine factors associated with profile membership, with the Developing profile serving as the reference group; results are reported as B, standard error (SE), Wald statistic, p value, odds ratio (OR), and 95% confidence interval (CI). Predictors were coded as years of experience (1\u0026ndash;5 for 0\u0026ndash;5, 6\u0026ndash;10, 11\u0026ndash;15, 16\u0026ndash;20, and \u0026ge;\u0026thinsp;21 years), professional title (1\u0026ndash;3 for junior, intermediate, and associate senior or above), educational attainment (1\u0026ndash;3 for technical secondary school, junior college, and bachelor\u0026rsquo;s degree or above), intention to participate in training (1\u0026ndash;5 from very unwilling to very willing), and perceived importance of training (1\u0026ndash;5 from not important at all to very important).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eParticipant Characteristics\u003c/h2\u003e \u003cp\u003eA total of 457 nurses were included (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Participants were predominantly female (95.6%), with males accounting for 4.4%. Age was mainly 20\u0026ndash;30 years (42.5%) and 31\u0026ndash;40 years (36.8%), followed by 41\u0026ndash;50 years (17.5%) and \u0026ge;\u0026thinsp;51 years (3.3%). Years of work experience were 0\u0026ndash;5 (26.0%), 6\u0026ndash;10 (27.1%), 11\u0026ndash;15 (25.6%), 16\u0026ndash;20 (5.9%), and \u0026ge;\u0026thinsp;21 years (15.3%). Professional title was junior (51.6%), intermediate (34.6%), and associate senior or above (13.8%); 74.2% held non-established positions. Educational attainment was bachelor\u0026rsquo;s degree or above (69.8%), junior college (27.4%), and secondary technical school (2.8%).\u003c/p\u003e \u003cp\u003eFor training attitudes, 59.7% reported being willing and 26.5% very willing to participate in TCM nursing training. Training was rated as important by 44.0% and very important by 39.6%. Perceived adequacy of prior TCM nursing training was reported as sufficient (54.0%), insufficient (22.8%), very insufficient (5.3%), or unclear (17.9%).\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\u003eParticipant Characteristics\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=\"left\" 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\u003eCategory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFrequency (n)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePercentage (%)\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\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.4\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\u003e437\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e95.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20\u0026ndash;30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e194\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e42.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31\u0026ndash;40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e168\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e36.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e41\u0026ndash;50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eYears of work experience (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u0026ndash;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6\u0026ndash;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e124\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11\u0026ndash;15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e117\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16\u0026ndash;20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eProfessional title\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJunior\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e236\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e51.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIntermediate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e158\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e34.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAssociate senior and above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.8\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\u003eEstablished position\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e118\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon-established position\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e339\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e74.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eEducation level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTechnical secondary school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJunior college\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBachelor\u0026rsquo;s degree or above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e319\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e69.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eWillingness to receive training\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVery unwilling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnwilling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNeutral\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWilling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e273\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e59.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVery willing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eImportance attached to training\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVery unimportant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnimportant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eImportant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e201\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVery important\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e181\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e39.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003ePerceived sufficiency of received TCM nursing training\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVery insufficient\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInsufficient\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnclear\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJust sufficient\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e247\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e54\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eReliability and Common Method Bias Assessment\u003c/h2\u003e \u003cp\u003eInternal consistency was assessed using Cronbach\u0026rsquo;s α in SPSS 26.0. The TCM nursing competency scale demonstrated high reliability across the three dimensions: Basic knowledge of TCM nursing (12 items; α\u0026thinsp;=\u0026thinsp;0.944), TCM nursing techniques (18 items; α\u0026thinsp;=\u0026thinsp;0.913), and Advanced TCM nursing clinical practice (7 items; α\u0026thinsp;=\u0026thinsp;0.912). All α values exceeded 0.90, indicating satisfactory internal consistency.\u003c/p\u003e \u003cp\u003eHarman single-factor test allowed evaluating common method bias (CMB) through performing an exploratory factor analysis on each and every item. Six factors were obtained and the original unrotated extract factored 40.284 percent of the variance which is less than 50 percent which was the criterion of the absence of any significant common method bias.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eDescriptive Statistics of Competence Dimensions\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents generally moderate scores across the three TCM nursing competency dimensions. The per-item mean and total score were 3.06\u0026thinsp;\u0026plusmn;\u0026thinsp;0.86 and 36.67\u0026thinsp;\u0026plusmn;\u0026thinsp;10.35 for Basic knowledge of TCM nursing; 3.21\u0026thinsp;\u0026plusmn;\u0026thinsp;0.85 and 51.43\u0026thinsp;\u0026plusmn;\u0026thinsp;13.56 for TCM nursing techniques; and 3.02\u0026thinsp;\u0026plusmn;\u0026thinsp;0.91 and 21.17\u0026thinsp;\u0026plusmn;\u0026thinsp;6.37 for Advanced TCM nursing clinical practice. Overall, TCM nursing techniques scored highest, followed by Basic knowledge of TCM nursing, whereas Advanced TCM nursing clinical practice scored lowest.