Cardiopulmonary Resuscitation Competency and Implementation Barriers among Security Guards in Urban China: A Mixed-Methods Study

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Despite their formal designation as frontline responders, the CPR competency of security guards remains largely unknown. This study assessed their CPR knowledge, attitudes, behaviors, self-efficacy, and skills, and explored barriers to implementation. Methods A convergent mixed-methods design was employed across six public settings in Shanghai (2020–2022). Quantitative data (n = 1,001) were analyzed using descriptive statistics, regression, and latent profile analysis (LPA). Qualitative interviews (n = 15) explored implementation barriers. The Pillar Integration Process (PIP) synthesized findings. Results Only 5.8% of guards demonstrated guideline-aligned CPR performance. Knowledge (mean: 35.1%), self-efficacy (55.0%), and behavioral scores (54.7%) were suboptimal. LPA identified three subgroups: low (77.0%), moderate (19.6%), and high behavior (3.4%). Low behavior was predicted by subway worksite (OR = 5.47), low self-efficacy (OR = 1.29), and lack of recent training (OR = 0.28). Thematic analysis revealed multilevel barriers: individual (skill decay), organizational (role ambiguity), societal (legal fears), and victim-level (hesitation toward gender/age). Integration via the Pillar Integration Process yielded a comprehensive explanatory framework (Fig. 2) capturing the dynamic interplay between psychological readiness, contextual barriers, and CPR behavior. Conclusions CPR competency gaps among security guards are critical yet modifiable. Targeted retraining, legal clarification, and institutional mandates are urgently needed to support China’s bystander response system and the Healthy China 2030 agenda. Cardiopulmonary resuscitation Out-of-hospital cardiac arrest Mixed-methods research Latent profile analysis First responders Security guards Health systems Public health policy Figures Figure 1 Figure 2 Introduction Out-of-hospital cardiac arrest (OHCA) represents a critical global health challenge, with survival rates markedly stratified by economic context—averaging 20.4% in high-income countries versus just 3.1% in low- and middle-income settings [ 1 , 2 ]. China, in particular, faces acute disparities: with an OHCA incidence of 97.1 per 100,000 person-years, survival remains as low as 1.2%, significantly trailing nations such as Norway (47.9) and the United States (62.2) [ 1 , 3 ]. Bystander cardiopulmonary resuscitation (B-CPR)—a key determinant of survival—has a proven potential to double survival odds [ 4 ]. Yet in China, it is performed in only 20.3% of OHCA cases, well below the international range of 19.1–79.0% [ 1 , 3 ]. Bridging this gap necessitates scalable, competency-based training aligned with international standards, such as those from the American Heart Association (AHA) and the International Liaison Committee on Resuscitation (ILCOR) [ 1 ]. Security guards are formally designated in China as occupational first responders [ 5 ], frequently deployed in high-risk environments—transport hubs, hospitals, residential complexes—where they may be first on scene during OHCA events [ 6 ]. Internationally, their capacity has been demonstrated: in U.S. casinos, trained security staff achieved 53% survival in witnessed arrests, vastly outperforming lay responders [ 7 ]. However, in China, fewer than 10% of guards meet AHA-standard CPR proficiency [ 8 ], with particularly low rates for critical skills such as adequate compression depth (15.5%) and correct compression-to-ventilation ratio (9.2%) [ 8 ]. Contributing factors include fragmented training infrastructure, minimal hands-on practice, unclear institutional mandates, and legal uncertainties [ 8 – 10 ]. Nearly 20% of guards report never having received CPR training [ 8 ]. Compounding this, the COVID-19 pandemic expanded their duties—e.g., crowd management and screening—without corresponding investment in emergency response readiness [14,15]. To investigate these systemic challenges, this study draws upon two behavioral frameworks: the Knowledge–Attitude–Practice (KAP) model [ 11 ] and the Intention-Focused Model [ 12 ]. While the KAP model assumes linear translation from knowledge to action, it fails to explain the widely observed “intention–behavior” gap. The Intention-Focused Model, in contrast, considers psychological mediators (e.g., confidence, feasibility) as distinct and essential stages linking intention to actual CPR behavior [ 13 , 14 ]. This study builds upon prior components—a cross-sectional survey of CPR-related KAP, and a qualitative study on B-CPR barriers among security guards in Shanghai [ 14 ]. Extending that foundation, the present study contributes: (1) identification of behavioral subgroups via latent profile analysis (LPA); and (2) integration of findings via the Pillar Integration Process (PIP) to generate a multilevel explanatory framework. No data or interpretations from prior work are duplicated. Specifically, this study aims to: (1) assess CPR-related knowledge, attitudes, behaviors, self-efficacy, and practical skills; (2) identify latent behavioral typologies; and (3) explore barriers across individual, organizational, societal, and contextual domains. Ultimately, findings aim to inform national policy reforms aligned with the Healthy China 2030 blueprint [ 16 ], including standardized CPR training protocols, broader dissemination of Article 184 of the Civil Code, and scalable retraining models such as virtual simulation. To enhance explanatory clarity, we also present a multilevel framework (Fig. 2 ) derived from mixed-methods integration to illustrate the intersecting pathways that shape CPR readiness and response. Methods Study design and setting This research employed a convergent mixed-methods design, conducted between December 2020 and August 2022 across six high-risk public settings in Shanghai: hospitals, airports, subway stations, supermarkets, office buildings, and residential compounds. Quantitative and qualitative data were collected concurrently, analyzed independently, and subsequently integrated using the Pillar Integration Process (PIP). The present study builds upon two previously published investigations: a cross-sectional survey assessing CPR-related knowledge, attitudes, and behaviors, and a qualitative study exploring implementation barriers among security guards in Shanghai [ 14 ]. While those studies provided foundational insights, this research introduces novel analytical advancements by (1) identifying behavioral subgroups through latent profile analysis (LPA), and (2) synthesizing findings through PIP to construct a multilevel explanatory framework. All data, analyses, and interpretations presented here are original and non-duplicative. The study was approved by the Institutional Review Board of Shanghai First People’s Hospital (Approval No.: 2021KY013). All participants provided informed consent prior to participation. Identifying information was anonymized, and all audio recordings were deleted within 48 hours of transcription to ensure confidentiality. Clinical trial number: not applicable. Quantitative Methods Participants and Sampling A total of 1,020 security guards were recruited through stratified convenience sampling by workplace type. After removing incomplete responses, 1,001 valid questionnaires were retained (response rate: 98.1%). Eligibility criteria included: (1) age 18–65 years; (2) current employment aligned with China’s National Occupational Skill Standards [ 19 ]; and (3) ability to independently complete assessments. Individuals with hearing, cognitive, or communication impairments were excluded. The required sample size was calculated using the formula: n= \(\:\frac{{\text{z}}^{2}\times\:\text{p}\times\:(1-\text{p})}{{\text{d}}^{2}}\) [ 20 ]. with Z = 1.96, p = 0.5, and d = 0.05, yielding a minimum of 385. To ensure power, 1,020 participants were recruited. Data Collection From December 2020 to February 2022, data were collected using both paper-based and online questionnaires (via Wenjuanxing). Quality control measures included IP filtering and logic validation. CPR skills were evaluated using Laerdal Little Anne manikins, following 2020 AHA guidelines [ 21 ]. Each participant performed a 2-minute simulation, independently scored by two certified AHA instructors, with strong inter-rater reliability (Cohen’s κ = 0.81). Measurements CPR Knowledge, Attitude, and Behavior Questionnaire : Adapted from Tao et al. [ 23 ], revised per 2020 AHA guidelines. The instrument’s validity and reliability were confirmed via pilot testing (n = 50) and expert review (CVI = 0.99; Cronbach’s α = 0.85). It included: (1) Knowledge: 13 binary items (score 0–13; ≥8 defined as competent); (2) Attitude: 5 Likert-scale items (score range: 5–25); (3) Behavior: 8 Likert-scale items (score range: 8–40). Total scores (maximum 78) were categorized as low ( 80%) [ 24 ]. Self-Efficacy Scale : Adapted from Yu et al. [ 25 ], with 4 items scored on a 3-point Likert scale (range: 4–12). Categories: high (≥ 9), moderate (6–8), low (< 6). The scale demonstrated good reliability (Cronbach’s α = 0.82) and validity (CVI = 0.94). CPR Skills Evaluation A 24-item binary checklist, developed according to AHA 2020 standards [ 21 ]. Participants were required to meet a minimum of 14 correct items (60%) and pass all critical actions (e.g., compression depth and rate) to qualify as competent. Inter-rater agreement was robust (Cohen’s κ = 0.81) [ 22 ]. Data Analysis Statistical analysis was performed in SPSS 21.0 and Mplus 8.3. Multivariate regression examined predictors of knowledge, attitudes, behaviors, skills, and self-efficacy. Latent profile analysis (LPA) was applied to identify CPR behavior subgroups. Model fit was assessed using Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), and entropy values (> 0.80) [ 26 ]. Predictors of profile membership were analyzed using binary logistic regression (p < 0.05) [ 27 ]. Qualitative Methods Sampling A purposive subsample of 15 participants was selected from the quantitative cohort to reflect workplace heterogeneity and training experience. Sampling continued until thematic saturation was reached. Data collection Semi-structured interviews (30–60 minutes) were conducted by trained researchers using an interview guide developed from the Intention-Focused Model [ 12 ]. Topics included perceived CPR roles, confidence levels, organizational protocol awareness, and legal anxieties. Interviews were audio-recorded and