Competency in Infectious Disease Emergency Response among Healthcare-Associated Infection Control Practitioners in Guizhou Province, China: A Cross-Sectional Study

Competency in Infectious Disease Emergency Response among Healthcare-Associated Infection Control Practitioners in Guizhou Province, China: A Cross-Sectional Study | 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 Competency in Infectious Disease Emergency Response among Healthcare-Associated Infection Control Practitioners in Guizhou Province, China: A Cross-Sectional Study Maojie Zhang, Shengwei Wu, Mohd Ismail Ibrahim, Siti Suraiya Md Noor, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6745432/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 8 You are reading this latest preprint version Abstract Background: Competency in infectious disease emergency response among Healthcare-Associated Infection Prevention and Control Practitioners (HAI-PCPs) is critical for effective outbreak management and infection control within healthcare settings. However, evidence regarding the current competency levels of HAI-PCPs and the factors influencing their emergency response capabilities remains limited in Guizhou Province, China. Objective: This study aimed to assess the competency of HAI-PCPs in infectious disease emergency response in Guizhou Province, identify demographic and professional factors associated with competency, and explore their training needs to inform targeted capacity-building strategies. Methods: A cross-sectional survey was conducted among HAI-PCPs across various healthcare institutions in Guizhou Province. Competency was evaluated using a previously developed and validated structured questionnaire. Descriptive statistics summarized competency levels, while univariate and multivariate logistic regression analyses identified factors independently associated with competency status. Training needs were assessed based on identified competency gaps and participants’ self-reported educational demands. Results: The overall competency rate in infectious disease emergency response was 36.8%, indicating substantial room for improvement. Multivariate analysis revealed that age, work experience, income level, and recent participation in infection control training were significantly associated with competency (p < 0.05). The identified training needs and low-performing competency areas highlight key training and capacity development targets. Conclusion: The competency of HAI-PCPs in infectious disease emergency response in Guizhou Province is currently suboptimal. There is a pressing need for systematic, competency-based training programs tailored to address specific skill gaps among practitioners. Promoting a model based on competency evaluation and training needs assessment can enhance training depth, optimize content comprehension, and improve training effectiveness. Healthcare-Associated Infection Prevention and Control Practitioners Infectious Disease Emergency Response Competency Assessment Training Needs Cross-Sectional Study Figures Figure 1 Figure 2 1. Introduction Healthcare-associated infections (HAIs), also known as hospital infections (HIs) or nosocomial infections (NIs), represent a significant challenge in healthcare sons are acquired by patients during their stay in healthcare facilities and are not present or incubating at the time of admission. HAIs can lead to severe complications, prolonged hospital stays, and increased mortality, particularly in vulnerable patient populations( 1 ). The prevalence and impact of HAIs have been further exacerbated during the COVID-19 pandemic, as healthcare systems have been strained, and infection control measures have been challenged( 2 ). The emergence of infectious diseases, such as COVID-19, has further underscored the critical importance of infection prevention and control (IPC)( 3 ). During the early stages of the pandemic, the incidence of HAIs increased rapidly, disrupting the normal functioning of healthcare institutions( 4 ). This revealed deficiencies in emergency response capacity among healthcare workers and exposed vulnerabilities in institutional risk management systems. Effective response to infectious disease emergencies relies on timely outbreak detection, rapid containment, and proper antimicrobial stewardship—all of which depend heavily on the competence of infection control personnel. Strengthening the emergency response competency of HAI Prevention and Control Practitioners (HAI-PCPs) is, therefore, essential to ensure the resilience and responsiveness of IPC systems. Their ability to act decisively during public health crises directly affects patient safety and institutional preparedness. Although the World Health Organization (WHO) and other international bodies have proposed core competency frameworks for IPC professionals( 5 ), empirical studies assessing the emergency preparedness of HAI-PCPs in China remain scarce. Most existing research focuses on infection rates, infrastructure, and protocol compliance, with little attention given to IPC personnel's preparedness, competency, and training needs. Moreover, validated tools are lacking to measure individual competency and explore associated demographic and professional factors. Guizhou Province, located in western China, is a representative under-resourced region( 6 ) characterized by limited medical resources, uneven distribution of healthcare personnel, and underdeveloped IPC infrastructure. The province's high population mobility and vulnerability to infectious disease outbreaks further highlight the urgency of strengthening local IPC capacity. Studying this context addresses local needs and provides valuable insights for similar settings in other low- and middle-income countries. To address these gaps, this study employed a previously developed and validated competency evaluation scale to conduct a cross-sectional assessment of emergency response competency and training needs among HAI-PCPs in Guizhou Province. The study aimed to ( 1 ) determine the proportion of HAI-PCPs who are competent in infectious disease emergency response, ( 2 ) identify factors associated with competency, and ( 3 ) explore their specific training needs. Findings from this study will inform the development of targeted, competency-based training programs and guide workforce planning and policy development to enhance emergency preparedness in under-resourced healthcare settings. 2. Methods 2.1 Study Design and Setting This was a cross-sectional study conducted in Guizhou Province, China. The setting included secondary and tertiary public and private hospitals across several municipalities. The survey was conducted between February 1-29, 2024. Given the uneven distribution of training resources, especially in regional hospitals, the study targeted comprehensive hospitals where infection prevention and control practitioners are more representative of the provincial healthcare system. 2.2 Study Participants Inclusion criteria: Practitioners responsible for healthcare-associated infection (HAI) prevention and control; Working full-time in the department; At least 2 years of relevant work experience; Voluntary participation and completion of the questionnaire. Exclusion criteria: Not actively engaged in infection control work within the past two years; No formal qualifications or certification in hospital infection management; Did not acknowledge the importance of competency assessment in infection control; Inability to comprehend or complete the online questionnaire due to reading or communication barriers. Sampling method: Convenience sampling was used. Sample size estimation: Based on logistic regression with 10 predictors, the minimum required sample size was calculated using the rule of thumb of 10 participants per variable(7), resulting in a minimum of 100 participants. Two hundred forty practitioners were invited to ensure adequate power and account for potential non-responses. 2.3 Instrument The validated HAI-PCPs Competency Index Questionnaire, developed in a previous study, was the primary data collection tool (Appendix 1). The questionnaire includes items reflecting essential competencies required for infectious disease emergency response among HAI practitioners. The instrument demonstrated good internal consistency (Cronbach's alpha > 0.80) and construct validity, established through expert consultation and exploratory factor analysis. It also contained demographic items, and a training needs assessment section. The questionnaire includes i. HAI-PCP Sociodemographic and professional factors, including age (categorized), Gender, Educational attainment (bachelor's, master's, doctoral), Professional title (junior, intermediate, senior), Working experience (in years), Type of hospital (secondary/tertiary), Training within the past 6 months (yes/no), Previous training level, Monthly income level. ii. HAI-PCP Competency Self Evaluation Scale: It contains five dimensions: Basic knowledge, Advanced Infection Control Competency, Environmental hygiene, Leadership and Professionalism. There were a total of 44 entries. Respondents rated their self-evaluation competency on a 5-point Likert scale (1 = incompetent, 5 = very competent). iii. The HAI-PCP training needs survey includes a survey of semi-open training content and training mode preferences. 2.4 Statistical Analysis All analyses were conducted using SPSS version 26.0. Descriptive statistics were used to summarize demographic characteristics and competency scores (mean, standard deviation, frequency, and percentage). Bivariate analysis was performed using Chi-square tests (for categorical variables) and t-tests (for continuous variables) to explore associations between competency status and independent variables. Multivariate analysis was conducted using logistic regression to identify independent predictors of competency status. Variables with p < 0.10 in bivariate analyses were included in the model. Odds ratios (ORs) and 95% confidence intervals (CIs) were reported. Multicollinearity was assessed using the variance inflation factor (VIF), with VIF ≥ 5 indicating multicollinearity. Model fit was assessed using the Hosmer–Lemeshow goodness-of-fit test and Nagelkerke’s pseudo R². All tests were two-tailed, and a significance level of p < 0.05 was used. Complete-case analysis was applied since no missing values were observed. 2.5 Data Collection Procedure Data were collected using an online survey platform (MikeCRM: https://www.mikecrm.com). The questionnaire link was distributed to 30 selected secondary and tertiary hospitals. Participants completed the survey anonymously. Logic checks were built into the online system to ensure data quality and incomplete submissions were automatically excluded. 3. Results 3.1 Baseline characteristics of the HAI-PCP in Guizhou province. 240 questionnaires were distributed, and 209 qualified questionnaires were collected, with a response rate of 87.1%. The general characteristics of the 209 professionals who participated in the survey are shown in Table 1 , which presents frequency distributions, mean scores, and competent counts across subgroups. 3.2 Basic competency ratio of the HAI-PCP in Guizhou province. Competency scores were collected from 209 HAI Prevention and Control Practitioners in Guizhou Province. Scores ranged from 97 to 219 out of a maximum of 220 points, with a mean score of 160.20 ± 28.0, a median of 162, and an interquartile range (IQR) of 137 to 179. To facilitate interpretation, the scores were converted into a percentage scale. Based on a predefined threshold of 75% (equivalent to 176 points), participants scoring ≥ 176 were categorized as competent, while those scoring < 176 were classified as non-competent. Based on the predefined competency threshold of 75%, 77 out of 209 participants (36.8%) were classified as competent, while 132 participants (63.2%) were categorized as non-competent. The distribution of competency levels among participants is illustrated in Fig. 1 . 3.3 Determine the relevant factors for competence Due to multiple variable stratification, the results of the univariate analysis were not significant. Therefore, the variable stratification was adjusted and merged, and a Univariate logistic regression analysis was conducted to identify variables potentially associated with competency status after training. The results indicated that the following variables were significantly associated with higher odds of being competent(Table 2 ): age group 40–59 years compared to 20–39 years (OR = 3.35, 95% CI: [1.85–6.08], p < 0.001); Professional title gourp senior title (OR = 6.73, 95% CI: [3.00–15.10], p < 0.001), intermediate title (OR = 2.23, 95% CI: [1.05–4.73], p = 0.037) compared to Junior title; working experience of ≥ 10 years compared to <10 years (OR = 5.29, 95% CI: [2.48–11.29], p 5000 CNY compared to ≤ 5000 (OR = 3.32, 95% CI: [1.79–6.16], p < 0.001); Hospital classification in tertiary hospitals compared to Secondary hospitals (OR = 1.92, 95% CI: [1.08–3.41], p = 0.027), and having received training within the past six months (OR = 2.23, 95% CI: [1.03–4.83], p = 0.041). Majors( p = 0.089 ) and Previous Training Level ( p = 0.080 ) also were selected as candidates for inclusion in the subsequent multivariate logistic regression model. There were eight variables (Age, Major, Professional title, Working Experience(yrs), Hospital classification, Training Within Six Months, Previous Training Level and Income Level. A stepwise logistic regression analysis was conducted to identify factors associated with post-training competency. After sequentially removing non-significant variables, the final model(step 6) retained three predictors: age, working years, and income. The results are shown in Table 3 . Table 1 The general characteristics of the 209 professionals Category Total n(%) Mean ± SD Mean ± SD in Percentage Competent(%) Gender Male 29(13.9) 167.1 ± 22.2 69.9 ± 12.6 13(44.8) Female 180(86.1) 159.1 ± 28.7 65.4 ± 16.3 64(35.6) Age (years) 20–29 29(13.9) 149.2 ± 19.5 59.8 ± 11.1 3(10.3) 30–39 83(39.7) 152.6 ± 27.2 61.7 ± 15.4 24(28.9) 40–49 60(28.7) 161.8 ± 29.0 67.0 ± 16.5 23(38.3) 50–59 37(17.7) 183.1 ± 20.0 79.0 ± 11.4 27(73.0) Academic degree Doctor 1(0.7) 195.0 ± 0.0 85.8 ± 0.0 1(100.0) Master 13(6.2) 172.2 ± 2.5 72.9 ± 1.4 2(15.4) Bachelor 163(78.0) 159.8 ± 28.2 65.8 ± 16.0 63(38.7) Others 32(15.3) 156.4 ± 31.2 63.9 ± 17.7 11(34.4) Major Clinical medicine 50(23.9) 165.8 ± 22.2 69.2 ± 12.6 19(38.0) Nursing 116(55.5) 160.9 ± 28.1 66.4 ± 16.0 47(40.5) Public health 25(12.0) 150.4 ± 29.7 60.5 ± 16.9 6(24.0) Medical laboratory science 12(5.8) 157.9 ± 42.8 64.7 ± 24.3 5(41.7) Others 6(2.9) 145.3 ± 16.2 57.8 ± 9.2 0(0.0) Professional title Senior 10(4.8) 187.9 ± 12.3 81.2 ± 7.0 8(80.0) Associate Senior 47(22.5) 176.4 ± 22.6 75.2 ± 12.8 27(57.4) Intermediate 84(40.2) 156.3 ± 28.2 63.9 ± 16.0 29(34.5) Junior 54(25.8) 151.0 ± 22.9 60.3 ± 13.0 11(20.4) Others 14(6.7) 148.0 ± 34.1 59.1 ± 19.4 2(14.3) Working Experience(yrs) 0–4 108(51.7) 150.1 ± 27.3 60.7 ± 15.5 23(21.3) 5–9 65(31.1) 164.4 ± 23.9 68.4 ± 13.6 29(44.66) 10–14 23(11.0) 170.8 ± 24.6 72.0 ± 14.0 12(52.2) 15–19 7(3.3) 198.6 ± 14.1 87.8 ± 8.0 7(100.0) ≥ 20 6(2.9) 196.3 ± 15.8 86.6 ± 9.0 6(100.0) Hospital classification Secondary hospitals 129(61.7) 154.1 ± 29.4 62.6 ± 16.7 40(31.0) Tertiary hospitals 80(38.3) 170.0 ± 22.3 71.6 ± 12.8 37(46.3) Training Within