\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 TCM Nursing Competency Dimensions\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\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 \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\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD (per item)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD (total score)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBasic knowledge of TCM nursing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e3.06\u0026thinsp;\u0026plusmn;\u0026thinsp;0.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e36.67\u0026thinsp;\u0026plusmn;\u0026thinsp;10.35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTCM nursing techniques\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e3.21\u0026thinsp;\u0026plusmn;\u0026thinsp;0.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e51.43\u0026thinsp;\u0026plusmn;\u0026thinsp;13.56\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdvanced TCM nursing clinical practice\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e3.02\u0026thinsp;\u0026plusmn;\u0026thinsp;0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e21.17\u0026thinsp;\u0026plusmn;\u0026thinsp;6.37\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eNote: Values are mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD. \u0026ldquo;Per-item\u0026rdquo; indicates the mean score per item (range: 1\u0026ndash;5); \u0026ldquo;total score\u0026rdquo; indicates the summed dimension score.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eLatent Profile Analysis of Competence Maturity\u003c/h2\u003e \u003cdiv id=\"Sec16\" class=\"Section3\"\u003e \u003ch2\u003eModel selection\u003c/h2\u003e \u003cp\u003eLatent profile analysis (LPA) was conducted using three continuous indicators of TCM nursing competency (Basic knowledge of TCM nursing, TCM nursing techniques, and Advanced TCM nursing clinical practice), fitting one- to five-profile models. Model selection was based on Akaike information criterion (AIC), Bayesian information criterion (BIC), sample-size adjusted BIC (aBIC), entropy, the Lo\u0026ndash;Mendell\u0026ndash;Rubin adjusted likelihood ratio test (LMRT), and the bootstrap likelihood ratio test (BLRT), together with profile proportions and interpretability.\u003c/p\u003e \u003cp\u003eFrom the one- to five-class solutions, AIC, BIC, and aBIC generally decreased as the number of classes increased. The two-class model improved fit over the one-class model (AIC\u0026thinsp;=\u0026thinsp;3222.344, BIC\u0026thinsp;=\u0026thinsp;3263.591, aBIC\u0026thinsp;=\u0026thinsp;3231.854) with entropy\u0026thinsp;=\u0026thinsp;0.709 and significant LMRT (P\u0026thinsp;=\u0026thinsp;0.019) and BLRT (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The three-class model further improved fit (AIC\u0026thinsp;=\u0026thinsp;2982.889, BIC\u0026thinsp;=\u0026thinsp;3040.635, aBIC\u0026thinsp;=\u0026thinsp;2996.203), increased entropy to 0.846, and remained significant on LMRT (P\u0026thinsp;=\u0026thinsp;0.003) and BLRT (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Class proportions were 64.3%, 12.3%, and 23.4%, supporting interpretability. Although the four-class model showed lower information criteria (AIC\u0026thinsp;=\u0026thinsp;2934.380, BIC\u0026thinsp;=\u0026thinsp;3008.624, aBIC\u0026thinsp;=\u0026thinsp;2951.498), LMRT was not significant (P\u0026thinsp;=\u0026thinsp;0.253) and a small class (5.7%) emerged. The five-class model continued to improve information criteria and entropy (0.897) but still produced a small class (7.2%) and may reflect overextraction with reduced interpretability. Therefore, the three-class model was selected as the optimal solution (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\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\u003eModel Fit Indices for Latent Profile Analysis (LPA)\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=\"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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProfile\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\u003eLMRT\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eBLRT\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eProportion\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1Class\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3527.164\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3551.912\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3532.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2Class\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3222.344\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3263.591\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3231.854\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.709\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.654/0.346\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3Class\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2982.889\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3040.635\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2996.203\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.846\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.643/0.123/0.234\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4Class\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2934.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3008.624\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2951.498\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.821\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.253\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.109/0.276/0.558/0.057\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5Class\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2576.542\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2667.285\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2597.464\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.897\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.072/0.354/0.212/0.077/0.284\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; LMRT\u0026thinsp;=\u0026thinsp;Lo\u0026ndash;Mendell\u0026ndash;Rubin adjusted likelihood ratio test; BLRT\u0026thinsp;=\u0026thinsp;bootstrap likelihood ratio test. Please format p values consistently (e.g., use \u0026ldquo;\u0026lt;0.001\u0026rdquo; rather than \u0026ldquo;\u0026lt;0.001\u0026rdquo;).\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eInterpretation and naming of latent profiles\u003c/h2\u003e \u003cp\u003eUsing the scores of the three dimensions of Traditional Chinese Medicine (TCM) nursing competence as continuous indicators, latent profile analysis (LPA) was performed to model heterogeneity in the sample. One- through five-class solutions were fitted sequentially to determine the optimal number of latent profiles. Model comparison was based on the Akaike information criterion (AIC), Bayesian information criterion (BIC), sample-size adjusted BIC (aBIC), entropy, and results from the Lo\u0026ndash;Mendell\u0026ndash;Rubin adjusted likelihood ratio test (LMRT) and bootstrap likelihood ratio test (BLRT), together with class proportions and substantive interpretability. As the number of classes increased from 1 to 5, AIC, BIC, and aBIC decreased overall. Relative to the 1-class solution, the 2-class solution showed improved fit (AIC\u0026thinsp;=\u0026thinsp;3222.344, BIC\u0026thinsp;=\u0026thinsp;3263.591, aBIC\u0026thinsp;=\u0026thinsp;3231.854), with entropy\u0026thinsp;=\u0026thinsp;0.709, LMRT p\u0026thinsp;=\u0026thinsp;0.019, and BLRT p\u0026thinsp;\u0026lt;\u0026thinsp;0.001. The 3-class solution yielded further decreases in the information criteria (AIC\u0026thinsp;=\u0026thinsp;2982.889, BIC\u0026thinsp;=\u0026thinsp;3040.635, aBIC\u0026thinsp;=\u0026thinsp;2996.203), with entropy\u0026thinsp;=\u0026thinsp;0.846, LMRT p\u0026thinsp;=\u0026thinsp;0.003, and BLRT p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; class proportions were 64.3%, 12.3%, and 23.4%, respectively. Although the 4-class solution continued to decrease the information criteria (AIC\u0026thinsp;=\u0026thinsp;2934.380, BIC\u0026thinsp;=\u0026thinsp;3008.624, aBIC\u0026thinsp;=\u0026thinsp;2951.498), the LMRT was not significant (p\u0026thinsp;=\u0026thinsp;0.253) and a small class (5.7%) emerged. The 5-class solution produced higher entropy (0.897) but also yielded a small class (7.2%). Collectively, the information criteria, entropy, LMRT and BLRT results, and class proportions supported the 3-class solution as the final model. Model fit indices and information criteria for all solutions are shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\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\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. Classification probabilities for the three-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\"\u003e\u0026nbsp;\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 \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\u003e0.950\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.034\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\u003e0.122\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.878\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\u003eC3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.091\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.909\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eBetween-group differences in study variables\u003c/h2\u003e \u003cp\u003eA chi-square test (χ\u0026sup2; test) was used to compare the distributions of study variables across the three competency-maturity profiles\u0026mdash;Developing, Transitional, and Mature (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Statistically significant differences in distribution were observed for years of work experience (χ\u0026sup2; = 33.544, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), professional title (χ\u0026sup2; = 74.069, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), educational attainment (χ\u0026sup2; = 50.898, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), willingness to participate in training (χ\u0026sup2; = 109.836, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and perceived importance of training (χ\u0026sup2; = 83.340, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Specifically, the proportion of participants classified as the Mature profile increased with greater work experience and higher professional title. Higher educational attainment was more common in the Transitional and Mature profiles. In addition, the Mature profile had higher proportions selecting \u0026ldquo;willing/very willing\u0026rdquo; and \u0026ldquo;important/very important\u0026rdquo; for willingness to participate in training and perceived importance of training, respectively.\u003c/p\u003e \u003cp\u003eNo statistically significant between-profile differences were found for sex (χ\u0026sup2; = 1.372, p\u0026thinsp;=\u0026thinsp;0.504), age (χ\u0026sup2; = 8.908, p\u0026thinsp;=\u0026thinsp;0.179), employment type (χ\u0026sup2; = 4.892, p\u0026thinsp;=\u0026thinsp;0.087), or the number of TCM nursing training sessions received (χ\u0026sup2; = 4.058, p\u0026thinsp;=\u0026thinsp;0.669) (all p\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\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\u003eDifferences in demographic and training-related variables across competency-development types.\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\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eInitial competency development\u003c/p\u003e \u003cp\u003e(n, %)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTransitional development\u003c/p\u003e \u003cp\u003e(n, %)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMature competency\u003c/p\u003e \u003cp\u003e(n, %)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eχ\u0026sup2;\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP 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\u003e1 (1.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15 (5.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4 (3.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e1.372\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.504\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\u003e55 (98.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e279 (94.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e103 (96.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20\u0026ndash;30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17 (30.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e129 (43.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e48 (44.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e8.908\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e0.179\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31\u0026ndash;40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e26 (46.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e102 (34.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e40 (37.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e41\u0026ndash;50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12 (21.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e55 (18.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e13 (12.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1 (1.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8 (2.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6 (5.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eYears of work experience (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u0026ndash;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17 (30.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e88 (29.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e14 (13.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e33.544\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6\u0026ndash;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17 (30.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e84 (28.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e23 (21.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11\u0026ndash;15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18 (32.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e69 (23.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e30 (28.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16\u0026ndash;20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0 (0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14 (4.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e13 (12.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4 (7.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e39 (13.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e27 (25.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eProfessional title\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJunior\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e34 (60.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e172 (58.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e30 (28.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e74.069\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIntermediate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e22 (39.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e99 (33.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e37 (34.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAssociate senior and above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0 (0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e23 (7.