transcribed verbatim. Data Analysis A hybrid deductive–inductive thematic approach was employed [ 28 ], combining deductive coding aligned to the Intention-Focused Model’s three phases—intention formation, skill acquisition, and contextual feasibility—with inductive coding to capture emergent themes. Coding was performed in NVivo 12.0 by two researchers with inter-coder consensus validation. Mixed-Methods Integration Findings from quantitative and qualitative strands were integrated using the Pillar Integration Process (PIP) [ 29 ]. Through listing, matching, checking, and pillar-building, joint displays were developed to visualize converging and complementary themes [ 30 , 31 ], culminating in the development of an integrated multilevel explanatory framework that captures the dynamic interplay of facilitators and barriers across individual, organizational, and societal levels. Results 1. Quantitative Findings Participant Demographics Of the 1,020 security guards recruited, 1,001 provided valid responses (response rate: 98.1%). The sample was predominantly male (95.5%) with a mean age of 42.3 ± 11.2 years. Over half (55.2%) had a junior high school education or below, and only 42.5% had ever received CPR training. Notably, 60.6% had not been trained in ≥ 3 years. Detailed demographics are presented in Table 1 . Table 1 Participant Demographics (N = 1,001) Category Subgroup Numb (n) Freq (%) Category Subgroup Numb (n) Freq (%) Gender Male 956 95.5 Job 10 years 160 16.0 50–59 years 358 35.8 Very poor 21 2.1 60–65 years 61 6.1 Training History No 576 57.5 Educational Junior high school or below 553 55.2 Yes 425 42.5 Level High school 308 30.8 Training 0 times 576 57.5 Junior college 104 10.4 Frequency (times) 1 time 271 27.1 Bachelor's degree or above 36 3.6 2 times 91 9.1 Monthly ≤ 2999 yuan 8 0.8 ≥ 3 times 63 6.3 Household 3000–4999 yuan 497 49.7 Last Training 7000 yuan 26 2.6 1–2 years 38 3.8 Workplace residential living 185 18.5 2–3 years 167 16.7 hospital 195 19.5 > 3 years or no training 607 60.6 airport 138 13.8 CPR Skills No 721 72.0 supermarket 188 18.8 Competency Yes 280 28.07 office building 198 19.8 Exposure to No 925 92.4 Subway 97 9.7 Cardiac Arrest Yes 76 7.6 Official Junior 891 89.0 Prior CPR No 977 97.6 Title Intermediate 79 7.9 Implementation Yes 24 2.4 Senior 20 2.0 Experience expert 11 1.1 Sum 1001 100.0 Notes : a 7.3 RMB ≈ 1$ CPR Competency CPR knowledge (35.1%), behavioral performance (54.7%), and self-efficacy (55.0%) were all suboptimal. Only 5.8% of participants demonstrated guideline-compliant CPR skills. Overall CPR readiness was characterized by a low total score (M = 45.29 ± 5.32; score rate = 58.1%). Regression and Latent Profile Analysis Multivariate linear regression identified advanced age (β = −0.51, p < 0.001), outdated or absent CPR training (β = −1.61, p < 0.001), and low self-efficacy (β = −1.75, p < 0.001) as independent predictors of poor overall CPR competency (Supplementary Table S1 ). Latent Profile Analysis (LPA) classified participants into three behavioral subgroups: low-behavior (77.0%), moderate-behavior (19.6%), and high-behavior (3.4%), with model fit indices indicating good classification (AIC = 12,543; BIC = 12,710; entropy = 0.86) (Fig. 1 ; Supplementary Tables S2–S3). Significant differences ( p < 0.05) across behavior groups were observed by age, education level, work setting, training frequency, and self-efficacy (Supplementary Table S4). Binary logistic regression further indicated that working in subway environments (OR = 5.47; 95% CI: 3.09–9.69), low self-efficacy (OR = 1.29; 95% CI: 1.17–1.43), and lack of recent CPR training (OR = 0.28; 95% CI: 0.23–0.34) were strong predictors of membership in the low-behavior group (Table 2 ). Table 2 Logistic Regression Analysis of Low-Behavior Group Membership (N = 1,001) Variable β SE Wald χ² P-value OR 95% CI Workplace (Subway) 1.699 0.292 33.963 < 0.001 5.471 3.089–9.690 Job experience (< 1years) 0.333 0.079 17.789 < 0.001 1.395 1.195–1.628 Low Knowledge Level 0.294 0.138 4.513 0.03 1.341 1.023–1.759 Low Self-Efficacy 0.257 0.05 24.91 3years or untrained) -1.286 0.105 148.688 < 0.001 0.276 0.225–0.340 Training Frequency -0.621 0.181 11.752 < 0.001 0.538 0.377–0.767 CPR Skill Competency -0.852 0.295 8.35 < 0.001 0.427 0.240–0.760 Note: P < 0.05, P < 0.01 2. Qualitative Findings Participant Profile and Themes Interviews were conducted with 15 security guards (86.7% male; mean age = 44.0 years), selected to ensure workplace diversity and variation in CPR exposure. Four major themes emerged: Individual-level barriers Widespread lack of CPR confidence and operational uncertainty were cited as major barriers to action. Victim-level concerns Fear of causing secondary harm, especially to elderly or opposite-gender victims, and uncertainty about medical history discouraged intervention. Organizational constraints Role ambiguity, limited retraining, and protocols that deprioritized CPR were frequently reported. Social/legal factors Negative media reports and insufficient legal protection (e.g., unclear "Good Samaritan" policies) further suppressed CPR engagement. Participants emphasized the need for frequent, simulation-based training, integrated psychological support, and institutional incentives such as certification or promotion linkage. 3. Mixed-Methods Integration Findings from the quantitative and qualitative strands were synthesized using the Pillar Integration Process (PIP), resulting in a cohesive, multilevel explanatory framework (Fig. 2 ). This integration enabled the identification of convergence, expansion, and complementarity across data sources: Convergence Quantitative evidence of low CPR self-efficacy and poor operational skills was corroborated by qualitative accounts describing psychological hesitancy, lack of confidence, and fear of performing CPR incorrectly. Expansion Qualitative narratives revealed barriers not captured in the structured survey instruments, including dependence on environmental reassurance (e.g., presence of surveillance cameras), cultural discomfort associated with age and gender of victims, and legal anxieties stemming from ambiguous interpretation of Article 184 of China’s Civil Code. Holistic Framework The resulting conceptual model demonstrates how individual-level factors (e.g., skill decay, emotional unreadiness), organizational-level constraints (e.g., role ambiguity, procedural rigidity), and sociocultural or legal concerns converge to reinforce the “intention–action gap” in B-CPR execution among trained personnel. Figure 2 . Multilevel Determinants of Bystander CPR Implementation Among Security Guards in Urban China: A Mixed-Methods Integration Framework This diagram visualizes the complex interplay of personal, organizational, societal, and victim-related barriers that suppress CPR engagement in OHCA settings, even among trained security staff. To further elaborate the integrated insights, Table 3 presents a joint display matrix summarizing aligned findings across both strands, organized by pillar themes and supported with illustrative quotes. Discussion Building on a Programmatic Line of Inquiry This study builds on our earlier work investigating CPR readiness among security personnel in Shanghai, which included a cross-sectional survey of knowledge, attitudes, and behaviors, and a qualitative analysis of implementation barriers [ 14 ]. While those studies offered foundational insights, the current research represents a significant methodological and conceptual expansion by (1) identifying latent behavioral profiles through Latent Profile Analysis (LPA), and (2) integrating findings through the Pillar Integration Process (PIP) to construct a multilevel explanatory framework. Together, these advances demonstrate the cumulative progression from isolated findings toward a cohesive understanding of CPR-related behavior and systemic barriers. Summary of Findings and Multilevel Insights Despite 42.5% of participants reporting prior CPR training, actual proficiency remained alarmingly low. Only 5.8% demonstrated guideline-compliant skills, and the mean knowledge score was 35.1%. Performance in core areas—chest compressions (0.9%) and rescue breathing (2.8%)—was especially poor, reflecting a training paradigm overly focused on theory. These outcomes are consistent with findings in other resource-limited or occupational first-aid settings [ 8 , 9 , 32 ]. Alarmingly, over 60% had not been trained in the past three years, exceeding the empirically established window for skill decay [ 33 ]. Comparative data from countries with standardized training mandates (e.g., the U.S. & Australia) demonstrate higher bystander CPR rates (68–79.3%) and improved survival outcomes [ 34 , 35 ]. For instance, structured programs for U.S. casino security personnel achieved a 53% survival rate for witnessed cardiac arrests [ 7 ]. In contrast, China’s fragmented training system and lack of accreditation contribute to suboptimal outcomes, underscoring the urgency for policy reforms aligned with international standards. Multivariate analysis highlighted modifiable predictors of CPR readiness, including age, training recency, and self-efficacy. Notably, guards in our sample scored low in CPR self-efficacy (mean = 55.0%), which has been identified in other studies as a strong predictor of bystander action [ 36 ]. The LPA further classified guards into three distinct groups—Low-Behavior (77.0%), Moderate-Behavior (19.6%), and High-Behavior (3.4%)—with key predictors of low-behavior status including subway workplace (OR = 5.47), outdated or absent training (OR = 0.28), and low self-efficacy (OR = 1.29). These results are consistent with international evidence from countries like Saudi Arabia and India, where training gaps were linked to poor security guard CPR response [ 8 , 9 ]. Qualitative data added crucial interpretive depth by highlighting the "intention–action gap." Even guards with basic CPR knowledge expressed hesitation due to concerns about legal consequences, lack of confidence, limited practice, and gender-related discomfort. Many cited ambiguity around Article 184 of China’s Civil Code as a deterrent—an issue also observed internationally in contexts where CPR performance carries legal ambiguity or cultural sensitivities, particularly with female or elderly victims [ 37 , 38 ]. Integrated Interpretation through Mixed Methods The Pillar Integration Process enabled systematic alignment of quantitative and qualitative findings [ 29 ]. Converging evidence underscored the pivotal role of self-efficacy and recent training, while