Six Months Yes 166(79.4) 163.6 ± 27.4 67.9 ± 15.6 67(40.4) No 43(20.6) 147.6 ± 26.9 58.9 ± 15.3 10(23.3) Previous Training Level National level 11(5.3) 166.1 ± 33.5 69.4 ± 19.0 11(100.0) Provincial level 100(47.8) 161.5 ± 30.4 65.6 ± 16.1 40(40.0) Municipal level 97(46.4) 159.5 ± 28.3 66.0 ± 15.5 30(30.6) Income Level ≤ 3000 12(5.7) 158.8 ± 24.0 65.2 ± 13.6 3(25.0) 3001–5000 79(37.8) 148.8 ± 27.8 59.5 ± 15.7 17(21.5) 5001–7000 71(34) 163.6 ± 25.1 68.0 ± 14.3 29(40.8) 7001–9000 32(15.3) 175.3 ± 29.0 74.6 ± 16.5 20(62.5) ≥ 9001 15(7.2) 173.0 ± 20.7 73.3 ± 11.7 8(53.3) Table 2 Results of Univariate Logistic Regression Analysis Variables B S.E Wald df p OR CI 95%[lower,upper] (Constant) Gender Male Female -0.387 0.405 0.915 1 0.339 0.679 -0.387 0.405 Age <40 ≥ 40 1.209 0.300 16.218 1 <0.05 3.349 1.209 0.300 Academic degree Low education (Bachelor + Others) High education (master's or above) -0.808 0.668 1.462 1 0.227 0.446 -0.808 0.668 Major Medical related majors (Clinical medicine + Nursing) Non-medical related majors(Public health + Medical laboratory science + Others) -0.652 0.384 2.889 1 0.089 0.521 -0.652 0.384 Professional title Junior title (Junior and others) Intermediate 0.802 0.384 4.357 1 0.037 2.231 0.802 0.384 Senior title (Senior + Associate Senior) 1.907 0.411 21.495 1 <0.05 6.731 1.907 0.411 Working Experience <10 ≥ 10 1.666 0.398 17.512 1 <0.05 5.288 1.666 0.398 Hospital classification Secondary hospitals Tertiary hospitals 0.649 0.294 4.875 1 0.027 1.915 0.649 0.294 Training Within Six Months No Yes 0.803 0.394 4.156 1 0.041 2.233 0.803 0.394 Previous Training Level Municipal level High training (National + Provincial) 0.510 0.291 3.057 1 0.080 1.665 0.510 0.291 Income Level ≤ 5000 >5000 1.199 0.313 14.669 1 <0.05 3.317 1.199 0.313 Table 3 Results of Multivariable Logistic Regression Analysis Step Variables B S.E Wald df p OR CI 95% [lower, upper] Step 1 (Constant) Gender <40 ≥ 40 0.563 0.414 1.847 1 0.174 1.756 0.780 3.957 Age Secondary hospitals Tertiary hospitals 0.535 0.379 1.994 1 0.158 1.708 0.812 3.589 Academic degree Low education (Bachelor + Others) High education (master's or above) -0.464 0.742 0.392 1 0.531 0.629 0.147 2.691 Major Medical related majors (Clinical medicine + Nursing) Non-medical related majors(Public health + Medical laboratory science + Others) -0.412 0.439 0.883 1 0.347 0.662 0.280 1.564 Professional title Junior title (Junior and others) Intermediate 0.343 0.432 0.628 1 0.428 1.408 0.604 3.286 Senior title (Senior + Associate Senior) 0.557 0.617 0.815 1 0.367 1.746 0.521 5.852 Working Experience 5000 0.64 0.414 2.39 1 0.122 1.897 0.842 4.27 Step 6 (Constant) Age <40 ≥ 40 0.892 0.321 7.717 1 0.005 2.441 1.301 4.581 Working Experience 5000 0.984 0.331 8.827 1 0.003 2.674 1.398 5.117 The model demonstrated a good fit based on the Hosmer and Lemeshow test (χ² = 3.584, df = 5, P = 0.611), indicating no significant deviation between predicted and observed outcomes. Through the test for multicollinearity, it was found that the variance inflation factor (VIF) of each independent variable was all <10, indicating that there was no serious multicollinearity problem in the model and that the regression results were reliable. Regarding classification performance, the final model correctly identified 78.0% of participants who were not competent and 61.0% who were competent, resulting in an overall classification accuracy of 71.8%. All retained variables in the final model were statistically significant (P < 0.01). Their odds ratios suggested that older age (OR = 2.441, 95% CI: 1.301–4.581), longer working years (OR = 3.635, 95% CI: 1.592–8.302), and higher income (OR = 2.674, 95% CI: 1.398–5.117) were positively associated with competency. In the multivariate logistic regression analysis, after testing possible interaction terms, none of the interaction effects were statistically significant (p > 0.05), and the results were shown in Table 4 . Compared with models that included more variables, the final 3-variable model(Table 3 step 6) achieved similar classification accuracy and better interpretability, making it a practical and parsimonious choice for informing future training interventions. Table 4 Results of Multivariable logistic regression (Including interaction terms.) Variables B S.E Wald df p OR CI 95%[lower,upper] (Constant) Age <40 ≥ 40 1.366 0.546 6.258 1 0.012 3.918 1.344 11.423 Working Experience 5000 1.079 1.028 1.103 1 0.294 2.942 0.393 22.045 Working experience * age -1.161 1.033 1.263 1 0.261 0.313 0.041 2.372 Income level * age -0.507 0.675 0.565 1 0.452 0.602 0.16 2.26 Income level * Working experience 1.539 0.956 2.591 1 0.108 4.659 0.715 30.352 To further evaluate the predictive ability of each variable in the competency model, ROC analysis was performed on age, working experience, and income level (Fig. 2 ). The results show that these three variables can distinguish competency status: the area under the curve (AUC) is 0.735 and has a specific discriminatory ability. 3.4 HAI-PCP Low Capability Project and Training Needs Survey Analysis of 44 competency self-assessment items (overall mean = 3.64, IQR: Q1 = 3.47, Q3 = 3.77) identified low-competency priorities as those scoring below the 25th percentile (Q1 = 3.47). As shown in Table 5 , 12 critical items requiring intervention were identified, with mean scores ranging from 3.11 to 3.47, significantly lower than the median (Median = 3.64). Single-sample Wilcoxon signed-rank tests confirmed systematic competency gaps for all low-scoring items (p < 0.05). Summarize the training needs survey form attached to the HAI-PCPs competency index questionnaire, extract HAI-PCPs training needs keywords, and summarize the content as shown in Table 6 . Table 5 HAI-PCPs competency evaluation low-scoring items Code. Item Score H5 Modify and incorporate existing standards, guidelines, regulations, literature, and publications into the infection prevention and control program and establish evidence-based infection prevention and control strategies and methods 3.45 H8 Provide recommendations to reduce infection risk during the design, construction, and remodeling of healthcare environments concerning building layout. 3.38 H11 Master the basic knowledge of medical microbiology 3.11 H15 Participate in the management of clinical application of antimicrobial drugs 3.29 H16 Be able to identify and develop a surveillance program for hospital-acquired infections and their associated risk factors by national infection control policy requirements. 3.47 H18 Utilizing software to collect and process surveillance data and establish a systematic database. 3.39 H20 Ability to assess the risk of healthcare-associated infectious diseases 3.46 H34 Based on the results of infection control evaluation, match quality improvement tools and carry out quality improvement activities. 3.44 H42 Concerned with information on unknown infectious diseases 3.39 H53 Ability to actively participate in academic research related to hospital infections and skillfully use tools to obtain cutting-edge information on sensory control 3.47 H54 Ability to think creatively 3.39 H55 Apply hospital infection research tools and results to clinical practice 3.35 Table 6 Summary Table of HAI-PCPs Training Mode and Training Needs Survey Item Frequency Proportion(%) Training Duration 2–4 hours 45 0.274 4–8 hours 54 0.329 8–12 hours 39 0.238 More than 12 hours 26 0.159 Training Frequency Once a month 16 0.095 Once a quarter 66 0.391 Once every six months 65 0.385 Once a year 22 0.13 Training Mode Case analysis 48 0.245 Self-study with assessment 23 0.117 Offline training 45 0.23 Scenario/tabletop exercises 39 0.199 Online training 41 0.209 Training Needs Basic knowledge of HAI management 19 0.116 Management of key departments 15 0.091 Practical experience in higher-level hospitals 14 0.084 Risk assessment and management practice 12 0.072 Monitoring and management of multidrug resistance 11 0.065 Targeted monitoring 9 0.053 Diagnosis of HAI cases 9 0.053 Development of in-hospital training courses 8 0.047 Rational use of antibiotics 7 0.041 Interpretation of guidelines 7 0.04 Research 5 0.029 Key points of HAI in primary hospitals 5 0.029 Knowledge of pathogenic microorganisms 4 0.023 Management of HAI outbreaks 4 0.023 Management and communication skills 4 0.022 Feedback and supervision in clinical HAI work 3 0.017 Practice of clinical HAI supervision 3 0.017 Personal ability assessment and improvement 3 0.017 Precision management of HAI 2 0.011 Environmental hygiene sampling 2 0.011 Prevention and control key points in clinical departments 2 0.011 Surgical site management 2 0.011 HAI management in operating rooms 2 0.011 Continuous improvement and rectification of HAI 2 0.011 Hand hygiene compliance 2 0.011 Case analysis 1 0.005 Infectious diseases 1 0.005 Laws and regulations 1 0.005 Epidemiological investigation 1 0.005 Environmental cleaning and disinfection 1 0.005 Emergency drills 1 0.005 HAI elements in medical quality management 1 0.005 Tool usage in HAI work 1 0.005 4. Discussion 4.1The Competency Score Situation of HAI-PCPs in Guizhou Province Through the investigation of the research, it was found that only 36.8% were categorized as competent. This is similar to the result of the cross-sectional survey on HAI-PCPs also carried out in Guizhou Province (the competency rate was 38.3%)( 8 ), indicating that under the background of this research. This indicates a generally low level of professional competence within the region and highlights a significant need for capacity building. The competence of Infection Prevention and Control Practitioners (IPCPs) in China is a critical factor in managing healthcare-associated infections (HAIs) and mitigating the spread of infectious diseases( 9 ). Low job competency will lead to the low-quality implementation of infection prevention and control work and result in the recurrence of hospital-acquired infection incidents( 10 ). Although China currently lacks a national competency standard for HAI-PCPs, structured evaluation tools allow for real-time assessment, early detection of competency gaps, and the development of more targeted training strategies. 4.2 To identify factors associated with infectious disease emergency response competency The three elements of epidemiology, time, space, and population, are related to the occurrence and development of diseases( 11 ). This concept still applies to infectious diseases and is the basis for handling incidents. For infectious disease emergency response competence, the occurrence and handling of an event require more assurance of the basic abilities of the personnel involved. The results of the univariate analysis in this study showed that age, professional title, working experience, income level, and recent participation in training within the past six months were identified as factors related to the competency rate (P < 0.05). Multivariate logistic regression further confirmed the independent associations of age, work experience, and income level with competency status, highlighting their potential as key predictors of emergency response capability among HAI-PCPs. Among these, factors such as age, professional title, working experience, and income level exhibit a certain degree of temporal correlation. A higher professional title often reflects greater expertise and professionalism, typically accompanied by more extensive working experience and corresponding increases in income level. As a general demographic characteristic, age broadly represents the accumulation of experience over time, further emphasizing its relevance to professional competence( 12 ). Although there is a correlation between these factors and competency, there may be an interaction between them. For example, a professional title may result from experience and age( 13 ). Although the independence and relative contribution of these variables can be confirmed through statistical analysis such as multiple regression or structural equation modeling, this is not one of the purposes of this study, and further research is expected to expand upon it. Additionally, recent participation in training within the past six months is understandably a significant factor influencing professional competence. Training provides a direct pathway for skill enhancement and professional development, leading to measurable improvements in competence( 14 ). This finding aligns with this study's objectives, as training's impact on competence will be further examined in subsequent phases. Educational background did not show a significant impact (P > 0.05), which may reflect certain deficiencies in the continuing education system in Guizhou Province. HAI PCPs have not received systematic IPC training, making converting educational advantages into practical abilities difficult. If a suitable improvement model is not established promptly, it may form a vicious cycle of "low training → low professional title → low competence." Few studies on competency are directly related to HAI-PCP. However, in the study of medical institution service personnel, work experience, education level, and work environment can significantly affect the workability of professionals, which has a specific reference value for our research( 15 ). Future research should explore potential mediating or moderating effects among key demographic and institutional variables to better understand how these factors influence HAI-PCPs' competency development over time. 4.3 Training Needs from HAI-PCPs. Training needs assessment is a critical process for identifying gaps in skills and knowledge within various professional fields. It helps design effective training programs aligning with organizational goals and individual performance improvement. The Hennessy-Hicks Training Needs Analysis (TNA) questionnaire is a widely used tool endorsed by the World Health Organization for assessing training needs globally( 16 ). It has been adapted and translated into various countries, revealing training gaps and promoting continuous professional development across different disciplines and settings. The tool effectively prioritizes and allocates educational resources based on identified needs, addressing the "know-do" gap in global human resources for health( 16 ). This study adopts a similar research design approach to screen for training defects in low-scoring projects after competency evaluation and supplements the design concerning training needs to complete the development of the training module. While evaluating the competence of professionals, we also surveyed their future training needs. Based on the summary of training needs assessments, the top ten prioritized topics identified were basic knowledge of healthcare-associated infection (HAI) management, management of key departments, practical experience in higher-level hospitals, risk assessment, and management practices, monitoring and management of multidrug resistance, targeted monitoring, diagnosis of HAI cases, development of in-hospital training courses, rational use of antibiotics, and interpretation of guidelines. These areas highlight the weak links and lack of key capabilities of HAI-PCPs in infection prevention and control practices: Knowledge-based, Skill-based, and Management-oriented needs. The relevant content is also included in the competency evaluation indicators, proving that the HAI-PCPs competency indicators developed for research and development are scientific and comprehensive. Training needs assessment is foundational in designing effective professional development programs across various sectors. By utilizing validated tools, organizations can better align training with strategic goals and address specific skill gaps. This ensures that workforce development keeps pace with evolving industry demands and technological advancements. 