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e40 (37.4)\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\u003eEstablished position\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17 (30.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e82 (27.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e19 (17.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e4.892\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.087\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon-established position\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e39 (69.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e212 (72.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e88 (82.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eEducation level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTechnical secondary school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6 (10.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4 (1.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3 (2.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e50.898\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJunior college\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e32 (57.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e75 (25.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e18 (16.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBachelor\u0026rsquo;s degree or above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18 (32.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e215 (73.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e86 (80.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eWillingness to receive training\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVery unwilling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14 (25.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4 (1.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2 (1.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e109.836\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnwilling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5 (8.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5 (1.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2 (1.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNeutral\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7 (12.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e21 (7.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3 (2.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWilling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e23 (41.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e200 (68.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e50 (46.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVery willing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7 (12.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e64 (21.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e50 (46.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eImportance attached to training\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVery unimportant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1 (1.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3 (1.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1 (0.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e83.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnimportant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6 (10.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2 (0.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0 (0.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21 (37.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e37 (12.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4 (3.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eImportant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17 (30.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e145 (49.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e39 (36.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVery important\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11 (19.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e107 (36.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e63 (58.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003ePerceived sufficiency of received TCM nursing training\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVery insufficient\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3 (5.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13 (4.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8 (7.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e4.058\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e0.669\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInsufficient\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15 (26.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e66 (22.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e23 (21.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnclear\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13 (23.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e52 (17.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e17 (15.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJust sufficient\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25 (44.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e163 (55.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e59 (55.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eFactors associated with latent profile membership\u003c/h2\u003e \u003cp\u003eWith the Developing profile as the reference category, a multinomial logistic regression model was fitted including years of work experience, professional title, educational attainment, willingness to participate in training, and perceived importance of training (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). The regression coefficients indicated different association patterns for membership in the Transitional and Mature profiles. Educational attainment, willingness to participate in training, and perceived importance of training were statistically significant in both contrasts.\u003c/p\u003e \u003cp\u003eFor the Transitional profile versus the Developing profile, educational attainment was positively associated with Transitional membership (B\u0026thinsp;=\u0026thinsp;1.943, odds ratio [OR]\u0026thinsp;=\u0026thinsp;6.981, 95% confidence interval [CI] 3.708\u0026ndash;13.144, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Willingness to participate in training (B\u0026thinsp;=\u0026thinsp;0.964, OR\u0026thinsp;=\u0026thinsp;2.623, 95% CI 1.878\u0026ndash;3.663, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and perceived importance of training (B\u0026thinsp;=\u0026thinsp;0.866, OR\u0026thinsp;=\u0026thinsp;2.377, 95% CI 1.546\u0026ndash;3.654, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were also positively associated, whereas years of work experience and professional title did not reach statistical significance.\u003c/p\u003e \u003cp\u003eFor the mature-competency profile versus the primary competency-development profile, professional title (B\u0026thinsp;=\u0026thinsp;1.154, OR\u0026thinsp;=\u0026thinsp;3.170, 95% CI 1.488\u0026ndash;6.753, p\u0026thinsp;=\u0026thinsp;0.003), educational attainment (B\u0026thinsp;=\u0026thinsp;2.085, OR\u0026thinsp;=\u0026thinsp;8.047, 95% CI 3.720\u0026ndash;17.408, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), willingness to participate in training (B\u0026thinsp;=\u0026thinsp;1.221, OR\u0026thinsp;=\u0026thinsp;3.390, 95% CI 2.052\u0026ndash;5.601, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and perceived importance of training (B\u0026thinsp;=\u0026thinsp;1.422, OR\u0026thinsp;=\u0026thinsp;4.144, 95% CI 2.349\u0026ndash;7.313, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were positively associated with mature-competency membership, whereas years of work experience did not reach statistical significance (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Overall, educational attainment, willingness to participate in training, and perceived importance of training were significant in both contrasts, with larger ORs in the mature-competency contrast; professional title was significant only in the mature-competency contrast.