qualitative expansions surfaced latent psychosocial dynamics such as fear of legal reprisal, emotional trauma from failed attempts, and dependence on environmental cues (e.g., surveillance cameras) for decision-making confidence. The resulting multilevel framework (Fig. 2 ) illustrates how CPR behavior is suppressed not by any single factor, but by the interaction of individual doubts, organizational ambiguity, and societal norms. Implications for Training, Policy, and Institutional Design We propose a three-pronged strategy for addressing these systemic barriers: Targeted retraining for vulnerable groups (e.g., older, under-trained guards) using simplified CPR-AED curricula, supported by immersive modalities such as virtual reality (VR) and incentives like certification-linked bonuses [ 39 , 40 ]. Legal reform and communication , including broader dissemination and clarification of Article 184’s Good Samaritan protections, ideally through real-case scenarios to alleviate fears of post-rescue liability [ 38 , 41 ]. Institutional integration , by embedding CPR roles within formal job descriptions, placing AEDs in high-traffic areas, and standardizing emergency response timelines (e.g., “3-minute zones”), as demonstrated effectively in cities like Chicago [ 7 ]. These recommendations not only reflect best practices internationally but also resonate with China’s strategic goals under the Healthy China 2030 blueprint. Limitations and Future Directions Several limitations should be noted. First, this study focused exclusively on Shanghai; generalizability to other regions in China may be limited. Future studies should adopt a multi-site approach, encompassing diverse urban and rural settings. Second, the cross-sectional nature of the design does not capture longitudinal changes in CPR competency or the effectiveness of retraining over time. Third, while training content was explored, the study did not experimentally evaluate emerging educational innovations such as real-time feedback systems, mobile app-based modules, or AI-enhanced instruction [ 39 ]. These merit future investigation through experimental or implementation science approaches. Conclusion Security guards, due to their constant presence in public spaces, are uniquely positioned to act as immediate responders during OHCA events. However, current CPR readiness among this group is undermined by insufficient training, weak institutional support, and legal uncertainty. This mixed-methods study provides integrative evidence linking training recency, self-efficacy, and organizational clarity to CPR behavior. By embedding CPR into the institutional fabric—via frequent hands-on training, legal safeguards, and role-based expectations—China can substantially strengthen its bystander response infrastructure. These improvements are not aspirational—they are both feasible and essential for achieving the national targets set forth in Healthy China 2030. Abbreviations AHA American Heart Association ABIC Adjusted Bayesian Information Criterion AIC Akaike Information Criterion AED Automated External Defibrillator B-CPR Bystander cardiopulmonary resuscitation BIC Bayesian Information Criterion BLRT Bootstrap Likelihood Ratio Test BLS Basic Life Support CPR Cardiopulmonary resuscitation CVI Content Validity Index EMS Emergency medical service KAP Knowledge-Attitude-Belief-Practice ILCOR International Liaison committee on resuscitation LMRT Lo-Mendel-Rubin Adjusted Likelihood Ratio Test LPA Latent Profile Analysis OHCA Out-of-Hospital Cardiac Arrest VR Virtual Reality Declarations Acknowledgments The authors sincerely thank all participating security guards for their time and cooperation. Special appreciation is extended to the research assistants whose dedication during data collection and transcription significantly contributed to the study’s quality. The authors also wish to acknowledge Dr. Getachew Kassa for his expert methodological input and insightful feedback, which greatly enhanced the conceptual rigor of this work. The authors are also grateful to institutional colleagues and field collaborators who supported the study design and implementation phases. Authors’ Contributions MZY conceptualized the study, led data collection and analysis, secured funding, and was solely responsible for drafting and revising the manuscript. SC and YQP coordinated data collection logistics, supervised field procedures, and contributed to data quality control. All authors reviewed and approved the final version of the manuscript. Funding This study was funded by: The Science and Technology Project of Songjiang District, Shanghai (No. 20SJKJGG373). The 2024 Overseas Visiting and Advanced Training Program for Young and Middle-aged Teachers in Shanghai Universities, sponsored by the Shanghai Municipal Education Commission. Funding Role Statement : The funding agencies had no role in the design of the study; data collection, analysis, or interpretation; decision to submit the article for publication; or preparation of the manuscript. Data Availability The datasets generated and analyzed during the current study are not publicly available due to privacy constraints, but are available from the corresponding author upon reasonable request, pending appropriate ethical review. Ethics Approval and Consent to Participate The study received ethical approval from the Institutional Review Board of Shanghai General Hospital (Approval No.: 2021KY013). All participants provided written informed consent before participation. All procedures were carried out in accordance with institutional guidelines and the ethical principles outlined in the Declaration of Helsinki. Consent for Publication Not applicable. Competing Interests The authors declare that they have no competing interests. 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Supplementary Files TableS1TableS48.2.docx Supplementary Information Supplementary Tables (S1–S4) are available in the supplementary files accompanying this article. These tables provide detailed quantitative results, model fit indices, subgroup comparisons, and coding frameworks referenced in the main text to enhance transparency and reproducibility. Table.doc Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 27 Oct, 2025 Reviewers agreed at journal 24 Oct, 2025 Reviewers invited by journal 10 Oct, 2025 Editor invited by journal 16 Sep, 2025 Editor assigned by journal 04 Aug, 2025 Submission checks completed at journal 04 Aug, 2025 First submitted to journal 11 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. 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04:18:10","extension":"html","order_by":33,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":170167,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-6641847/v1/6f7a2797c9ca3bcbee84a90f.html"},{"id":94622668,"identity":"9709ce5a-57de-4b16-bf40-0ae2bdbb3179","added_by":"auto","created_at":"2025-10-29 04:18:27","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":97155,"visible":true,"origin":"","legend":"\u003cp\u003eLatent Class-Specific CPR Behavior Profiles (Visualization of low-, moderate-, and high-behaviour groups based on 8 CPR indicators)\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6641847/v1/d256889ead9dcbcbef6eafdb.png"},{"id":94622118,"identity":"acd449bf-028f-461e-a675-64b769929928","added_by":"auto","created_at":"2025-10-29 04:17:55","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":64046,"visible":true,"origin":"","legend":"\u003cp\u003eMultilevel Determinants of Bystander CPR Implementation Among Security Guards in Urban China: A Mixed-Methods Integration Framework\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNotes:\u003c/em\u003e \u003cem\u003eThis integrative framework, derived via the Pillar Integration Process (PIP), illustrates how CPR implementation is constrained by multiple, interacting levels of influence. Key barriers include low self-efficacy, limited operational skills, insufficient retraining, legal ambiguity, and unclear workplace role expectations. These multilevel constraints reinforce the “intention–action gap” in emergency scenarios. The model emphasizes that improving bystander CPR requires coordinated reforms across individual, organizational, and societal systems.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6641847/v1/37fe60a2ec4bdc915e9878f9.png"},{"id":94641081,"identity":"5252cdd3-b7fc-4862-8130-9446e7bfe88e","added_by":"auto","created_at":"2025-10-29 07:51:05","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1267811,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6641847/v1/2dc51498-a0eb-4269-a865-393e89082c8f.pdf"},{"id":94622237,"identity":"e17a5a73-4fe2-43b3-907d-2f68d0f40e78","added_by":"auto","created_at":"2025-10-29 04:18:12","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":33417,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSupplementary Tables (S1–S4) are available in the supplementary files accompanying this article. These tables provide detailed quantitative results, model fit indices, subgroup comparisons, and coding frameworks referenced in the main text to enhance transparency and reproducibility.