4.4 limitation Although multiple abilities-related factors were identified, such as age, hospital level, professional title, work experience, etc., the study did not explore the possible interaction effects between these variables. For example, job titles may be influenced by both work experience and age, and these complex relationships have not been thoroughly analyzed, leading to limitations in factor association research. The lack of deeper statistical analysis on the independence or relative contribution of variables in the study (such as multiple regression or structural equation modeling) limits the understanding of causal relationships among influencing factors. The investigation of training needs is limited to areas related to low-scoring projects, which may have overlooked other potential but not yet recognized important training needs. The study did not provide a detailed explanation of the representativeness of the survey sample (such as regional distribution, differences in hospital levels, etc.), which may affect the generalizability and applicability of the results. 4.5 Future research directions Interaction effect: This study did not explore the possible interaction effects between variables such as age, job title, and work experience. Future research can use advanced statistical techniques such as multiple regression or structural equation modeling to understand the complex relationships between these factors better or focus on studying the impact of interaction effects on abilities. Training needs: Although this study focuses on low-scoring areas, it may overlook other important training needs. A more comprehensive investigation can capture a broader range of abilities that require attention, thereby gaining a more comprehensive understanding of training requirements. Sampling representativeness: This study did not provide a detailed explanation of the representativeness of the survey sample, such as regional distribution and hospital level. Future research should focus on more representative sampling to improve the generalizability and reliability of the results. 5. Conclusion This study revealed that only 36.8% of HAI-PCPs in Guizhou Province were competent in infectious disease emergency response, indicating a substantial gap in preparedness among the workforce. Key demographic and professional factors—including age, working experience, and income level—were significantly associated with competency status. Recent training was also a strong predictor of professional competence, underscoring the importance of continuous professional development. The study also identified prioritized training needs in knowledge-based, skill-based, and management-oriented domains, which can serve as a foundation for developing targeted training programs. These findings not only offer a valuable reference for workforce development in Guizhou Province but also have broader applicability to other resource-limited settings aiming to strengthen infection control capacity. Future research should explore the interaction effects between influencing factors using more advanced statistical techniques, such as structural equation modeling, and expand the scope of training needs assessment. Ensuring the representativeness of survey samples will also enhance the generalizability and reliability of future findings. Declarations Author Contributions MJ.Z. and SW.W. contributed to the study design, implementation of the survey, data collection, and interpretation of the results. M.I.I. and SS.MN. provided methodological guidance and assisted with data analysis. WMZ., as the corresponding author, supervised the study, provided critical feedback, and ensured the accuracy and integrity of the final submission. All authors read and approved the final manuscript. Funding This research received no external funding. Data Availability The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. Conflict of Interest The authors declare no competing interests. Ethics approval and consent to participate The study was conducted in accordance with the principles of the Declaration of Helsinki. The ethics committee with the code has approved this study: USM-JEPeM Code: USM/JEPeM/KK/23050390. Guizhou Traditional Chinese Medicine University Ethical review approval code: KS2023150. Informed consent was obtained from all participants prior to their inclusion in the study. Consent for publication Not applicable. Acknowledgements The authors would like to thank all healthcare-associated infection prevention and control practitioners who participated in this study for their valuable time and contributions. References Malheiro R, Gomes AA, Fernandes C, Fareleira A, Lebre A, Pascoalinho D, et al. Hospital Context Determinants of Variability in Healthcare-Associated Infection Prevalence: Multi-Level Analysis. Microorganisms. 2024 Dec 7;12(12):2522. https://doi.org/10.3390/microorganisms12122522 PMID: 39770725 Khavandegar A, Siami Z, Rasouli A, Nazemi P, Gull A. Impact of healthcare-associated infections on in-hospital outcomes during the COVID-19 era: a multicenter comparative study of 20,942 isolated microorganisms from ICU patients. Front Public Health. 2025;13:1475221. https://doi.org/10.3389/fpubh.2025.1475221 PMID: 39991697 Bangani O, English R, Dramowski A. Intensive care unit nurses’ knowledge, attitudes and practices of COVID-19 infection prevention and control. S Afr J Infect Dis. 2023;38(1):478. https://doi.org/10.4102/sajid.v38i1.478 PMID: 37435115 Verberk JDM, van der Kooi TII, Kampstra NA, Reimes N, van Rooden SM, Hopmans TEM, et al. Healthcare-associated infections in Dutch hospitals during the COVID-19 pandemic. Antimicrob Resist Infect Control. 2023 Jan 5;12(1):2. https://doi.org/10.1186/s13756-022-01201-z PMID: 36604755 Sonpar A, Hundal CO, Totté JEE, Wang J, Klein SD, Twyman A, et al. Multimodal strategies for the implementation of infection prevention and control interventions-update of a systematic review for the WHO guidelines on core components of infection prevention and control programmes at the facility level. Clin Microbiol Infect. 2025 Jun;31(6):948–57. https://doi.org/10.1016/j.cmi.2025.01.011 PMID: 39863071 Zhou Z, Zhu C. Relative Spatial Poverty Within Guizhou Province, A Multidimensional Approach. Soc Indic Res. 2022 May 1;161(1):151–70. https://doi.org/10.1007/s11205-021-02825-1 Courvoisier DS, Combescure C, Agoritsas T, Gayet-Ageron A, Perneger TV. Performance of logistic regression modeling: beyond the number of events per variable, the role of data structure. Journal of Clinical Epidemiology. 2011 Sep 1;64(9):993–1000. https://doi.org/10.1016/j.jclinepi.2010.11.012 Yao Y, Zha Z, Huang B, Jing Z, Wang L, Wu Q, et al. Factors associated with core competencies of infection prevention and control practitioners in 511 hospitals: A large cross-sectional survey in Guizhou in southwest China. Journal of Hospital Infection [Internet]. 2025 Feb 14 [cited 2025 Apr 19];0(0). https://doi.org/10.1016/j.jhin.2025.02.004 PMID: 39961511 Liu H, Fei C, Zhang X, Yang L, Ji X, Zeng Q, et al. What we learned from the infection control and what we need in the future: A quantitative and qualitative study on hospital infection prevention and control practitioners (HIPCPs) in Tianjin, China. Am J Infect Control. 2024 Sep;52(9):1073–83. https://doi.org/10.1016/j.ajic.2024.05.004 PMID: 38740285 Houben F, den Heijer CD, Dukers-Muijrers NH, Nava J-CB, Theunissen M, van Eck B, et al. Self-reported compliance with infection prevention and control of healthcare workers in Dutch residential care facilities for people with intellectual and developmental disabilities during the COVID-19 pandemic: A cross-sectional study. Disabil Health J. 2023 Oct 11;101542. https://doi.org/10.1016/j.dhjo.2023.101542 PMID: 39492010 Ahlbom A. Epidemiology is about disease in populations. Eur J Epidemiol. 2020 Dec 1;35(12):1111–3. https://doi.org/10.1007/s10654-020-00701-9 Ter Maten-Speksnijder A, Grypdonck M, Pool A, Meurs P, Van Staa A. Learning to attain an advanced level of professional responsibility. Nurse Educ Today. 2015 Aug;35(8):954–9. https://doi.org/10.1016/j.nedt.2015.03.005 PMID: 25825354 Herman S, Gish M, Rosenblum R, Herman M. Effects of RN Age and Experience on Transformational Leadership Practices. J Nurs Adm. 2017 Jun;47(6):327–37. https://doi.org/10.1097/NNA.0000000000000488 PMID: 28509720 Al-Omary H, Soltani A, Stewart D, Nazar Z. Implementing learning into practice from continuous professional development activities: a scoping review of health professionals’ views and experiences. BMC Med Educ. 2024 Sep 20;24(1):1031. https://doi.org/10.1186/s12909-024-06016-7 PMID: 39304841 Rizany I, Hariyati RTS, Handayani H. Factors that affect the development of nurses’ competencies: a systematic review. Enfermería Clínica. 2018 Feb 1;28:154–7. https://doi.org/10.1016/S1130-8621(18)30057-3 Markaki A, Malhotra S, Billings R, Theus L. Training needs assessment: tool utilization and global impact. BMC Medical Education. 2021 May 31;21(1):310. https://doi.org/10.1186/s12909-021-02748-y Additional Declarations No competing interests reported. Supplementary Files appendix1.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 03 Jul, 2025 Reviewers agreed at journal 26 Jun, 2025 Reviewers agreed at journal 24 Jun, 2025 Reviewers invited by journal 24 Jun, 2025 Editor assigned by journal 18 Jun, 2025 Editor invited by journal 02 Jun, 2025 Submission checks completed at journal 30 May, 2025 First submitted to journal 30 May, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6745432","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":475910991,"identity":"452757ba-34ee-4c01-816a-86f90a04b196","order_by":0,"name":"Maojie Zhang","email":"","orcid":"","institution":"Universiti Sains Malaysia","correspondingAuthor":false,"prefix":"","firstName":"Maojie","middleName":"","lastName":"Zhang","suffix":""},{"id":475910992,"identity":"37e3c0ce-79d5-4bbe-8437-0807259a03a9","order_by":1,"name":"Shengwei Wu","email":"","orcid":"","institution":"The First Affiliated Hospital of Guizhou University of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Shengwei","middleName":"","lastName":"Wu","suffix":""},{"id":475910993,"identity":"50fd57e4-28bb-4dd9-a4a8-0139de648bed","order_by":2,"name":"Mohd Ismail Ibrahim","email":"","orcid":"","institution":"Universiti Sains Malaysia","correspondingAuthor":false,"prefix":"","firstName":"Mohd","middleName":"Ismail","lastName":"Ibrahim","suffix":""},{"id":475910994,"identity":"9001dcd9-a2e7-47d6-92c9-4bb10ff6dd08","order_by":3,"name":"Siti Suraiya Md Noor","email":"","orcid":"","institution":"Universiti Sains Malaysia","correspondingAuthor":false,"prefix":"","firstName":"Siti","middleName":"Suraiya Md","lastName":"N","suffix":"Md"},{"id":475910995,"identity":"88444111-0a21-4539-a725-292cb6a08453","order_by":4,"name":"Wan Mohd Zahiruddin","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAApklEQVRIiWNgGAWjYDCCAwwMzAwGDHKkazEmVQsDQ2ID0Tr4bh9/+Lmg4F76hvOnE5huthGhRfJcQrL0DIPi3A03cjcw5xKjxeAMwwFpHoMEoBZeorUwNv8Gakk3OH+WaC3MbCBbEgwOEOswyTNsbNZALYYzgX45nHOOCC18Z9gf3+b5kyDPd/7sxsc5ZURoQQEHGNlI1cLA8Id0LaNgFIyCUTD8AQDlIjdPhNJPkQAAAABJRU5ErkJggg==","orcid":"","institution":"Universiti Sains Malaysia","correspondingAuthor":true,"prefix":"","firstName":"Wan","middleName":"Mohd","lastName":"Zahiruddin","suffix":""}],"badges":[],"createdAt":"2025-05-25 19:38:03","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6745432/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6745432/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":85725246,"identity":"4b1dea22-3f0b-46dc-b931-061e0212e443","added_by":"auto","created_at":"2025-07-01 06:29:19","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":24615,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution chart of HAI PCPs competency self-assessment results.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6745432/v1/a74c61785e4cebeb33f8e281.png"},{"id":85725247,"identity":"1f32e4ae-dfb4-48e8-a416-9e420e3fa054","added_by":"auto","created_at":"2025-07-01 06:29:19","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":45030,"visible":true,"origin":"","legend":"\u003cp\u003eROC Curve of the HAI-PCPs Competency Classification Model\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6745432/v1/d7860e0eae90b644c11f751f.png"},{"id":85727854,"identity":"6475b191-dfec-4b47-9298-9bdc32035218","added_by":"auto","created_at":"2025-07-01 06:53:27","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1497885,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6745432/v1/03d2829c-15f6-47f3-808a-daae3e0d00c4.pdf"},{"id":85725256,"identity":"790dad7e-5aeb-44cf-881b-ffb84f6dd52d","added_by":"auto","created_at":"2025-07-01 06:29:19","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":1495070,"visible":true,"origin":"","legend":"","description":"","filename":"appendix1.docx","url":"https://assets-eu.researchsquare.com/files/rs-6745432/v1/c29c357aea65fa20626c6c7a.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Competency in Infectious Disease Emergency Response among Healthcare-Associated Infection Control Practitioners in Guizhou Province, China: A Cross-Sectional Study","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eHealthcare-associated infections (HAIs), also known as hospital infections (HIs) or nosocomial infections (NIs), represent a significant challenge in healthcare sons are acquired by patients during their stay in healthcare facilities and are not present or incubating at the time of admission. HAIs can lead to severe complications, prolonged hospital stays, and increased mortality, particularly in vulnerable patient populations(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). The prevalence and impact of HAIs have been further exacerbated during the COVID-19 pandemic, as healthcare systems have been strained, and infection control measures have been challenged(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe emergence of infectious diseases, such as COVID-19, has further underscored the critical importance of infection prevention and control (IPC)(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). During the early stages of the pandemic, the incidence of HAIs increased rapidly, disrupting the normal functioning of healthcare institutions(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). This revealed deficiencies in emergency response capacity among healthcare workers and exposed vulnerabilities in institutional risk management systems. Effective response to infectious disease emergencies relies on timely outbreak detection, rapid containment, and proper antimicrobial stewardship\u0026mdash;all of which depend heavily on the competence of infection control personnel. Strengthening the emergency response competency of HAI Prevention and Control Practitioners (HAI-PCPs) is, therefore, essential to ensure the resilience and responsiveness of IPC systems. Their ability to act decisively during public health crises directly affects patient safety and institutional preparedness.