\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\u003eMultinomial logistic regression predicting latent profile membership.\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=\"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=\"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\u003eCategory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePredictor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eWald\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003eTransitional development type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIntercept\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-10.543\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.622\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e42.267\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 \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYears of work experience\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.149\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.153\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.945\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.331\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.860\u0026ndash;1.566\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProfessional title\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.148\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.351\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.178\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.673\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.582\u0026ndash;2.309\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEducation level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.943\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.323\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e36.24\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 \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.981\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.708\u0026ndash;13.144\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWillingness to receive training\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.964\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e32.028\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 \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.623\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.878\u0026ndash;3.663\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eImportance attached to training\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.866\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.219\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e15.561\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 \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.377\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.546\u0026ndash;3.654\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003eMature competency type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIntercept\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-17.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e76.079\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 \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYears of work experience\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.247\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.179\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.902\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.168\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.901\u0026ndash;1.818\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProfessional title\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.154\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.386\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8.943\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.488\u0026ndash;6.753\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEducation level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.085\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.394\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e28.058\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 \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8.047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.720\u0026ndash;17.408\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWillingness to receive training\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.221\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.256\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e22.703\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 \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.052\u0026ndash;5.601\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eImportance attached to training\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.422\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e24.076\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 \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.349\u0026ndash;7.313\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eMain findings\u003c/h2\u003e \u003cp\u003eBased on 457 nurses from a Grade A tertiary hospital, latent profile analysis (LPA) identified three profiles of Traditional Chinese Medicine (TCM) nursing competency maturity: developing (12.3%), transitional (64.3%), and mature (23.4%). Classification quality was supported by an entropy of 0.846 and average posterior probabilities\u0026thinsp;\u0026gt;\u0026thinsp;0.85, indicating a robust three-profile solution. Overall, these findings suggest that TCM nursing competency development may not progress linearly and that a large proportion of nurses may plateau at an intermediate stage.\u003c/p\u003e \u003cp\u003eThe high proportion of the transitional-development profile is similar to other past latent profile research on the nursing field where an intermediate category often forms the largest category [27\u0026ndash;29]. Such distribution can be a competency level product and bottleneck in the progression between the command of basic knowledge/technical skills and their application into the clinical practice in a complex environment. Technical competence and foundational knowledge scores were also higher with descriptive analyses, and the scores in clinical practice competence were lower, indicating that practice integration should be viewed as an essential gap in competency development. The big transitional category also indicates that although nurses might have already basic theoretical and procedural basis, stable competencies in clinical reasoning, integration of syndrome differentiation-informed nursing trajectories, and context-sensitive decision-making in complicated situations are not established yet on a regular basis \u003csup\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e. Therefore, the transitional-development profile can become an important level in establishing a TCM nursing talent path: its scale can have an impact on the level of service provision, and the characteristics of the ways nurses are able to move up to the mature-competency profile can affect access to advanced TCM nursing capacity.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eAssociations of educational attainment and years of work experience with competence maturity\u003c/h2\u003e \u003cp\u003eThe primary competency-development profile was used as a reference in doing multinomial logistic regression and to determine the factors based on the maturity profiles. In relation to the reference profile, the annual levels of educational attainment, eagerness to train, and perceived significance of training were positively correlated with membership in both transitional-development and mature-competency profiles, but again the association with educational attainment is the strongest. Only the mature-competency profile showed a statistically significant difference in professional title whilst the years of work experience did not have any significant relationship with membership of profile in any of the contrasts. All these findings, together, indicate that inputs on competence maturation can rely more on education and learning-related input than tenure does.