\u003c/p\u003e","description":"","filename":"TableS1TableS48.2.docx","url":"https://assets-eu.researchsquare.com/files/rs-6641847/v1/e0d7db1fe3f8f621141dded4.docx"},{"id":94622176,"identity":"787f4b09-b4cc-460d-ac8a-776bcbe35c4b","added_by":"auto","created_at":"2025-10-29 04:18:07","extension":"doc","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":311296,"visible":true,"origin":"","legend":"","description":"","filename":"Table.doc","url":"https://assets-eu.researchsquare.com/files/rs-6641847/v1/a47aed1164f53488b3fe584c.doc"}],"financialInterests":"No competing interests reported.","formattedTitle":"Cardiopulmonary Resuscitation Competency and Implementation Barriers among Security Guards in Urban China: A Mixed-Methods Study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eOut-of-hospital cardiac arrest (OHCA) represents a critical global health challenge, with survival rates markedly stratified by economic context—averaging 20.4% in high-income countries versus just 3.1% in low- and middle-income settings [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. China, in particular, faces acute disparities: with an OHCA incidence of 97.1 per 100,000 person-years, survival remains as low as 1.2%, significantly trailing nations such as Norway (47.9) and the United States (62.2) [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eBystander cardiopulmonary resuscitation (B-CPR)—a key determinant of survival—has a proven potential to double survival odds [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Yet in China, it is performed in only 20.3% of OHCA cases, well below the international range of 19.1–79.0% [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Bridging this gap necessitates scalable, competency-based training aligned with international standards, such as those from the American Heart Association (AHA) and the International Liaison Committee on Resuscitation (ILCOR) [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eSecurity guards are formally designated in China as occupational first responders [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], frequently deployed in high-risk environments—transport hubs, hospitals, residential complexes—where they may be first on scene during OHCA events [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Internationally, their capacity has been demonstrated: in U.S. casinos, trained security staff achieved 53% survival in witnessed arrests, vastly outperforming lay responders [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. However, in China, fewer than 10% of guards meet AHA-standard CPR proficiency [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e8\u003c/span\u003e], with particularly low rates for critical skills such as adequate compression depth (15.5%) and correct compression-to-ventilation ratio (9.2%) [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eContributing factors include fragmented training infrastructure, minimal hands-on practice, unclear institutional mandates, and legal uncertainties [\u003cspan additionalcitationids=\"CR9\" citationid=\"CR9\" class=\"CitationRef\"\u003e8\u003c/span\u003e–\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Nearly 20% of guards report never having received CPR training [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Compounding this, the COVID-19 pandemic expanded their duties—e.g., crowd management and screening—without corresponding investment in emergency response readiness [14,15].\u003c/p\u003e\u003cp\u003eTo investigate these systemic challenges, this study draws upon two behavioral frameworks: the Knowledge–Attitude–Practice (KAP) model [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e11\u003c/span\u003e] and the Intention-Focused Model [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. While the KAP model assumes linear translation from knowledge to action, it fails to explain the widely observed “intention–behavior” gap. The Intention-Focused Model, in contrast, considers psychological mediators (e.g., confidence, feasibility) as distinct and essential stages linking intention to actual CPR behavior [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThis study builds upon prior components—a cross-sectional survey of CPR-related KAP, and a qualitative study on B-CPR barriers among security guards in Shanghai [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Extending that foundation, the present study contributes: (1) identification of behavioral subgroups via latent profile analysis (LPA); and (2) integration of findings via the Pillar Integration Process (PIP) to generate a multilevel explanatory framework. No data or interpretations from prior work are duplicated.\u003c/p\u003e\u003cp\u003eSpecifically, this study aims to: (1) assess CPR-related knowledge, attitudes, behaviors, self-efficacy, and practical skills; (2) identify latent behavioral typologies; and (3) explore barriers across individual, organizational, societal, and contextual domains.\u003c/p\u003e\u003cp\u003eUltimately, findings aim to inform national policy reforms aligned with the \u003cem\u003eHealthy China 2030\u003c/em\u003e blueprint [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], including standardized CPR training protocols, broader dissemination of Article 184 of the Civil Code, and scalable retraining models such as virtual simulation. To enhance explanatory clarity, we also present a multilevel framework (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) derived from mixed-methods integration to illustrate the intersecting pathways that shape CPR readiness and response.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cb\u003eStudy design and setting\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThis research employed a convergent mixed-methods design, conducted between December 2020 and August 2022 across six high-risk public settings in Shanghai: hospitals, airports, subway stations, supermarkets, office buildings, and residential compounds. Quantitative and qualitative data were collected concurrently, analyzed independently, and subsequently integrated using the Pillar Integration Process (PIP).\u003c/p\u003e\u003cp\u003eThe present study builds upon two previously published investigations: a cross-sectional survey assessing CPR-related knowledge, attitudes, and behaviors, and a qualitative study exploring implementation barriers among security guards in Shanghai [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. While those studies provided foundational insights, this research introduces novel analytical advancements by (1) identifying behavioral subgroups through latent profile analysis (LPA), and (2) synthesizing findings through PIP to construct a multilevel explanatory framework. All data, analyses, and interpretations presented here are original and non-duplicative.\u003c/p\u003e\u003cp\u003e The study was approved by the Institutional Review Board of Shanghai First People’s Hospital (Approval No.: 2021KY013). All participants provided informed consent prior to participation. Identifying information was anonymized, and all audio recordings were deleted within 48 hours of transcription to ensure confidentiality.\u003c/p\u003e\u003cp\u003e\u003cb\u003eClinical trial number: not applicable.\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eQuantitative Methods\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eParticipants and Sampling\u003c/b\u003e\u003c/p\u003e\u003cp\u003eA total of 1,020 security guards were recruited through stratified convenience sampling by workplace type. After removing incomplete responses, 1,001 valid questionnaires were retained (response rate: 98.1%). Eligibility criteria included: (1) age 18–65 years; (2) current employment aligned with China’s National Occupational Skill Standards [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e19\u003c/span\u003e]; and (3) ability to independently complete assessments. Individuals with hearing, cognitive, or communication impairments were excluded.\u003c/p\u003e\u003cp\u003eThe required sample size was calculated using the formula: n= \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{{\\text{z}}^{2}\\times\\:\\text{p}\\times\\:(1-\\text{p})}{{\\text{d}}^{2}}\\)\u003c/span\u003e\u003c/span\u003e [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. with Z = 1.96, p = 0.5, and d = 0.05, yielding a minimum of 385. To ensure power, 1,020 participants were recruited.\u003c/p\u003e\u003cp\u003e\u003cb\u003eData Collection\u003c/b\u003e\u003c/p\u003e\u003cp\u003eFrom December 2020 to February 2022, data were collected using both paper-based and online questionnaires (via Wenjuanxing). Quality control measures included IP filtering and logic validation. CPR skills were evaluated using Laerdal Little Anne manikins, following 2020 AHA guidelines [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Each participant performed a 2-minute simulation, independently scored by two certified AHA instructors, with strong inter-rater reliability (Cohen’s κ = 0.81).\u003c/p\u003e\u003cp\u003e\u003cb\u003eMeasurements\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eCPR Knowledge, Attitude, and Behavior Questionnaire\u003c/b\u003e: Adapted from Tao et al. [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e23\u003c/span\u003e], revised per 2020 AHA guidelines. The instrument’s validity and reliability were confirmed via pilot testing (n = 50) and expert review (CVI = 0.99; Cronbach’s α = 0.85). It included: (1) Knowledge: 13 binary items (score 0–13; ≥8 defined as competent); (2) Attitude: 5 Likert-scale items (score range: 5–25); (3) Behavior: 8 Likert-scale items (score range: 8–40). Total scores (maximum 78) were categorized as low (\u0026lt; 60%), moderate (60–80%), or high (\u0026gt; 80%) [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003cb\u003eSelf-Efficacy Scale\u003c/b\u003e: Adapted from Yu et al. [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e25\u003c/span\u003e], with 4 items scored on a 3-point Likert scale (range: 4–12). Categories: high (≥ 9), moderate (6–8), low (\u0026lt; 6). The scale demonstrated good reliability (Cronbach’s α = 0.82) and validity (CVI = 0.94).\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eCPR Skills Evaluation\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eA 24-item binary checklist, developed according to AHA 2020 standards [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Participants were required to meet a minimum of 14 correct items (60%) and pass all critical actions (e.g., compression depth and rate) to qualify as competent. Inter-rater agreement was robust (Cohen’s κ = 0.81) [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e\u003ch2\u003eData Analysis\u003c/h2\u003e\u003cp\u003eStatistical analysis was performed in SPSS 21.0 and Mplus 8.3. Multivariate regression examined predictors of knowledge, attitudes, behaviors, skills, and self-efficacy. Latent profile analysis (LPA) was applied to identify CPR behavior subgroups. Model fit was assessed using Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), and entropy values (\u0026gt; 0.80) [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Predictors of profile membership were analyzed using binary logistic regression (p \u0026lt; 0.05) [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003cb\u003eQualitative Methods\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eSampling\u003c/b\u003e\u003c/p\u003e\u003cp\u003eA purposive subsample of 15 participants was selected from the quantitative cohort to reflect workplace heterogeneity and training experience. Sampling continued until thematic saturation was reached.\u003c/p\u003e\u003cp\u003e\u003cb\u003eData collection\u003c/b\u003e\u003c/p\u003e\u003cp\u003eSemi-structured interviews (30–60 minutes) were conducted by trained researchers using an interview guide developed from the Intention-Focused Model [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Topics included perceived CPR roles, confidence levels, organizational protocol awareness, and legal anxieties. Interviews were audio-recorded and transcribed verbatim.