\u003c/p\u003e \u003cp\u003eAlthough the World Health Organization (WHO) and other international bodies have proposed core competency frameworks for IPC professionals(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e), empirical studies assessing the emergency preparedness of HAI-PCPs in China remain scarce. Most existing research focuses on infection rates, infrastructure, and protocol compliance, with little attention given to IPC personnel's preparedness, competency, and training needs. Moreover, validated tools are lacking to measure individual competency and explore associated demographic and professional factors.\u003c/p\u003e \u003cp\u003eGuizhou Province, located in western China, is a representative under-resourced region(\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e) characterized by limited medical resources, uneven distribution of healthcare personnel, and underdeveloped IPC infrastructure. The province's high population mobility and vulnerability to infectious disease outbreaks further highlight the urgency of strengthening local IPC capacity. Studying this context addresses local needs and provides valuable insights for similar settings in other low- and middle-income countries.\u003c/p\u003e \u003cp\u003eTo address these gaps, this study employed a previously developed and validated competency evaluation scale to conduct a cross-sectional assessment of emergency response competency and training needs among HAI-PCPs in Guizhou Province. The study aimed to (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) determine the proportion of HAI-PCPs who are competent in infectious disease emergency response, (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) identify factors associated with competency, and (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) explore their specific training needs. Findings from this study will inform the development of targeted, competency-based training programs and guide workforce planning and policy development to enhance emergency preparedness in under-resourced healthcare settings.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003ch3\u003e2.1 Study Design and Setting\u003c/h3\u003e\n\u003cp\u003eThis was a cross-sectional study conducted in Guizhou Province, China. The setting included secondary and tertiary public and private hospitals across several municipalities. The survey was conducted between February 1-29, 2024. Given the uneven distribution of training resources, especially in regional hospitals, the study targeted comprehensive hospitals where infection prevention and control practitioners are more representative of the provincial healthcare system.\u003c/p\u003e\n\u003ch3\u003e2.2 Study Participants\u003c/h3\u003e\n\u003cp\u003eInclusion criteria:\u003c/p\u003e\n\u003cp\u003ePractitioners responsible for healthcare-associated infection (HAI) prevention and control; Working full-time in the department; At least 2 years of relevant work experience; Voluntary participation and completion of the questionnaire.\u003c/p\u003e\n\u003cp\u003eExclusion criteria:\u003c/p\u003e\n\u003cp\u003eNot actively engaged in infection control work within the past two years; No formal qualifications or certification in hospital infection management; Did not acknowledge the importance of competency assessment in infection control; Inability to comprehend or complete the online questionnaire due to reading or communication barriers.\u003c/p\u003e\n\u003cp\u003eSampling method: Convenience sampling was used.\u003c/p\u003e\n\u003cp\u003eSample size estimation: Based on logistic regression with 10 predictors, the minimum required sample size was calculated using the rule of thumb of 10 participants per variable(7), resulting in a minimum of 100 participants. Two hundred forty practitioners were invited to ensure adequate power and account for potential non-responses.\u003c/p\u003e\n\u003ch3\u003e2.3 Instrument\u003c/h3\u003e\n\u003cp\u003eThe validated HAI-PCPs Competency Index Questionnaire, developed in a previous study, was the primary data collection tool (Appendix 1). The questionnaire includes items reflecting essential competencies required for infectious disease emergency response among HAI practitioners. The instrument demonstrated good internal consistency (Cronbach's alpha \u0026gt; 0.80) and construct validity, established through expert consultation and exploratory factor analysis. It also contained demographic items, and a training needs assessment section.\u003c/p\u003e\n\u003cp\u003eThe questionnaire includes\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ei. HAI-PCP Sociodemographic and professional factors, including age (categorized), Gender, Educational attainment (bachelor's, master's, doctoral), Professional title (junior, intermediate, senior), Working experience (in years), Type of hospital (secondary/tertiary), Training within the past 6 months (yes/no), Previous training level, Monthly income level.\u003c/p\u003e\n\u003cp\u003eii. HAI-PCP Competency Self Evaluation Scale: It contains five dimensions: Basic knowledge, Advanced Infection Control Competency, Environmental hygiene, Leadership and Professionalism. There were a total of 44 entries. Respondents rated their self-evaluation competency on a 5-point Likert scale (1 = incompetent, 5 = very competent).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eiii. The HAI-PCP training needs survey includes a survey of semi-open training content and training mode preferences.\u003c/p\u003e\n\u003ch3\u003e2.4 Statistical Analysis\u003c/h3\u003e\n\u003cp\u003eAll analyses were conducted using SPSS version 26.0. Descriptive statistics were used to summarize demographic characteristics and competency scores (mean, standard deviation, frequency, and percentage). Bivariate analysis was performed using Chi-square tests (for categorical variables) and t-tests (for continuous variables) to explore associations between competency status and independent variables. Multivariate analysis was conducted using logistic regression to identify independent predictors of competency status. Variables with p \u0026lt; 0.10 in bivariate analyses were included in the model. Odds ratios (ORs) and 95% confidence intervals (CIs) were reported. Multicollinearity was assessed using the variance inflation factor (VIF), with VIF ≥ 5 indicating multicollinearity. Model fit was assessed using the Hosmer–Lemeshow goodness-of-fit test and Nagelkerke’s pseudo R². All tests were two-tailed, and a significance level of p \u0026lt; 0.05 was used. Complete-case analysis was applied since no missing values were observed.\u003c/p\u003e\n\u003ch3\u003e2.5 Data Collection Procedure\u003c/h3\u003e\n\u003cp\u003eData were collected using an online survey platform (MikeCRM: https://www.mikecrm.com). The questionnaire link was distributed to 30 selected secondary and tertiary hospitals. Participants completed the survey anonymously. Logic checks were built into the online system to ensure data quality and incomplete submissions were automatically excluded.\u003c/p\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Baseline characteristics of the HAI-PCP in Guizhou province.\u003c/h2\u003e \u003cp\u003e240 questionnaires were distributed, and 209 qualified questionnaires were collected, with a response rate of 87.1%. The general characteristics of the 209 professionals who participated in the survey are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, which presents frequency distributions, mean scores, and competent counts across subgroups.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Basic competency ratio of the HAI-PCP in Guizhou province.\u003c/h2\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eCompetency scores were collected from 209 HAI Prevention and Control Practitioners in Guizhou Province. Scores ranged from 97 to 219 out of a maximum of 220 points, with a mean score of 160.20\u0026thinsp;\u0026plusmn;\u0026thinsp;28.0, a median of 162, and an interquartile range (IQR) of 137 to 179. To facilitate interpretation, the scores were converted into a percentage scale. Based on a predefined threshold of 75% (equivalent to 176 points), participants scoring\u0026thinsp;\u0026ge;\u0026thinsp;176 were categorized as competent, while those scoring\u0026thinsp;\u0026lt;\u0026thinsp;176 were classified as non-competent. Based on the predefined competency threshold of 75%, 77 out of 209 participants (36.8%) were classified as competent, while 132 participants (63.2%) were categorized as non-competent. The distribution of competency levels among participants is illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Determine the relevant factors for competence\u003c/h2\u003e \u003cp\u003eDue to multiple variable stratification, the results of the univariate analysis were not significant. Therefore, the variable stratification was adjusted and merged, and a Univariate logistic regression analysis was conducted to identify variables potentially associated with competency status after training. The results indicated that the following variables were significantly associated with higher odds of being competent(Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e): age group 40\u0026ndash;59 years compared to 20\u0026ndash;39 years (OR\u0026thinsp;=\u0026thinsp;3.35, 95% CI: [1.85\u0026ndash;6.08], p\u0026thinsp;\u0026lt;\u0026thinsp;0.001); Professional title gourp senior title (OR\u0026thinsp;=\u0026thinsp;6.73, 95% CI: [3.00\u0026ndash;15.10], p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), intermediate title (OR\u0026thinsp;=\u0026thinsp;2.23, 95% CI: [1.05\u0026ndash;4.73], p\u0026thinsp;=\u0026thinsp;0.037) compared to Junior title; working experience of \u0026ge;\u0026thinsp;10 years compared to \u0026lt;10 years (OR\u0026thinsp;=\u0026thinsp;5.29, 95% CI: [2.48\u0026ndash;11.29], p\u0026thinsp;\u0026lt;\u0026thinsp;0.001); monthly income group\u0026thinsp;\u0026gt;\u0026thinsp;5000 CNY compared to \u0026le;\u0026thinsp;5000 (OR\u0026thinsp;=\u0026thinsp;3.32, 95% CI: [1.79\u0026ndash;6.16], p\u0026thinsp;\u0026lt;\u0026thinsp;0.001); Hospital classification in tertiary hospitals compared to Secondary hospitals (OR\u0026thinsp;=\u0026thinsp;1.92, 95% CI: [1.08\u0026ndash;3.41], p\u0026thinsp;=\u0026thinsp;0.027), and having received training within the past six months (OR\u0026thinsp;=\u0026thinsp;2.23, 95% CI: [1.03\u0026ndash;4.83], p\u0026thinsp;=\u0026thinsp;0.041). Majors(\u003cem\u003ep\u0026thinsp;=\u0026thinsp;0.089\u003c/em\u003e) and Previous Training Level (\u003cem\u003ep\u0026thinsp;=\u0026thinsp;0.080\u003c/em\u003e) also were selected as candidates for inclusion in the subsequent multivariate logistic regression model. There were eight variables (Age, Major, Professional title, Working Experience(yrs), Hospital classification, Training Within Six Months, Previous Training Level and Income Level.\u003c/p\u003e \u003cp\u003eA stepwise logistic regression analysis was conducted to identify factors associated with post-training competency. After sequentially removing non-significant variables, the final model(step 6) retained three predictors: age, working years, and income. The results are shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe general characteristics of the 209 professionals\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTotal n(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD in Percentage\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCompetent(%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29(13.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e167.1\u0026thinsp;\u0026plusmn;\u0026thinsp;22.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e69.9\u0026thinsp;\u0026plusmn;\u0026thinsp;12.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e13(44.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e180(86.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e159.1\u0026thinsp;\u0026plusmn;\u0026thinsp;28.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e65.4\u0026thinsp;\u0026plusmn;\u0026thinsp;16.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e64(35.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20\u0026ndash;29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29(13.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e149.2\u0026thinsp;\u0026plusmn;\u0026thinsp;19.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e59.8\u0026thinsp;\u0026plusmn;\u0026thinsp;11.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3(10.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30\u0026ndash;39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e83(39.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e152.6\u0026thinsp;\u0026plusmn;\u0026thinsp;27.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e61.7\u0026thinsp;\u0026plusmn;\u0026thinsp;15.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e24(28.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40\u0026ndash;49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e60(28.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e161.8\u0026thinsp;\u0026plusmn;\u0026thinsp;29.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e67.0\u0026thinsp;\u0026plusmn;\u0026thinsp;16.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e23(38.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50\u0026ndash;59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37(17.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e183.1\u0026thinsp;\u0026plusmn;\u0026thinsp;20.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e79.0\u0026thinsp;\u0026plusmn;\u0026thinsp;11.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e27(73.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAcademic degree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDoctor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1(0.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e195.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e85.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1(100.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMaster\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13(6.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e172.2\u0026thinsp;\u0026plusmn;\u0026thinsp;2.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e72.9\u0026thinsp;\u0026plusmn;\u0026thinsp;1.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2(15.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBachelor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e163(78.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e159.8\u0026thinsp;\u0026plusmn;\u0026thinsp;28.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e65.8\u0026thinsp;\u0026plusmn;\u0026thinsp;16.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e63(38.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32(15.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e156.4\u0026thinsp;\u0026plusmn;\u0026thinsp;31.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e63.9\u0026thinsp;\u0026plusmn;\u0026thinsp;17.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e11(34.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMajor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eClinical medicine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50(23.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e165.8\u0026thinsp;\u0026plusmn;\u0026thinsp;22.