Objective: To explore the causal link of cumulative ecological risk and future orientation with health risk behaviors among higher vocational college students, so as to provide reference for reducing and preventing health risk behaviors among higher vocational college students. Methods: A longitudinal follow up study was conducted on 612 students using convenience sampling from 2 vocational colleges in Hunan Province. The Cumulative Ecological Risk Scale, Future Orientation Scale, and Health Risk Behavior Scale were used during three follow up visits (T1: September 2022, T2: June 2023, T3: March 2024), and a cross lagged panel model was constructed to examine the longitudinal causal relationship of cumulative ecological risk, future orientation and health risk behaviors. Analysis of longitudinal intermediary effect between variables by Bootstrap. Results: The cumulative ecological risk scores of T1, T2 and T3 among higher vocational college students were (2.94±1.44,2.99±1.63,3.02±1.54), future orientation scores (40.49±4.71,41.51±5.72,41.06±4.35) and health risk behavior scores (3.73±2.01,3.49±2.00,3.23±2.00). The results of repeated measures ANOVA showed that the future orientation score of T2 was higher than that of T1, and the main effect of measurement time was statistically significant ( F=5.09，P＜0.01,η 2=0.02). The health risk behavior score of T1 was higher than that of T2, and the health risk behavior score of T2 was higher than that of T3, and the main effect of measurement time was statistically significant ( F=10.12，P＜0.01,η 2=0.03).The cross lagged model showed good adaptability, with χ 2/df =7.20 ( P <0.01), relative fitting indicators GFI=0.98, CFI=0.99, TLI=0.96, IFI=0.99, NFI =0.99, and absolute fitting indicator RMSEA =0.06. Among them, the T1, T2 cumulative ecological risk showed negatively predictive effects on T2, T3 future orientation ( β =-0.24, -0.47 ), and T1, T2 cumulative ecological risk positively predicted T2, T3 health risk behavior ( β =0.20, 0.24), while T1, T2 future orientation negatively predicted T2, T3 health risk behavior ( β =-0.25, -0.18) ( P ＜0.01). Bootstrap test analysis found that T2 future orientation had a longitudinal mediating effect ( β=0.04, P ＜0.01) on the T1 cumulative ecological risk and T3 health risk behavior. Conclusions The accumulation of ecological risk among higher vocational college students can positively predict health risk behaviors, while future orientation can negatively predict healthrisk behaviors. Moreover, future orientation plays a longitudinal mediating role between accumulated ecological risks and health risk behaviors.

Objective: To explore the association of latent profiles of mobile phone dependence and self control with physical exercise among junior high school students, so as to provide references for the prevention of mobile phone dependence and the improvement of self control among junior high school students. Methods: From April to May 2024, a stratified random cluster sampling method was used to select a total of 2 311 students from grade 7 to grade 9 in three public junior high schools in Xiangxi Autonomous Prefecture, Hunan Province. Latent profile analysis was conducted to identify the latent profiles of mobile phone dependence and self control among junior high school students. Pearson correlation analysis was used to examine the correlation between mobile phone dependence and self control, and Chi square test was used to analyze the distribution differences of latent profiles of adolescents across different demographic characteristics. Multiple Logistic regression analysis was applied to explore the association between mobile phone dependence, self control, and physical exercise. Results: Four latent profiles of mobile phone dependence and self control were identified: low dependence-moderate self control group ( n =885, 38.3%), moderate dependence-low self control group ( n =910, 39.4%), high dependence-no self control group ( n =232, 10.0%), and no dependence-high self control group ( n =284, 12.3%). Significant differences were observed in the distribution of latent profiles across gender, grade and only child status ( χ 2=10.85, 35.72, 13.85， P <0.05). Logistic regression analysis showed that, after controlling for demographic variables, compared with the low dependence-moderate self control group, physical exercise was negatively associated with the moderate dependence-low self control group ( OR =0.79) and the high dependence-no self control group ( OR =0.81), while positively associated with the no dependence-high self control group ( OR =1.58) ( P <0.01). Conclusions The influence of physical exercise on junior high school students different potential profile types of mobile phone dependence and self control is different. Schools and families should adopt targeted physical exercise interventions based on the characteristics of different profiles to promote the physical and mental health of junior high school students.