\u003c/p\u003e\u003ch2\u003eData Analysis\u003c/h2\u003e\u003cp\u003eA hybrid deductive–inductive thematic approach was employed [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e28\u003c/span\u003e], combining deductive coding aligned to the Intention-Focused Model’s three phases—intention formation, skill acquisition, and contextual feasibility—with inductive coding to capture emergent themes. Coding was performed in NVivo 12.0 by two researchers with inter-coder consensus validation.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMixed-Methods Integration\u003c/b\u003e\u003c/p\u003e\u003cp\u003eFindings from quantitative and qualitative strands were integrated using the Pillar Integration Process (PIP) [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Through listing, matching, checking, and pillar-building, joint displays were developed to visualize converging and complementary themes [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e31\u003c/span\u003e], culminating in the development of an integrated multilevel explanatory framework that captures the dynamic interplay of facilitators and barriers across individual, organizational, and societal levels.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cspan\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1. Quantitative Findings\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eParticipant Demographics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOf the 1,020 security guards recruited, 1,001 provided valid responses (response rate: 98.1%). The sample was predominantly male (95.5%) with a mean age of 42.3\u0026thinsp;\u0026plusmn;\u0026thinsp;11.2 years. Over half (55.2%) had a junior high school education or below, and only 42.5% had ever received CPR training. Notably, 60.6% had not been trained in \u0026ge;\u0026thinsp;3 years. Detailed demographics are presented in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eParticipant Demographics (N\u0026thinsp;=\u0026thinsp;1,001)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCategory\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSubgroup\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNumb (n)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFreq (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCategory\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSubgroup\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNumb (n)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFreq\u003c/p\u003e\n \u003cp\u003e(%)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e956\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e95.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eJob\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;1 year\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e146\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eExperience\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u0026ndash;2 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e241\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e24.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18\u0026ndash;29 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u0026ndash;4 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e228\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30\u0026ndash;39 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e218\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u0026ndash;10 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e226\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRange\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40\u0026ndash;49 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e286\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e28.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;10 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e160\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50\u0026ndash;59 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e358\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e35.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVery poor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60\u0026ndash;65 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTraining History\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e576\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e57.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEducational\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eJunior high school or below\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e553\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e55.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e425\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e42.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLevel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e308\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e30.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTraining\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 times\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e576\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e57.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eJunior college\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFrequency (times)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 time\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e271\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e27.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBachelor\u0026apos;s degree or above\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2 times\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMonthly\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026le;\u0026thinsp;2999 yuan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ge;\u0026thinsp;3 times\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHousehold\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3000\u0026ndash;4999 yuan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e497\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e49.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLast Training\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;1/2 year\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIncome\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5000\u0026ndash;6999 yuan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e470\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e47.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTime (years ago)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1/2\u0026thinsp;\u0026minus;\u0026thinsp;1 year\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e129\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(RMB a)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;7000 yuan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u0026ndash;2 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWorkplace\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eresidential living\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e185\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e18.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u0026ndash;3 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e167\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehospital\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e195\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;3 years or no training\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e607\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e60.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eairport\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e138\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCPR Skills\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e721\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e72.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003esupermarket\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e188\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e18.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCompetency\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e280\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e28.07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eoffice building\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e198\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eExposure to\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e925\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e92.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSubway\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCardiac Arrest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOfficial\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eJunior\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e891\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e89.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePrior CPR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e977\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e97.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTitle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIntermediate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eImplementation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSenior\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eExperience\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eexpert\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e100.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\"\u003e\u003cem\u003eNotes\u003c/em\u003e: \u003csup\u003ea\u003c/sup\u003e7.3 RMB\u0026thinsp;\u0026asymp;\u0026thinsp;1$\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eCPR Competency\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCPR knowledge (35.1%), behavioral performance (54.7%), and self-efficacy (55.0%) were all suboptimal. Only 5.8% of participants demonstrated guideline-compliant CPR skills. Overall CPR readiness was characterized by a low total score (M\u0026thinsp;=\u0026thinsp;45.29\u0026thinsp;\u0026plusmn;\u0026thinsp;5.32; score rate\u0026thinsp;=\u0026thinsp;58.1%).