\u003c/p\u003e \u003cp\u003eMore education can increase knowledge assimilation, facts and reasoning on matters of abstract theories, and promote progression to greater degrees of maturity \u003csup\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/sup\u003e. The correlations in relation to professional title could build up the effect of more role and task complexity; the nurses who receive higher titles have more possibilities to acquire the responsibilities of preceptorship (training junior nurses), quality assurance and improvement activities, technical procedures in the specialty, and complex management cases obtain more intensive and more qualified practice opportunities, and it makes them competent in maturation \u003csup\u003e[\u003cspan additionalcitationids=\"CR32\" citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]\u003c/sup\u003e. This absence of association with the number of years of work experience is also in agreement with systematic reviews that competence development is not progressive with tenure, but rather is contingent on the structured learning and quality practice \u003csup\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]\u003c/sup\u003e. In that regard, years of on-the-job experience might not be sufficient to measure quality of development, and competence might change or level off between career phases.\u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003eInterpreting Training Motivation Through Self-Determination Theory (SDT)\u003c/h2\u003e \u003cp\u003eThe high levels of predictive validity of intention to participate in training and perceived relevance of training in competency maturity imply that the correlations of maturity lie not only in the training provision, but better in the ability of nurses to attribute learning value and the quality of motivation. The fact that the three profiles have no substantial differences in the number of individuals who participate further suggests that in comparable organizational settings, the volume of training is not enough to clarify the difference in maturity. Under the SDT approach, intention and perceived importance may be regarded as predictors of motivational internalization. When participation contributes to the fulfillment of the basic psychological needs of nurses to attain autonomy, competence and the associated relatedness, then nurses would tend to develop autonomous motivation, continue to learn, and translate training into practice in clinical environments; when participation is influenced by mandates, then participation may likely be based on the achievement of requirements leading to a limited growth in competency \u003csup\u003e[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]\u003c/sup\u003e. The investigation of continuing professional development (CPD) also indicates that motivation, organizational support, working load, and the relevance and accessibility of courses are all the factors that collectively determine continuing education results \u003csup\u003e[\u003cspan additionalcitationids=\"CR34\" citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]\u003c/sup\u003e. In this regard, interventions need to focus on the transfer of training to establish a close-loop of implementation, which incorporates goal-oriented feedback, deliberate practice, and reflective practices \u003csup\u003e[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003eDeveloping Tiered Training Pathways, Competency-Oriented Assessment, and an Evidence-Based TCM Nursing System\u003c/h2\u003e \u003cp\u003eClassification of profiles based on profile allows differentiated interventions to be practiced by the nursing management and education with a focus to plan competency-development pathways that can be transferred across. In the case of the novice profile, training must focus on standardized background knowledge and core technical skills, and with scenario-centered education and a preceptorship model to reinforce the practice of protocol adherence. The transitional one that includes the highest number of nurses in the shift should redirect the training to incorporate technical abilities in complicated scenarios, decision-making in pattern differentiation-guided nursing (bianzheng shihu) and risk management. Weak areas of high frequencies can be tackled by means of an intentional practice, competency checklists, and goal-focused feedback to address the performance levels \u003csup\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/sup\u003e. In the case of the mature profile, it should be focused on specialty and development of role-based competence through systematic development of skills in specialty care in the outpatient environment, clinical education, nursing management, translation of evidence, and quality enhancement, which are the role models and facilitators \u003csup\u003e[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThis assessment system ought to combine scale-based assessments with objective evidence that involves psychometric testing of instruments to achieve a sense of validity \u003csup\u003e[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]\u003c/sup\u003e. It must also include structured clinical competency tests (e.g. objective structured clinical examinations [OSCEs]) and process and outcome quality measures (e.g. clinical questions, evidence summary and implementation plans) to eliminate the common-method bias and minimise the chance of a mismatch between motivation and competence. On the policy level, in compliance with nationalized needs to enhance TCM nursing service capacity and development of its workforce \u003csup\u003e[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]\u003c/sup\u003e, the creation and execution of the evidence-based consensus statement or clinical guidelines can promote the standardization of training and standardized clinical practice \u003csup\u003e[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]\u003c/sup\u003e. Unfortunately, alongside it, the promotion of the creation of TCM nursing outpatient clinics and specialty posts may be the role platforms of the mature profile and extend the spillover effects \u003csup\u003e[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]\u003c/sup\u003e. Future studies can use longitudinal or Interventional research designs to the ability of learning motivation and learning behaviors to shape competency maturity as well as to test how these two factors interact with training exposure.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study was a systematic methodology study to assess the Traditional Chinese Medicine (TCM) nursing competency maturity of clinical nurses in Class A tertiary hospitals (the highest tier of tertiary hospitals in China) using a questionnaire survey that has been executed through the use of a cross-sectional survey designs and has shown significant differences in terms of nursing knowledge, technical skills, and clinical practice competence. In general, there was moderate competency maturity. The competence of technical skills was relatively higher, and the competence in clinical practice was relatively less, and there was an indication of a developmental illusion in the aspect of transitioning through the execution of technical tasks to high-level clinical practice in complex scenarios.