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e69.2\u0026thinsp;\u0026plusmn;\u0026thinsp;12.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e19(38.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNursing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e116(55.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e160.9\u0026thinsp;\u0026plusmn;\u0026thinsp;28.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e66.4\u0026thinsp;\u0026plusmn;\u0026thinsp;16.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e47(40.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePublic health\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25(12.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e150.4\u0026thinsp;\u0026plusmn;\u0026thinsp;29.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e60.5\u0026thinsp;\u0026plusmn;\u0026thinsp;16.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6(24.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMedical laboratory science\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12(5.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e157.9\u0026thinsp;\u0026plusmn;\u0026thinsp;42.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e64.7\u0026thinsp;\u0026plusmn;\u0026thinsp;24.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5(41.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6(2.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e145.3\u0026thinsp;\u0026plusmn;\u0026thinsp;16.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e57.8\u0026thinsp;\u0026plusmn;\u0026thinsp;9.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0(0.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProfessional title\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSenior\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10(4.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e187.9\u0026thinsp;\u0026plusmn;\u0026thinsp;12.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e81.2\u0026thinsp;\u0026plusmn;\u0026thinsp;7.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8(80.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAssociate Senior\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47(22.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e176.4\u0026thinsp;\u0026plusmn;\u0026thinsp;22.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e75.2\u0026thinsp;\u0026plusmn;\u0026thinsp;12.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e27(57.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIntermediate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e84(40.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e156.3\u0026thinsp;\u0026plusmn;\u0026thinsp;28.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e63.9\u0026thinsp;\u0026plusmn;\u0026thinsp;16.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e29(34.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJunior\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e54(25.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e151.0\u0026thinsp;\u0026plusmn;\u0026thinsp;22.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e60.3\u0026thinsp;\u0026plusmn;\u0026thinsp;13.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e11(20.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14(6.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e148.0\u0026thinsp;\u0026plusmn;\u0026thinsp;34.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e59.1\u0026thinsp;\u0026plusmn;\u0026thinsp;19.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2(14.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eWorking Experience(yrs)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u0026ndash;4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e108(51.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e150.1\u0026thinsp;\u0026plusmn;\u0026thinsp;27.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e60.7\u0026thinsp;\u0026plusmn;\u0026thinsp;15.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e23(21.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5\u0026ndash;9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e65(31.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e164.4\u0026thinsp;\u0026plusmn;\u0026thinsp;23.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e68.4\u0026thinsp;\u0026plusmn;\u0026thinsp;13.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e29(44.66)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10\u0026ndash;14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23(11.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e170.8\u0026thinsp;\u0026plusmn;\u0026thinsp;24.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e72.0\u0026thinsp;\u0026plusmn;\u0026thinsp;14.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e12(52.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15\u0026ndash;19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7(3.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e198.6\u0026thinsp;\u0026plusmn;\u0026thinsp;14.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e87.8\u0026thinsp;\u0026plusmn;\u0026thinsp;8.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e7(100.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6(2.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e196.3\u0026thinsp;\u0026plusmn;\u0026thinsp;15.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e86.6\u0026thinsp;\u0026plusmn;\u0026thinsp;9.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6(100.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHospital classification\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSecondary hospitals\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e129(61.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e154.1\u0026thinsp;\u0026plusmn;\u0026thinsp;29.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e62.6\u0026thinsp;\u0026plusmn;\u0026thinsp;16.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e40(31.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTertiary hospitals\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e80(38.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e170.0\u0026thinsp;\u0026plusmn;\u0026thinsp;22.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e71.6\u0026thinsp;\u0026plusmn;\u0026thinsp;12.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e37(46.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTraining Within Six Months\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e166(79.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e163.6\u0026thinsp;\u0026plusmn;\u0026thinsp;27.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e67.9\u0026thinsp;\u0026plusmn;\u0026thinsp;15.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e67(40.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43(20.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e147.6\u0026thinsp;\u0026plusmn;\u0026thinsp;26.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e58.9\u0026thinsp;\u0026plusmn;\u0026thinsp;15.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e10(23.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePrevious Training Level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNational level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11(5.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e166.1\u0026thinsp;\u0026plusmn;\u0026thinsp;33.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e69.4\u0026thinsp;\u0026plusmn;\u0026thinsp;19.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e11(100.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProvincial level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100(47.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e161.5\u0026thinsp;\u0026plusmn;\u0026thinsp;30.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e65.6\u0026thinsp;\u0026plusmn;\u0026thinsp;16.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e40(40.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMunicipal level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e97(46.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e159.5\u0026thinsp;\u0026plusmn;\u0026thinsp;28.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e66.0\u0026thinsp;\u0026plusmn;\u0026thinsp;15.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e30(30.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIncome Level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;3000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12(5.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e158.8\u0026thinsp;\u0026plusmn;\u0026thinsp;24.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e65.2\u0026thinsp;\u0026plusmn;\u0026thinsp;13.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3(25.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3001\u0026ndash;5000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e79(37.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e148.8\u0026thinsp;\u0026plusmn;\u0026thinsp;27.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e59.5\u0026thinsp;\u0026plusmn;\u0026thinsp;15.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e17(21.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5001\u0026ndash;7000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e71(34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e163.6\u0026thinsp;\u0026plusmn;\u0026thinsp;25.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e68.0\u0026thinsp;\u0026plusmn;\u0026thinsp;14.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e29(40.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7001\u0026ndash;9000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32(15.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e175.3\u0026thinsp;\u0026plusmn;\u0026thinsp;29.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e74.6\u0026thinsp;\u0026plusmn;\u0026thinsp;16.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e20(62.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;9001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15(7.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e173.0\u0026thinsp;\u0026plusmn;\u0026thinsp;20.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e73.3\u0026thinsp;\u0026plusmn;\u0026thinsp;11.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8(53.3)\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 \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\u003eResults of Univariate Logistic Regression Analysis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" 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=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eS.E\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eWald\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003edf\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003eCI 95%[lower,upper]\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(Constant)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.387\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.405\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.915\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.339\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.679\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-0.387\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.405\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.209\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e16.218\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;0.05\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e3.349\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1.209\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.300\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAcademic degree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow education (Bachelor\u0026thinsp;+\u0026thinsp;Others)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHigh education (master's or above)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.808\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.668\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.462\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.227\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.446\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-0.808\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.668\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMajor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMedical related majors\u003c/p\u003e \u003cp\u003e(Clinical medicine\u0026thinsp;+\u0026thinsp;Nursing)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNon-medical related majors(Public health\u0026thinsp;+\u0026thinsp;Medical laboratory science\u0026thinsp;+\u0026thinsp;Others)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.652\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.384\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.889\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.089\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.521\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-0.652\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.384\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProfessional title\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJunior title (Junior and others)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIntermediate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.802\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.384\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4.357\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e2.231\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.802\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.384\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSenior title (Senior\u0026thinsp;+\u0026thinsp;Associate Senior)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.907\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.411\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e21.495\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;0.05\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e6.731\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1.907\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.411\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWorking Experience\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.666\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.398\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e17.512\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;0.05\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e5.288\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1.666\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.398\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHospital classification\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSecondary hospitals\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTertiary hospitals\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.649\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.294\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4.875\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.027\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.915\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.649\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.294\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTraining Within Six