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRegression and Latent Profile Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMultivariate linear regression identified advanced age (\u0026beta; = \u0026minus;0.51, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), outdated or absent CPR training (\u0026beta; = \u0026minus;1.61, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and low self-efficacy (\u0026beta; = \u0026minus;1.75, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) as independent predictors of poor overall CPR competency (Supplementary Table \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eLatent Profile Analysis (LPA) classified participants into three behavioral subgroups: low-behavior (77.0%), moderate-behavior (19.6%), and high-behavior (3.4%), with model fit indices indicating good classification (AIC\u0026thinsp;=\u0026thinsp;12,543; BIC\u0026thinsp;=\u0026thinsp;12,710; entropy\u0026thinsp;=\u0026thinsp;0.86) (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e; Supplementary Tables S2\u0026ndash;S3).\u003c/p\u003e\n\u003cp\u003eSignificant differences (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) across behavior groups were observed by age, education level, work setting, training frequency, and self-efficacy (Supplementary Table S4). Binary logistic regression further indicated that working in subway environments (OR\u0026thinsp;=\u0026thinsp;5.47; 95% CI: 3.09\u0026ndash;9.69), low self-efficacy (OR\u0026thinsp;=\u0026thinsp;1.29; 95% CI: 1.17\u0026ndash;1.43), and lack of recent CPR training (OR\u0026thinsp;=\u0026thinsp;0.28; 95% CI: 0.23\u0026ndash;0.34) were strong predictors of membership in the low-behavior group (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eLogistic Regression Analysis of Low-Behavior Group Membership (N\u0026thinsp;=\u0026thinsp;1,001)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u0026beta;\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSE\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eWald \u0026chi;\u0026sup2;\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOR\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWorkplace (Subway)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.699\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.292\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e33.963\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.471\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.089\u0026ndash;9.690\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eJob experience (\u0026lt;\u0026thinsp;1years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.333\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.079\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17.789\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.395\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.195\u0026ndash;1.628\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLow Knowledge Level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.294\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.138\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.513\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.341\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.023\u0026ndash;1.759\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLow Self-Efficacy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.257\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e24.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.293\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.169\u0026ndash;1.430\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLast Training Time (\u0026gt;\u0026thinsp;3years or untrained)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-1.286\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.105\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e148.688\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.276\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.225\u0026ndash;0.340\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTraining Frequency\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.621\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.181\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11.752\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.538\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.377\u0026ndash;0.767\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCPR Skill Competency\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.852\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.295\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.427\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.240\u0026ndash;0.760\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNote: \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cspan\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2. Qualitative Findings\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eParticipant Profile and Themes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInterviews were conducted with 15 security guards (86.7% male; mean age\u0026thinsp;=\u0026thinsp;44.0 years), selected to ensure workplace diversity and variation in CPR exposure. Four major themes emerged:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIndividual-level barriers\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWidespread lack of CPR confidence and operational uncertainty were cited as major barriers to action.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eVictim-level concerns\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFear of causing secondary harm, especially to elderly or opposite-gender victims, and uncertainty about medical history discouraged intervention.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOrganizational constraints\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRole ambiguity, limited retraining, and protocols that deprioritized CPR were frequently reported.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSocial/legal factors\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNegative media reports and insufficient legal protection (e.g., unclear \u0026quot;Good Samaritan\u0026quot; policies) further suppressed CPR engagement.\u003c/p\u003e\n\u003cp\u003eParticipants emphasized the need for frequent, simulation-based training, integrated psychological support, and institutional incentives such as certification or promotion linkage.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3. Mixed-Methods Integration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFindings from the quantitative and qualitative strands were synthesized using the Pillar Integration Process (PIP), resulting in a cohesive, multilevel explanatory framework (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). This integration enabled the identification of convergence, expansion, and complementarity across data sources:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConvergence\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eQuantitative evidence of low CPR self-efficacy and poor operational skills was corroborated by qualitative accounts describing psychological hesitancy, lack of confidence, and fear of performing CPR incorrectly.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eExpansion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eQualitative narratives revealed barriers not captured in the structured survey instruments, including dependence on environmental reassurance (e.g., presence of surveillance cameras), cultural discomfort associated with age and gender of victims, and legal anxieties stemming from ambiguous interpretation of Article 184 of China\u0026rsquo;s Civil Code.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHolistic Framework\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe resulting conceptual model demonstrates how individual-level factors (e.g., skill decay, emotional unreadiness), organizational-level constraints (e.g., role ambiguity, procedural rigidity), and sociocultural or legal concerns converge to reinforce the \u0026ldquo;intention\u0026ndash;action gap\u0026rdquo; in B-CPR execution among trained personnel.\u003c/p\u003e\n\u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e. \u003cem\u003eMultilevel Determinants of Bystander CPR Implementation Among Security Guards in Urban China: A Mixed-Methods Integration Framework\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThis diagram visualizes the complex interplay of personal, organizational, societal, and victim-related barriers that suppress CPR engagement in OHCA settings, even among trained security staff.\u003c/p\u003e\n\u003cp\u003eTo further elaborate the integrated insights, \u003cstrong\u003eTable\u0026nbsp;3\u003c/strong\u003e presents a joint display matrix summarizing aligned findings across both strands, organized by pillar themes and supported with illustrative quotes.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003e\u003cb\u003eBuilding on a Programmatic Line of Inquiry\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThis study builds on our earlier work investigating CPR readiness among security personnel in Shanghai, which included a cross-sectional survey of knowledge, attitudes, and behaviors, and a qualitative analysis of implementation barriers [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. While those studies offered foundational insights, the current research represents a significant methodological and conceptual expansion by (1) identifying latent behavioral profiles through Latent Profile Analysis (LPA), and (2) integrating findings through the Pillar Integration Process (PIP) to construct a multilevel explanatory framework. Together, these advances demonstrate the cumulative progression from isolated findings toward a cohesive understanding of CPR-related behavior and systemic barriers.\u003c/p\u003e\u003cp\u003e\u003cb\u003eSummary of Findings and Multilevel Insights\u003c/b\u003e\u003c/p\u003e\u003cp\u003eDespite 42.5% of participants reporting prior CPR training, actual proficiency remained alarmingly low. Only 5.8% demonstrated guideline-compliant skills, and the mean knowledge score was 35.1%. Performance in core areas\u0026mdash;chest compressions (0.9%) and rescue breathing (2.8%)\u0026mdash;was especially poor, reflecting a training paradigm overly focused on theory. These outcomes are consistent with findings in other resource-limited or occupational first-aid settings [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Alarmingly, over 60% had not been trained in the past three years, exceeding the empirically established window for skill decay [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Comparative data from countries with standardized training mandates (e.g., the U.S. \u0026amp; Australia) demonstrate higher bystander CPR rates (68\u0026ndash;79.3%) and improved survival outcomes [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. For instance, structured programs for U.S. casino security personnel achieved a 53% survival rate for witnessed cardiac arrests [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. In contrast, China\u0026rsquo;s fragmented training system and lack of accreditation contribute to suboptimal outcomes, underscoring the urgency for policy reforms aligned with international standards.