\u003c/p\u003e \u003cp\u003eLatent profile analysis revealed three competency-maturity profiles that had low-high gradient distributions, the transitional development profile was the major group and this denotes that majority of the nurses are at a critical transition point of competency development. There were considerable differences in profiles with regard to years of service, professional title, education attainment, and attitudes towards training including that of willingness to take part in training and perceived importance of training contributed stably to profile differentiation. Specifically, multinomial logistic regression indicated that educational attainment and training related attitude factor were always predictive of membership of higher-maturity profiles and those involving professional title only significant correlates against the mature competency profile, but years of service was not significant in multivariate models. Such results suggest that accumulating tenure alone cannot account for competency promotion, and must be interpreted in relation to educational qualifications, job position and training motivation.\u003c/p\u003e \u003cp\u003eOn the basis of such findings, managers ought to incorporate profile-based, tiered development and more precisely allocate training resources: In the case of the novice profile, they should focus on the augmentation of foundational knowledge and the essential technical processes; in transitional profile, they should focus on the competency of clinical practice by training them on more scenarios and facilitating training transfer; and in the mature profile, they should focus on the further training and preceptorship and specialization. Training willingness and value endorsement training motivation factors should too be included in the design of training strategies to improve investment to learning as well as the effectiveness of training transfer.\u003c/p\u003e \u003cdiv id=\"Sec26\" class=\"Section2\"\u003e \u003ch2\u003eStrengths, Limitations, and Future Directions\u003c/h2\u003e \u003c/div\u003e \u003cdiv id=\"Sec27\" class=\"Section2\"\u003e \u003ch2\u003eStrengths and limitations\u003c/h2\u003e \u003cp\u003eA key strength of this study is the use of latent profile analysis (LPA) to characterize unobserved heterogeneity in nurses\u0026rsquo; Traditional Chinese Medicine (TCM) nursing competency maturity. LPA is more resource-allocation theoretical and tiers-based developmental on competency configurations than traditional mean based comparisons because it more directly identifies different developmental-stage specific competency configurations. Incorporating motivational variables into profile comparisons and multinomial logistic regression, including willingness to undergo training and perceived importance of training, we were able to test competency-motivation relationship quantitatively and in a way that is consistent with reality as perceived in decision-making as far as training management in the hospital is concerned. A standardized questionnaire with a strict data quality control procedure was utilized in the study, and the internal consistency was satisfactory, and the common method variance (CMV) was tested, and the CMV test did not indicate any serious problem. Managerial implications of the findings can also be well spelled out because profile-specific deficits as well as its description can be mapped into practical training pathways and career development options.\u003c/p\u003e \u003cp\u003eThere are a number of restrictions that should be considered. To start with, the cross-sectional research design restricts the use of causal inference and is incapable of establishing the causal pathway or dynamic feedback between the attitudes and competency maturity due to training. Second, the sample was represented by a single tertiary hospital, and the need to be able to generalize the findings to take the other regions and subsets with regard to the availability of TCM nursing services has to be verified. Third, training attitudes and competency were measured using a self-report, which could be biased by the response (e.g., social desirability bias). In spite of the fact that CMV was checked, residual bias cannot be completely dismissed. Fourth, due to the higher levels of total scores used in the LPA, item-level differences might have been lost, which could possibly undermine within-profile heterogeneity of competency structure. Lastly, the regression models mainly comprised of demographic and training-attitude variables and failed to exhaust other important determinants, including organizational support, job situation, and the real training exposure.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec28\" class=\"Section2\"\u003e \u003ch2\u003eFuture directions\u003c/h2\u003e \u003cp\u003eFuture studies must be done in inter-regional and inter-hospital multicenter studies. It is also necessary to involve institutions of different degrees of TCM nursing services delivery to justify the structure of the profile, its proportions distribution as well as stability and contribute to the generalizability. Longitudinal or cohort designs must be used to outline profile changes and development patterns, as well as to estimate how well educational education, professional job title, role exposure and training motivation advance competency. Regarding measurement, the addition of objective assessment data of competency rather than self-report and optimization of exposure variables in regards to participation in training, training quality, and practice opportunities are encouraged, which can enhance mechanistic interpretation and decrease single-method bias. As an intervention issue, profile-based tiered training interventions are to be planned and piloted, especially those based on the lack of advanced clinical practice competence. These programs are supposed to focus on scenario based training, feedback loops and training transfer; and measure post-training competency change, sustainability, and results of clinical application.Mechanistically, more systematic model development and testing around the pathway of \u0026ldquo;training value endorsement\u0026ndash;learning investment\u0026ndash;training transfer\u0026rdquo; may provide stronger evidence to inform hospital strategies for tiered development, career progression, and training resource allocation.\u003c/p\u003e \u003c/div\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAIC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAkaike information criterion\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBIC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBayesian information criterion\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBLRT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ebootstrap likelihood ratio test\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003econfidence interval\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCMB\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ecommon method bias\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCMV\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ecommon method variance\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCPD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003econtinuing professional development\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCVI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003econtent validity index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eFIML\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003efull information maximum likelihood\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLMRT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLo\u0026ndash;Mendell\u0026ndash;Rubin adjusted likelihood ratio test\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLPA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003elatent profile analysis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eML\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003emaximum likelihood\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eOR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eodds ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003estandard deviation\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSDT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eself-determination theory\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSE\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003estandard error\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSPSS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eStatistical Package for the Social Sciences (IBM SPSS Statistics)\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTCM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003etraditional Chinese medicine\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eWHO\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eWorld Health Organization\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003ch2\u003eEthics approval and consent to participate\u003c/h2\u003e\n\u003cp\u003eThe study protocol was approved by the Ethics Committee of Guangyuan Central Hospital (approval no. GYZXLL2024010). This cross-sectional study involved no intervention and was conducted in accordance with the Declaration of Helsinki and the Belmont Report principles. Participation was voluntary and the questionnaire was completed anonymously. Written and verbal informed consent was obtained from all participants.\u003c/p\u003e\n\u003ch2\u003eConsent for publication\u003c/h2\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003ch2\u003eAvailability of data and materials\u003c/h2\u003e\n\u003cp\u003eThe datasets generated and/or analysed during the current study are available from the corresponding author on reasonable request. The data are not publicly available due to the need to protect participants’ privacy.\u003c/p\u003e\n\u003ch2\u003eCompeting interests\u003c/h2\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eThis work was supported by the National Natural Science Foundation of China (Grant No. 52503166) and the Science and Technology Department of Sichuan Province (Grant No. 2024NSFSC1023).\u003c/p\u003e\n\u003ch2\u003eAuthors’ contributions\u003c/h2\u003e\n\u003cp\u003eMJ conceived the study and led the research design. YPL and JY contributed to the study design and coordinated the implementation of the study. YS and XZ were responsible for data cleaning and manuscript formatting/layout. MJ, JY, and YL conducted the statistical analyses and interpreted the data. ZW provided overall supervision, critically revised the manuscript for important intellectual content, and gave final approval of the version to be published. MJ drafted the initial manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003ch2\u003eAcknowledgements\u003c/h2\u003e\n\u003cp\u003eThe authors thank the Nursing Department of Guangyuan Central Hospital for its support in coordinating questionnaire distribution and all nurses who participated in the survey.\u003c/p\u003e\n\u003ch2\u003eAuthors’ information\u003c/h2\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSingh B, Kumar A. Ageing, multimorbidity, and quality of life: a mediation analysis using longitudinal ageing study in India. 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Med (Baltim). 2024;103(46):e40527. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1097/MD.0000000000040527\u003c/span\u003e\u003cspan address=\"10.1097/MD.0000000000040527\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\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":"Traditional Chinese Medicine (TCM) nursing competency, competency maturity, latent profile analysis (LPA), clinical nurses, cross-sectional study, intention to participate in training, training needs, multinomial logistic regression","lastPublishedDoi":"10.21203/rs.3.rs-8921882/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8921882/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eTo assess the overall level of self-assessed maturity of Traditional Chinese Medicine (TCM) nursing competency among clinical nurses, identify latent profiles, and examine factors associated with profile membership.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eFrom January to October 2024, a cross-sectional survey was conducted at a tertiary Grade A hospital in China. Eligible nurses were recruited through hospital-wide questionnaire distribution and voluntary participation. A total of 457 valid questionnaires were analyzed. Data were collected using the \u0026ldquo;Traditional Chinese Medicine Nursing Competency Questionnaire\u0026rdquo; and a TCM-nursing staff training needs questionnaire. Latent profile analysis (LPA) was used to classify competency maturity profiles, and multinomial logistic regression was performed to identify associated factors.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eOverall maturity of TCM nursing competency was moderate. Scores were higher for TCM nursing techniques but lower for advanced clinical practice, indicating relatively limited advanced-practice and integrative application capacity. LPA supported a three-profile solution (entropy\u0026thinsp;=\u0026thinsp;0.846): Developing (12.3%), Transitional (64.3%), and Mature (23.4%) profiles. Profiles differed in years of service, professional title, educational attainment, intention to participate in training, and perceived importance of training (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). In multinomial logistic regression, lower educational attainment, weaker intention to participate in training, and lower perceived importance of training were associated with membership in lower-maturity profiles; professional title was significant in selected comparisons, whereas years of service was not independently associated after adjustment.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eSelf-assessed TCM nursing competency maturity among clinical nurses shows a stratified profile pattern dominated by a Transitional group, with advanced clinical practice as a relative weakness. Profile-informed, tiered development and targeted training\u0026mdash;emphasizing contextualized practice and translation of competencies into clinical practice\u0026mdash;may be warranted. These associations should be further verified in longitudinal or intervention studies.\u003c/p\u003e","manuscriptTitle":"Training Intention and Latent Profiles of Traditional Chinese Medicine (TCM) Nursing Competency: Associated Factors Identified via Latent Profile Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-26 16:55:14","doi":"10.21203/rs.3.rs-8921882/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-22T06:52:27+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-19T20:56:26+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"164662404218960413914842582069710028745","date":"2026-04-15T18:09:13+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-11T07:11:06+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"40176935853679506003539040827201569736","date":"2026-03-01T01:48:39+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"296787491717107510990286916898785590109","date":"2026-02-25T22:01:33+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-23T16:29:36+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-02-23T08:53:09+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-21T11:01:12+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-21T10:58:15+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Nursing","date":"2026-02-20T02:59:28+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":"a07de2c5-9779-428a-881e-592becde0219","owner":[],"postedDate":"February 26th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-30T15:40:08+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-26 16:55:14","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8921882","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8921882","identity":"rs-8921882","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}