Months\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.803\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.394\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4.156\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.041\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e2.233\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.803\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.394\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrevious Training Level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMunicipal level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHigh training (National\u0026thinsp;+\u0026thinsp;Provincial)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.510\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.291\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.057\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.080\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.665\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.510\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.291\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIncome Level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;5000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026gt;5000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.199\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.313\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e14.669\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;0.05\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e3.317\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1.199\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.313\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \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\u003eResults of Multivariable Logistic Regression Analysis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" 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=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStep\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eS.E\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eWald\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003edf\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003eCI 95%\u003c/p\u003e \u003cp\u003e[lower, upper]\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eStep 1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e(Constant)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.563\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.414\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.847\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.174\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.756\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.780\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e3.957\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSecondary hospitals\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTertiary hospitals\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.535\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.379\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.994\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.158\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.708\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.812\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e3.589\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAcademic degree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow education (Bachelor\u0026thinsp;+\u0026thinsp;Others)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHigh education (master's or above)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.464\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.742\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.392\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.531\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.629\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.147\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e2.691\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMajor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMedical related majors\u003c/p\u003e \u003cp\u003e(Clinical medicine\u0026thinsp;+\u0026thinsp;Nursing)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNon-medical related majors(Public health\u0026thinsp;+\u0026thinsp;Medical laboratory science\u0026thinsp;+\u0026thinsp;Others)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.412\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.439\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.883\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.347\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.662\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.280\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e1.564\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProfessional title\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJunior title\u003c/p\u003e \u003cp\u003e(Junior and others)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIntermediate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.343\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.432\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.628\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.428\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.408\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.604\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e3.286\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSenior title\u003c/p\u003e \u003cp\u003e(Senior\u0026thinsp;+\u0026thinsp;Associate Senior)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.557\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.617\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.815\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.367\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.746\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.521\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e5.852\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWorking Experience\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.269\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.431\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8.666\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e3.556\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1.528\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e8.275\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTraining Within Six Months\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.249\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.451\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.305\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.581\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.283\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.530\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e3.103\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIncome Level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;5000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026gt;5000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.414\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.122\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.897\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.842\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e4.27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eStep 6\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e(Constant)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.892\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.321\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e7.717\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e2.441\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1.301\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e4.581\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWorking Experience\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.291\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.421\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e9.383\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e3.635\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1.592\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e8.302\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIncome Level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;5000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026gt;5000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.984\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.331\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8.827\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e2.674\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1.398\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e5.117\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\u003eThe model demonstrated a good fit based on the Hosmer and Lemeshow test (χ\u0026sup2; = 3.584, df\u0026thinsp;=\u0026thinsp;5, P\u0026thinsp;=\u0026thinsp;0.611), indicating no significant deviation between predicted and observed outcomes. Through the test for multicollinearity, it was found that the variance inflation factor (VIF) of each independent variable was all \u0026lt;10, indicating that there was no serious multicollinearity problem in the model and that the regression results were reliable. Regarding classification performance, the final model correctly identified 78.0% of participants who were not competent and 61.0% who were competent, resulting in an overall classification accuracy of 71.8%. All retained variables in the final model were statistically significant (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Their odds ratios suggested that older age (OR\u0026thinsp;=\u0026thinsp;2.441, 95% CI: 1.301\u0026ndash;4.581), longer working years (OR\u0026thinsp;=\u0026thinsp;3.635, 95% CI: 1.592\u0026ndash;8.302), and higher income (OR\u0026thinsp;=\u0026thinsp;2.674, 95% CI: 1.398\u0026ndash;5.117) were positively associated with competency. In the multivariate logistic regression analysis, after testing possible interaction terms, none of the interaction effects were statistically significant (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05), and the results were shown in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. Compared with models that included more variables, the final 3-variable model(Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e step 6) achieved similar classification accuracy and better interpretability, making it a practical and parsimonious choice for informing future training interventions.\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\u003eResults of Multivariable logistic regression (Including interaction terms.)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eS.E\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eWald\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003edf\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003eCI 95%[lower,upper]\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(Constant)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.366\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.546\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.258\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.012\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3.918\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.344\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e11.423\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWorking Experience\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.497\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.156\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.041\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.757\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e7.31\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIncome Level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;5000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026gt;5000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.079\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.294\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.942\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.393\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e22.045\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eWorking experience * age\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.161\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.263\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.261\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.313\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e2.372\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eIncome level * age\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.507\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.675\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.565\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.452\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.602\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e2.26\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eIncome level * Working experience\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.539\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.956\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.591\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4.659\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.715\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e30.352\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\u003eTo further evaluate the predictive ability of each variable in the competency model, ROC analysis was performed on age, working experience, and income level (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The results show that these three variables can distinguish competency status: the area under the curve (AUC) is 0.735 and has a specific discriminatory ability.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.4 HAI-PCP Low Capability Project and Training Needs Survey\u003c/h2\u003e \u003cp\u003eAnalysis of 44 competency self-assessment items (overall mean\u0026thinsp;=\u0026thinsp;3.64, IQR: Q1\u0026thinsp;=\u0026thinsp;3.47, Q3\u0026thinsp;=\u0026thinsp;3.77) identified low-competency priorities as those scoring below the 25th percentile (Q1\u0026thinsp;=\u0026thinsp;3.47). As shown in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, 12 critical items requiring intervention were identified, with mean scores ranging from 3.11 to 3.47, significantly lower than the median (Median\u0026thinsp;=\u0026thinsp;3.64). Single-sample Wilcoxon signed-rank tests confirmed systematic competency gaps for all low-scoring items (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003eSummarize the training needs survey form attached to the HAI-PCPs competency index questionnaire, extract HAI-PCPs training needs keywords, and summarize the content as shown in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eHAI-PCPs competency evaluation low-scoring items\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=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCode.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eItem\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eScore\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModify and incorporate existing standards, guidelines, regulations, literature, and publications into the infection prevention and control program and establish evidence-based infection prevention and control strategies and methods\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.45\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProvide recommendations to reduce infection risk during the design, construction, and remodeling of healthcare environments concerning building layout.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.38\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMaster the basic knowledge of medical microbiology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eParticipate in the management of clinical application of antimicrobial drugs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.29\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBe able to identify and develop a surveillance program for hospital-acquired infections and their associated risk factors by national infection control policy requirements.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.47\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUtilizing software to collect and process surveillance data and establish a systematic database.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.39\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAbility to assess the risk of healthcare-associated infectious diseases\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.46\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBased on the results of infection control evaluation, match quality improvement tools and carry out quality improvement activities.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.44\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eConcerned with information on unknown infectious diseases\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.39\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAbility to actively participate in academic research related to hospital infections and skillfully use tools to obtain cutting-edge information on sensory control\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.47\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAbility to think creatively\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.39\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eApply hospital infection research tools and results to clinical practice\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.35\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 \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\u003eSummary Table of HAI-PCPs Training Mode and Training Needs Survey\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=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eItem\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFrequency\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eProportion(%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTraining Duration\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u0026ndash;4 hours\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.274\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u0026ndash;8 hours\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.329\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u0026ndash;12 hours\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.238\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMore than 12 hours\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.159\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTraining Frequency\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOnce a month\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.095\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOnce a quarter\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.391\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOnce every six months\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.385\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOnce a year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTraining Mode\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCase analysis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.245\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSelf-study with assessment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.117\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOffline training\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eScenario/tabletop exercises\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.199\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOnline training\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.209\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTraining Needs\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBasic knowledge of HAI management\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.116\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eManagement of key departments\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.091\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePractical experience in higher-level hospitals\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.084\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRisk assessment and management practice\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.072\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMonitoring and management of multidrug resistance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.065\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTargeted monitoring\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.053\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiagnosis of HAI cases\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.053\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDevelopment of in-hospital training courses\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.047\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRational use of antibiotics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.041\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInterpretation of guidelines\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResearch\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.029\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKey points of HAI in primary hospitals\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.029\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKnowledge of pathogenic microorganisms\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.023\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eManagement of HAI outbreaks\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.023\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eManagement and communication skills\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.022\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFeedback and supervision in clinical HAI work\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePractice of clinical HAI supervision\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePersonal ability assessment and improvement\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrecision management of HAI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEnvironmental hygiene sampling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrevention and control key points in clinical departments\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSurgical site management\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHAI management in operating rooms\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eContinuous improvement and rectification of HAI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHand hygiene compliance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCase analysis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInfectious diseases\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLaws and regulations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEpidemiological investigation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEnvironmental cleaning and disinfection\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEmergency drills\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHAI elements in medical quality management\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTool usage in HAI work\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.005\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":"4. Discussion","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e4.1The Competency Score Situation of HAI-PCPs in Guizhou Province\u003c/h2\u003e \u003cp\u003eThrough the investigation of the research, it was found that only 36.8% were categorized as competent. This is similar to the result of the cross-sectional survey on HAI-PCPs also carried out in Guizhou Province (the competency rate was 38.3%)(\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e), indicating that under the background of this research. This indicates a generally low level of professional competence within the region and highlights a significant need for capacity building. The competence of Infection Prevention and Control Practitioners (IPCPs) in China is a critical factor in managing healthcare-associated infections (HAIs) and mitigating the spread of infectious diseases(\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). Low job competency will lead to the low-quality implementation of infection prevention and control work and result in the recurrence of hospital-acquired infection incidents(\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). Although China currently lacks a national competency standard for HAI-PCPs, structured evaluation tools allow for real-time assessment, early detection of competency gaps, and the development of more targeted training strategies.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e4.2 To identify factors associated with infectious disease emergency response competency\u003c/h2\u003e \u003cp\u003eThe three elements of epidemiology, time, space, and population, are related to the occurrence and development of diseases(\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). This concept still applies to infectious diseases and is the basis for handling incidents. For infectious disease emergency response competence, the occurrence and handling of an event require more assurance of the basic abilities of the personnel involved. The results of the univariate analysis in this study showed that age, professional title, working experience, income level, and recent participation in training within the past six months were identified as factors related to the competency rate (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Multivariate logistic regression further confirmed the independent associations of age, work experience, and income level with competency status, highlighting their potential as key predictors of emergency response capability among HAI-PCPs. Among these, factors such as age, professional title, working experience, and income level exhibit a certain degree of temporal correlation. A higher professional title often reflects greater expertise and professionalism, typically accompanied by more extensive working experience and corresponding increases in income level. As a general demographic characteristic, age broadly represents the accumulation of experience over time, further emphasizing its relevance to professional competence(\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). Although there is a correlation between these factors and competency, there may be an interaction between them. For example, a professional title may result from experience and age(\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). Although the independence and relative contribution of these variables can be confirmed through statistical analysis such as multiple regression or structural equation modeling, this is not one of the purposes of this study, and further research is expected to expand upon it. Additionally, recent participation in training within the past six months is understandably a significant factor influencing professional competence. Training provides a direct pathway for skill enhancement and professional development, leading to measurable improvements in competence(\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). This finding aligns with this study's objectives, as training's impact on competence will be further examined in subsequent phases. Educational background did not show a significant impact (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05), which may reflect certain deficiencies in the continuing education system in Guizhou Province. HAI PCPs have not received systematic IPC training, making converting educational advantages into practical abilities difficult. If a suitable improvement model is not established promptly, it may form a vicious cycle of \"low training \u0026rarr; low professional title \u0026rarr; low competence.\"\u003c/p\u003e \u003cp\u003eFew studies on competency are directly related to HAI-PCP. However, in the study of medical institution service personnel, work experience, education level, and work environment can significantly affect the workability of professionals, which has a specific reference value for our research(\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). Future research should explore potential mediating or moderating effects among key demographic and institutional variables to better understand how these factors influence HAI-PCPs' competency development over time.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Training Needs from HAI-PCPs.\u003c/h2\u003e \u003cp\u003eTraining needs assessment is a critical process for identifying gaps in skills and knowledge within various professional fields. It helps design effective training programs aligning with organizational goals and individual performance improvement. The Hennessy-Hicks Training Needs Analysis (TNA) questionnaire is a widely used tool endorsed by the World Health Organization for assessing training needs globally(\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). It has been adapted and translated into various countries, revealing training gaps and promoting continuous professional development across different disciplines and settings. The tool effectively prioritizes and allocates educational resources based on identified needs, addressing the \"know-do\" gap in global human resources for health(\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). This study adopts a similar research design approach to screen for training defects in low-scoring projects after competency evaluation and supplements the design concerning training needs to complete the development of the training module.