\u003c/p\u003e\u003cp\u003eMultivariate analysis highlighted modifiable predictors of CPR readiness, including age, training recency, and self-efficacy. Notably, guards in our sample scored low in CPR self-efficacy (mean\u0026thinsp;=\u0026thinsp;55.0%), which has been identified in other studies as a strong predictor of bystander action [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. The LPA further classified guards into three distinct groups\u0026mdash;Low-Behavior (77.0%), Moderate-Behavior (19.6%), and High-Behavior (3.4%)\u0026mdash;with key predictors of low-behavior status including subway workplace (OR\u0026thinsp;=\u0026thinsp;5.47), outdated or absent training (OR\u0026thinsp;=\u0026thinsp;0.28), and low self-efficacy (OR\u0026thinsp;=\u0026thinsp;1.29). These results are consistent with international evidence from countries like Saudi Arabia and India, where training gaps were linked to poor security guard CPR response [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eQualitative data added crucial interpretive depth by highlighting the \"intention\u0026ndash;action gap.\" Even guards with basic CPR knowledge expressed hesitation due to concerns about legal consequences, lack of confidence, limited practice, and gender-related discomfort. Many cited ambiguity around Article 184 of China\u0026rsquo;s Civil Code as a deterrent\u0026mdash;an issue also observed internationally in contexts where CPR performance carries legal ambiguity or cultural sensitivities, particularly with female or elderly victims [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e38\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003cb\u003eIntegrated Interpretation through Mixed Methods\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe Pillar Integration Process enabled systematic alignment of quantitative and qualitative findings [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Converging evidence underscored the pivotal role of self-efficacy and recent training, while qualitative expansions surfaced latent psychosocial dynamics such as fear of legal reprisal, emotional trauma from failed attempts, and dependence on environmental cues (e.g., surveillance cameras) for decision-making confidence. The resulting multilevel framework (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) illustrates how CPR behavior is suppressed not by any single factor, but by the interaction of individual doubts, organizational ambiguity, and societal norms.\u003c/p\u003e\u003cp\u003e\u003cb\u003eImplications for Training, Policy, and Institutional Design\u003c/b\u003e\u003c/p\u003e\u003cp\u003eWe propose a three-pronged strategy for addressing these systemic barriers:\u003c/p\u003e\u003cp\u003e\u003cb\u003eTargeted retraining\u003c/b\u003e for vulnerable groups (e.g., older, under-trained guards) using simplified CPR-AED curricula, supported by immersive modalities such as virtual reality (VR) and incentives like certification-linked bonuses [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e40\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003cb\u003eLegal reform and communication\u003c/b\u003e, including broader dissemination and clarification of Article 184\u0026rsquo;s Good Samaritan protections, ideally through real-case scenarios to alleviate fears of post-rescue liability [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e41\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003cb\u003eInstitutional integration\u003c/b\u003e, by embedding CPR roles within formal job descriptions, placing AEDs in high-traffic areas, and standardizing emergency response timelines (e.g., \u0026ldquo;3-minute zones\u0026rdquo;), as demonstrated effectively in cities like Chicago [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThese recommendations not only reflect best practices internationally but also resonate with China\u0026rsquo;s strategic goals under the Healthy China 2030 blueprint.\u003c/p\u003e\u003cp\u003e\u003cb\u003eLimitations and Future Directions\u003c/b\u003e\u003c/p\u003e\u003cp\u003eSeveral limitations should be noted. First, this study focused exclusively on Shanghai; generalizability to other regions in China may be limited. Future studies should adopt a multi-site approach, encompassing diverse urban and rural settings. Second, the cross-sectional nature of the design does not capture longitudinal changes in CPR competency or the effectiveness of retraining over time. Third, while training content was explored, the study did not experimentally evaluate emerging educational innovations such as real-time feedback systems, mobile app-based modules, or AI-enhanced instruction [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. These merit future investigation through experimental or implementation science approaches.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eSecurity guards, due to their constant presence in public spaces, are uniquely positioned to act as immediate responders during OHCA events. However, current CPR readiness among this group is undermined by insufficient training, weak institutional support, and legal uncertainty. This mixed-methods study provides integrative evidence linking training recency, self-efficacy, and organizational clarity to CPR behavior. By embedding CPR into the institutional fabric\u0026mdash;via frequent hands-on training, legal safeguards, and role-based expectations\u0026mdash;China can substantially strengthen its bystander response infrastructure. These improvements are not aspirational\u0026mdash;they are both feasible and essential for achieving the national targets set forth in Healthy China 2030.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eAHA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eAmerican Heart Association\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eABIC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eAdjusted Bayesian Information Criterion\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eAIC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eAkaike Information Criterion\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eAED\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eAutomated External Defibrillator\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eB-CPR\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eBystander cardiopulmonary resuscitation\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eBIC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eBayesian Information Criterion\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eBLRT\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eBootstrap Likelihood Ratio Test\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eBLS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eBasic Life Support\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCPR\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eCardiopulmonary resuscitation\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCVI\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eContent Validity Index\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eEMS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eEmergency medical service\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eKAP\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eKnowledge-Attitude-Belief-Practice\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eILCOR\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eInternational Liaison committee on resuscitation\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eLMRT\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eLo-Mendel-Rubin Adjusted Likelihood Ratio Test\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eLPA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eLatent Profile Analysis\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eOHCA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eOut-of-Hospital Cardiac Arrest\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eVR\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eVirtual Reality\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors sincerely thank all participating security guards for their time and cooperation. Special appreciation is extended to the research assistants whose dedication during data collection and transcription significantly contributed to the study’s quality. The authors also wish to acknowledge Dr. Getachew Kassa for his expert methodological input and insightful feedback, which greatly enhanced the conceptual rigor of this work.\u003cbr\u003e\u0026nbsp;The authors are also grateful to institutional colleagues and field collaborators who supported the study design and implementation phases.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors’ Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMZY conceptualized the study, led data collection and analysis, secured funding, and was solely responsible for drafting and revising the manuscript. SC and YQP coordinated data collection logistics, supervised field procedures, and contributed to data quality control. All authors reviewed and approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was funded by: The Science and Technology Project of Songjiang District, Shanghai (No. 20SJKJGG373). The 2024 Overseas Visiting and Advanced Training Program for Young and Middle-aged Teachers in Shanghai Universities, sponsored by the Shanghai Municipal Education Commission.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding Role Statement\u003c/strong\u003e:\u003cbr\u003e\u0026nbsp;The funding agencies had no role in the design of the study; data collection, analysis, or interpretation; decision to submit the article for publication; or preparation of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and analyzed during the current study are not publicly available due to privacy constraints, but are available from the corresponding author upon reasonable request, pending appropriate ethical review.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Approval and Consent to Participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study received ethical approval from the Institutional Review Board of Shanghai General Hospital (Approval No.: 2021KY013). All participants provided written informed consent before participation. All procedures were carried out in accordance with institutional guidelines and the ethical principles outlined in the Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for Publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eKiguchi T, Okubo M, Nishiyama C, et al. Out-of-hospital cardiac arrest across the world: first report from the International Liaison Committee on Resuscitation (ILCOR). 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New York: Wiley; 1977. https://doi.org/10.1002/97804 70314756\u003c/li\u003e\n \u003cli\u003eMerchant RM, Topjian AA, et al, Adult Basic and Advanced Life Support, Pediatric Basic and Advanced Life Support, Neonatal Life Support, Resuscitation Education Science, and Systems of Care Writing Groups. Part 1: Executive Summary: 2020 American Heart Association Guidelines for Cardiopulmonary Resuscitation and Emergency Cardiovascular Care. Circulation. 2020 Oct 20;142(16_suppl_2): S337-S357. doi: 10.1161/CIR.0000000000000918.\u003c/li\u003e\n \u003cli\u003eLandis JR, Koch GG. The measurement of observer agreement for categorical data. Biometrics. 1977 Mar;33(1):159-74. https://doi.org/10.2307/2529310\u003c/li\u003e\n \u003cli\u003eTao Pingyue. Analysis of influencing factors and intervention strategies of knowledge, attitude and practice of cardiopulmonary resuscitation in primary caregivers of PCI patients[D]. Guangxi: Guang xi Medical University,2018.\u003c/li\u003e\n \u003cli\u003eXiang GP. A study on the effect of health education on KAP of safe medication for community-dwelling elderly with chronic diseases. Master Thesis. Changsha: Central South University; 2012.\u003c/li\u003e\n \u003cli\u003eYu Wenzhen, Gong Lin, Zhang Yingying. Investigation of Knowledge, Attitudes and Self-Efficacy of Cardiopulmonary Resuscitation in the General Population. China Medical Herald.2015;12(35):39-43.\u003c/li\u003e\n \u003cli\u003eNylund-Gibson K, Choi AY. Ten frequently asked questions about latent class analysis. Transl Issues Psychol Sci. 2018;4(4):440\u0026ndash;461. https://doi.org/10.1037/tps0000176\u003c/li\u003e\n \u003cli\u003eHosmer DW, Lemeshow S. Applied Logistic Regression. 2nd ed. New York: Wiley; 2000. https://doi. org/10.1002/0471722146\u003c/li\u003e\n \u003cli\u003eLindgren BM, Lundman B, Graneheim UH. Abstraction and interpretation during the qualitative content analysis process. Int J Nurs Stud. 2020; 108:103632. https://doi.org/10.1016/j.ijnurstu.2020.1\u003c/li\u003e\n \u003cli\u003eJohnson R E, Grove A L, Clarke A. Pillar integration process: A joint display technique to integrate data in mixed methods research[J]. Journal of Mixed Methods Research, 2019, 13(3): 301-320. https://doi.org/10.1177/1558689817743108\u003c/li\u003e\n \u003cli\u003eFetters MD, Curry LA, Creswell JW. Achieving integration in mixed methods designs-principles and practices. Health Serv Res. 2013 Dec;48(6 Pt 2):2134-56. doi: 10.1111/1475-6773.12117.\u003c/li\u003e\n \u003cli\u003eGuetterman T C, F\u0026agrave;bregues S, Sakakibara R. Visuals in joint displays to represent integration in mixed methods research: A methodological review[J]. Methods in Psychology, 2021, 5: 100080. https://doi.org/10.1016/j.metip.2021.100080\u003c/li\u003e\n \u003cli\u003eQara FJ, Alsulimani LK, Fakeeh MM, Bokhary DH. Knowledge of Nonmedical Individuals about Cardiopulmonary Resuscitation in Case of Cardiac Arrest: A Cross-Sectional Study in the Population of Jeddah, Saudi Arabia. Emerg Med Int. 2019 Jan 16; 2019:3686202. doi: 10.1155/2019/3686202.\u003c/li\u003e\n \u003cli\u003eNiles DE, Nishisaki A, Sutton RM, Elci OU, et al. Improved Retention of Chest Compression Psychomotor Skills With Brief \u0026quot;Rolling Refresher\u0026quot; Training. Simul Healthc. 2017 Aug;12(4):213-219. doi: 10.1097/SIH.0000000000000228.\u003c/li\u003e\n \u003cli\u003eBray JE, Smith K, Case R, et al. Public cardiopulmonary resuscitation training rates and awareness of hands-only cardiopulmonary resuscitation: a cross-sectional survey of Victorians. Emerg Med Australas. 2017 Apr;29(2):158-164. doi: 10.1111/1742-6723.12720.\u003c/li\u003e\n \u003cli\u003eSipsma K, Stubbs BA, Plorde M. Training rates and willingness to perform CPR in King County, Washington: a community survey. Resuscitation. 2011 May;82(5):564-7. doi: 10.1016/j.resuscitation.2010.12.007. Epub 2011 Jan 22.\u003c/li\u003e\n \u003cli\u003eHuy LD, Tung PT, et al. The willingness to perform first aid among high school students and associated factors in Hue, Vietnam. PLoS One. 2022 Jul 27;17(7): e 0271567. doi: 10.1371/journal. pone.0271567.\u003c/li\u003e\n \u003cli\u003eReader SW, Walton GH, Linder SH. Review and inventory of 911 Good Samaritan Law Provisions in the United States. Int J Drug Policy. 2022 Dec; 110:103896. doi: 10.1016/j.drugpo.2022.103896.\u003c/li\u003e\n \u003cli\u003eDaud A, Nawi AM, Aizuddin AN, Yahya MF. Factors and Barriers on Cardiopulmonary Resuscitation and Automated External Defibrillator Willingness to Use among the Community: A 2016-2021 Systematic Review and Data Synthesis. Glob Heart. 2023 Aug 25;18(1):46. doi: 10.5334/gh.1255.\u003c/li\u003e\n \u003cli\u003eKrishnamurti C, Mehdi Z, et al. The Role of Artificial Intelligence in CPR Training and Management: A Review[J]. Journal of Resuscitation, 2024, 1(1): 43-48. DOI: 10.4103/IRCF.IRCF_3_24\u003c/li\u003e\n \u003cli\u003ePerry, Marianna A. \u0026quot;The importance of CPR/AED and first aid training for security officers and SROs.\u0026quot; Handbook of Loss Prevention and Crime Prevention. Butterworth-Heinemann, 2020. 449-453. https://doi.org/10.1016/B978-0-12-817273-5.00038-7\u003c/li\u003e\n \u003cli\u003eNguyen H, Parker BR. Assessing the effectiveness of New York\u0026apos;s 911 Good Samaritan Law-Evidence from a natural experiment. Int J Drug Policy. 2018 Aug; 58:149-156. doi: 10.1016/j. drugpo. 2018.05.013.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Table","content":"\u003cp\u003eTable 4 is available in the Supplementary Files section\u003c/p\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-cardiovascular-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcar","sideBox":"Learn more about [BMC Cardiovascular Disorders](http://bmccardiovascdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcar/default.aspx","title":"BMC Cardiovascular Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Cardiopulmonary resuscitation, Out-of-hospital cardiac arrest, Mixed-methods research, Latent profile analysis, First responders, Security guards, Health systems, Public health policy","lastPublishedDoi":"10.21203/rs.3.rs-6641847/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6641847/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eOut-of-hospital cardiac arrest (OHCA) has a survival rate of just 1.2% in China, with bystander cardiopulmonary resuscitation (B-CPR) performed in only 4.5% of cases. Despite their formal designation as frontline responders, the CPR competency of security guards remains largely unknown. This study assessed their CPR knowledge, attitudes, behaviors, self-efficacy, and skills, and explored barriers to implementation.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eA convergent mixed-methods design was employed across six public settings in Shanghai (2020\u0026ndash;2022). Quantitative data (n\u0026thinsp;=\u0026thinsp;1,001) were analyzed using descriptive statistics, regression, and latent profile analysis (LPA). Qualitative interviews (n\u0026thinsp;=\u0026thinsp;15) explored implementation barriers. The Pillar Integration Process (PIP) synthesized findings.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003e Only 5.8% of guards demonstrated guideline-aligned CPR performance. Knowledge (mean: 35.1%), self-efficacy (55.0%), and behavioral scores (54.7%) were suboptimal. LPA identified three subgroups: low (77.0%), moderate (19.6%), and high behavior (3.4%). Low behavior was predicted by subway worksite (OR\u0026thinsp;=\u0026thinsp;5.47), low self-efficacy (OR\u0026thinsp;=\u0026thinsp;1.29), and lack of recent training (OR\u0026thinsp;=\u0026thinsp;0.28). Thematic analysis revealed multilevel barriers: individual (skill decay), organizational (role ambiguity), societal (legal fears), and victim-level (hesitation toward gender/age). Integration via the Pillar Integration Process yielded a comprehensive explanatory framework (Fig.\u0026nbsp;2) capturing the dynamic interplay between psychological readiness, contextual barriers, and CPR behavior.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eCPR competency gaps among security guards are critical yet modifiable. Targeted retraining, legal clarification, and institutional mandates are urgently needed to support China\u0026rsquo;s bystander response system and the Healthy China 2030 agenda.\u003c/p\u003e","manuscriptTitle":"Cardiopulmonary Resuscitation Competency and Implementation Barriers among Security Guards in Urban China: A Mixed-Methods Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-29 03:57:35","doi":"10.21203/rs.3.rs-6641847/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2025-10-27T10:22:24+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"198100760986429557398325917211966757622","date":"2025-10-24T07:16:44+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-10T14:54:32+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-09-16T16:17:41+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-08-04T07:39:34+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-08-04T07:39:21+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Cardiovascular Disorders","date":"2025-05-12T00:38:57+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-cardiovascular-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcar","sideBox":"Learn more about [BMC Cardiovascular Disorders](http://bmccardiovascdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcar/default.aspx","title":"BMC Cardiovascular Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b1a91e18-d86a-40b6-8e59-78bc52b93f1a","owner":[],"postedDate":"October 29th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-10-29T03:57:36+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-29 03:57:35","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6641847","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6641847","identity":"rs-6641847","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}