\u003c/p\u003e \u003cp\u003eWhile evaluating the competence of professionals, we also surveyed their future training needs. Based on the summary of training needs assessments, the top ten prioritized topics identified were basic knowledge of healthcare-associated infection (HAI) management, management of key departments, practical experience in higher-level hospitals, risk assessment, and management practices, monitoring and management of multidrug resistance, targeted monitoring, diagnosis of HAI cases, development of in-hospital training courses, rational use of antibiotics, and interpretation of guidelines. These areas highlight the weak links and lack of key capabilities of HAI-PCPs in infection prevention and control practices: Knowledge-based, Skill-based, and Management-oriented needs. The relevant content is also included in the competency evaluation indicators, proving that the HAI-PCPs competency indicators developed for research and development are scientific and comprehensive. Training needs assessment is foundational in designing effective professional development programs across various sectors. By utilizing validated tools, organizations can better align training with strategic goals and address specific skill gaps. This ensures that workforce development keeps pace with evolving industry demands and technological advancements.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e4.4 limitation\u003c/h2\u003e \u003cp\u003eAlthough multiple abilities-related factors were identified, such as age, hospital level, professional title, work experience, etc., the study did not explore the possible interaction effects between these variables. For example, job titles may be influenced by both work experience and age, and these complex relationships have not been thoroughly analyzed, leading to limitations in factor association research. The lack of deeper statistical analysis on the independence or relative contribution of variables in the study (such as multiple regression or structural equation modeling) limits the understanding of causal relationships among influencing factors.\u003c/p\u003e \u003cp\u003eThe investigation of training needs is limited to areas related to low-scoring projects, which may have overlooked other potential but not yet recognized important training needs. The study did not provide a detailed explanation of the representativeness of the survey sample (such as regional distribution, differences in hospital levels, etc.), which may affect the generalizability and applicability of the results.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e4.5 Future research directions\u003c/h2\u003e \u003cp\u003eInteraction effect: This study did not explore the possible interaction effects between variables such as age, job title, and work experience. Future research can use advanced statistical techniques such as multiple regression or structural equation modeling to understand the complex relationships between these factors better or focus on studying the impact of interaction effects on abilities.\u003c/p\u003e \u003cp\u003eTraining needs: Although this study focuses on low-scoring areas, it may overlook other important training needs. A more comprehensive investigation can capture a broader range of abilities that require attention, thereby gaining a more comprehensive understanding of training requirements.\u003c/p\u003e \u003cp\u003eSampling representativeness: This study did not provide a detailed explanation of the representativeness of the survey sample, such as regional distribution and hospital level. Future research should focus on more representative sampling to improve the generalizability and reliability of the results.\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThis study revealed that only 36.8% of HAI-PCPs in Guizhou Province were competent in infectious disease emergency response, indicating a substantial gap in preparedness among the workforce. Key demographic and professional factors\u0026mdash;including age, working experience, and income level\u0026mdash;were significantly associated with competency status. Recent training was also a strong predictor of professional competence, underscoring the importance of continuous professional development. The study also identified prioritized training needs in knowledge-based, skill-based, and management-oriented domains, which can serve as a foundation for developing targeted training programs. These findings not only offer a valuable reference for workforce development in Guizhou Province but also have broader applicability to other resource-limited settings aiming to strengthen infection control capacity. Future research should explore the interaction effects between influencing factors using more advanced statistical techniques, such as structural equation modeling, and expand the scope of training needs assessment. Ensuring the representativeness of survey samples will also enhance the generalizability and reliability of future findings.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMJ.Z. and SW.W. contributed to the study design, implementation of the survey, data collection, and interpretation of the results. M.I.I. and SS.MN. provided methodological guidance and assisted with data analysis. WMZ., as the corresponding author, supervised the study, provided critical feedback, and ensured the accuracy and integrity of the final submission. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research received no external funding.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was conducted in accordance with the principles of the Declaration of Helsinki. The ethics committee with the code has approved this study: USM-JEPeM Code: USM/JEPeM/KK/23050390. Guizhou Traditional Chinese Medicine University Ethical review approval code: KS2023150. Informed consent was obtained from all participants prior to their inclusion in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003cbr\u003e\u0026nbsp;Not applicable.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to thank all healthcare-associated infection prevention and control practitioners who participated in this study for their valuable time and contributions.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eMalheiro R, Gomes AA, Fernandes C, Fareleira A, Lebre A, Pascoalinho D, et al. Hospital Context Determinants of Variability in Healthcare-Associated Infection Prevalence: Multi-Level Analysis. Microorganisms. 2024 Dec 7;12(12):2522. https://doi.org/10.3390/microorganisms12122522 PMID: 39770725\u003c/li\u003e\n\u003cli\u003eKhavandegar A, Siami Z, Rasouli A, Nazemi P, Gull A. Impact of healthcare-associated infections on in-hospital outcomes during the COVID-19 era: a multicenter comparative study of 20,942 isolated microorganisms from ICU patients. Front Public Health. 2025;13:1475221. https://doi.org/10.3389/fpubh.2025.1475221 PMID: 39991697\u003c/li\u003e\n\u003cli\u003eBangani O, English R, Dramowski A. Intensive care unit nurses\u0026rsquo; knowledge, attitudes and practices of COVID-19 infection prevention and control. S Afr J Infect Dis. 2023;38(1):478. https://doi.org/10.4102/sajid.v38i1.478 PMID: 37435115\u003c/li\u003e\n\u003cli\u003eVerberk JDM, van der Kooi TII, Kampstra NA, Reimes N, van Rooden SM, Hopmans TEM, et al. Healthcare-associated infections in Dutch hospitals during the COVID-19 pandemic. Antimicrob Resist Infect Control. 2023 Jan 5;12(1):2. https://doi.org/10.1186/s13756-022-01201-z PMID: 36604755\u003c/li\u003e\n\u003cli\u003eSonpar A, Hundal CO, Tott\u0026eacute; JEE, Wang J, Klein SD, Twyman A, et al. Multimodal strategies for the implementation of infection prevention and control interventions-update of a systematic review for the WHO guidelines on core components of infection prevention and control programmes at the facility level. Clin Microbiol Infect. 2025 Jun;31(6):948\u0026ndash;57. https://doi.org/10.1016/j.cmi.2025.01.011 PMID: 39863071\u003c/li\u003e\n\u003cli\u003eZhou Z, Zhu C. Relative Spatial Poverty Within Guizhou Province, A Multidimensional Approach. Soc Indic Res. 2022 May 1;161(1):151\u0026ndash;70. https://doi.org/10.1007/s11205-021-02825-1\u003c/li\u003e\n\u003cli\u003eCourvoisier DS, Combescure C, Agoritsas T, Gayet-Ageron A, Perneger TV. Performance of logistic regression modeling: beyond the number of events per variable, the role of data structure. Journal of Clinical Epidemiology. 2011 Sep 1;64(9):993\u0026ndash;1000. https://doi.org/10.1016/j.jclinepi.2010.11.012\u003c/li\u003e\n\u003cli\u003eYao Y, Zha Z, Huang B, Jing Z, Wang L, Wu Q, et al. Factors associated with core competencies of infection prevention and control practitioners in 511 hospitals: A large cross-sectional survey in Guizhou in southwest China. Journal of Hospital Infection [Internet]. 2025 Feb 14 [cited 2025 Apr 19];0(0). https://doi.org/10.1016/j.jhin.2025.02.004 PMID: 39961511\u003c/li\u003e\n\u003cli\u003eLiu H, Fei C, Zhang X, Yang L, Ji X, Zeng Q, et al. What we learned from the infection control and what we need in the future: A quantitative and qualitative study on hospital infection prevention and control practitioners (HIPCPs) in Tianjin, China. Am J Infect Control. 2024 Sep;52(9):1073\u0026ndash;83. https://doi.org/10.1016/j.ajic.2024.05.004 PMID: 38740285\u003c/li\u003e\n\u003cli\u003eHouben F, den Heijer CD, Dukers-Muijrers NH, Nava J-CB, Theunissen M, van Eck B, et al. Self-reported compliance with infection prevention and control of healthcare workers in Dutch residential care facilities for people with intellectual and developmental disabilities during the COVID-19 pandemic: A cross-sectional study. Disabil Health J. 2023 Oct 11;101542. https://doi.org/10.1016/j.dhjo.2023.101542 PMID: 39492010\u003c/li\u003e\n\u003cli\u003eAhlbom A. Epidemiology is about disease in populations. Eur J Epidemiol. 2020 Dec 1;35(12):1111\u0026ndash;3. https://doi.org/10.1007/s10654-020-00701-9\u003c/li\u003e\n\u003cli\u003eTer Maten-Speksnijder A, Grypdonck M, Pool A, Meurs P, Van Staa A. Learning to attain an advanced level of professional responsibility. Nurse Educ Today. 2015 Aug;35(8):954\u0026ndash;9. https://doi.org/10.1016/j.nedt.2015.03.005 PMID: 25825354\u003c/li\u003e\n\u003cli\u003eHerman S, Gish M, Rosenblum R, Herman M. Effects of RN Age and Experience on Transformational Leadership Practices. J Nurs Adm. 2017 Jun;47(6):327\u0026ndash;37. https://doi.org/10.1097/NNA.0000000000000488 PMID: 28509720\u003c/li\u003e\n\u003cli\u003eAl-Omary H, Soltani A, Stewart D, Nazar Z. Implementing learning into practice from continuous professional development activities: a scoping review of health professionals\u0026rsquo; views and experiences. BMC Med Educ. 2024 Sep 20;24(1):1031. https://doi.org/10.1186/s12909-024-06016-7 PMID: 39304841\u003c/li\u003e\n\u003cli\u003eRizany I, Hariyati RTS, Handayani H. Factors that affect the development of nurses\u0026rsquo; competencies: a systematic review. Enfermer\u0026iacute;a Cl\u0026iacute;nica. 2018 Feb 1;28:154\u0026ndash;7. https://doi.org/10.1016/S1130-8621(18)30057-3\u003c/li\u003e\n\u003cli\u003eMarkaki A, Malhotra S, Billings R, Theus L. Training needs assessment: tool utilization and global impact. BMC Medical Education. 2021 May 31;21(1):310. https://doi.org/10.1186/s12909-021-02748-y\u003c/li\u003e\n\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-health-services-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bhsr","sideBox":"Learn more about [BMC Health Services Research](http://bmchealthservres.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/BHSR/default.aspx","title":"BMC Health Services Research","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Healthcare-Associated Infection Prevention and Control Practitioners, Infectious Disease Emergency Response, Competency Assessment, Training Needs, Cross-Sectional Study","lastPublishedDoi":"10.21203/rs.3.rs-6745432/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6745432/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCompetency in infectious disease emergency response among Healthcare-Associated Infection Prevention and Control Practitioners (HAI-PCPs) is critical for effective outbreak management and infection control within healthcare settings. However, evidence regarding the current competency levels of HAI-PCPs and the factors influencing their emergency response capabilities remains limited in Guizhou Province, China.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eObjective:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study aimed to assess the competency of HAI-PCPs in infectious disease emergency response in Guizhou Province, identify demographic and professional factors associated with competency, and explore their training needs to inform targeted capacity-building strategies.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA cross-sectional survey was conducted among HAI-PCPs across various healthcare institutions in Guizhou Province. Competency was evaluated using a previously developed and validated structured questionnaire. Descriptive statistics summarized competency levels, while univariate and multivariate logistic regression analyses identified factors independently associated with competency status. Training needs were assessed based on identified competency gaps and participants’ self-reported educational demands.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe overall competency rate in infectious disease emergency response was 36.8%, indicating substantial room for improvement. Multivariate analysis revealed that age, work experience, income level, and recent participation in infection control training were significantly associated with competency (p \u0026lt; 0.05). The identified training needs and low-performing competency areas highlight key training and capacity development targets.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe competency of HAI-PCPs in infectious disease emergency response in Guizhou Province is currently suboptimal. There is a pressing need for systematic, competency-based training programs tailored to address specific skill gaps among practitioners. Promoting a model based on competency evaluation and training needs assessment can enhance training depth, optimize content comprehension, and improve training effectiveness.\u003c/p\u003e","manuscriptTitle":"Competency in Infectious Disease Emergency Response among Healthcare-Associated Infection Control Practitioners in Guizhou Province, China: A Cross-Sectional Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-01 06:29:14","doi":"10.21203/rs.3.rs-6745432/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2025-07-03T14:53:25+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"311716503640767497818444907166101180813","date":"2025-06-26T14:34:37+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"312406078329398927413414002453164015373","date":"2025-06-24T13:24:32+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-06-24T05:17:12+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-06-18T09:46:44+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-06-02T09:05:40+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-05-30T17:22:10+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Health Services Research","date":"2025-05-30T17:19:02+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-health-services-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bhsr","sideBox":"Learn more about [BMC Health Services Research](http://bmchealthservres.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/BHSR/default.aspx","title":"BMC Health Services Research","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b41498fc-e6e2-43ac-9379-c67e80ac3c17","owner":[],"postedDate":"July 1st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-07-01T06:29:14+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-01 06:29:14","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6745432","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6745432","identity":"rs-6745432","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}