Objective: To analyze the heterogeneity of mobile phone dependence development trajectory in middle school students and its association with loneliness and selfcontrol ability, so as to provide reference for the prevention of mobile phone dependence in middle school students. Methods: A total of 941 grade 1 students from 4 public middle schools in Xiangxi Autonomous Prefecture, Hunan Province were selected for the followup survey by random cluster sampling from October 2023 to April 2024 and October 2024. Mobile Phone Addiction Index (MPAI), University of California, Los Angeles Loneliness Scale-20 (UCLA-20) and Selfcontrol Scales (SCS) were used for questionnaire survey. The heterogeneity of the developmental trajectory of middle school students mobile phone dependence was analyzed by the latent growth curve model (LGMM), and the influencing factors of the developmental trajectory of middle school students mobile phone dependence were explored by multiple Logistic regression analysis. Results: The development trajectory of middle school students mobile phone dependence could be divided into four categories: C1 "low risk slow decline group (n=438,44.6%)", C2 "medium risk slow rise group (n=272,29.7%)", C3 "high risk rapid decline group (n=73,8.6%)" and C4 "high risk rapid rise group (n=158,17.1%)". There were significant differences in the distribution of mobile phone dependence development track heterogeneity subgroups among sex, only child, lodging, and leftbehind students (χ2=117.79, 44.88, 37.09, 130.50, P <0.01). The results of the multinomial Logistic regression model analysis showed that, with C1 group as the reference, C2, C3, and C4 were positively correlated with students loneliness [OR(95%CI)=1.04 (1.02-1.06), 1.11(1.08-1.14), 1.12(1.09-1.14)]; C2 and C4 groups were negatively correlated with students selfcontrol [OR(95%CI)=0.97(0.96-0.99), 0.95(0.93-0.97)] (P<0.01). Conclusions The development trajectory of mobile phone dependence among middle school students is heterogeneous. Reducing the loneliness of individuals and cultivating good selfcontrol ability are helpful to alleviate mobile phone dependence behavior among middle school students.

Objective: To explore the influence of social support and school connectedness on the developmental trajectory of self efficacy in adolescents and analyze its subgroup effects, so as to provide a basis for enhancing adolescents self-efficacy. Methods: Using a cluster random sampling method, 930 first year middle school students from four schools in Xiangxi Autonomous Prefecture, Hunan Province, were selected for three longitudinal surveys in October 2023 (T1), April 2024 (T2), and October 2024 (T3). The General Self-efficacy Scale (GSES), Social Support Scale (SSS), and School Connectedness Scale (SCS) were administered. Latent growth mixture modeling (LGMM) was used to identify different developmental trajectories of self-efficacy in early adolescence. Multivariate Logistic regression was employed to examine the associations of self-efficacy trajectories with social support and school connectedness in adolescents. Results: The developmental trajectory of self-efficacy in adolescents was classified into three categories:category 1 was low efficacy-rapid growth group (53 students, 6.6%), category 2 was moderate efficacy-stable growth group (793 students, 84.1%), and category 3 was high efficacy-rapid decline group (84 students, 9.3%). Using the low efficacy- rapid growth group as the reference, students with higher social support were more likely to belong to the moderate efficacy- stable growth group ( OR=1.06, 95%CI =1.03-1.08) and the high efficacy-rapid decline group ( OR=1.06, 95%CI = 1.03 -1.09), students with higher school connectedness were more likely to belong to the high efficacy-rapid decline group ( OR= 1.10 , 95%CI =1.03-1.18) (all P <0.05). Subgroup analysis revealed significant effects for boarding status (low efficacy-rapid growth group at T1, t =2.10; high efficacy-rapid decline group in social support, t =-2.15) and only child status (moderate efficacy-stable growth group at T2, t =2.05) (all P <0.05). Conclusions The developmental trajectory of self-efficacy in adolescents exhibits group heterogeneity, with boarding status and only child status showing subgroup effects. Enhancing social support and school connectedness can help improve self-efficacy in adolescents.

Objective: To explore the latent class characteristics and related factors of health risk behaviors among higher vocational college students (referred to as vocational students) in the Wuling Mountain Area, so as to provide references for promoting their healthy development and formulating effective intervention measures. Methods: From April to June 2024, a stratified random cluster sampling method was used to survey 1 737 students from three higher vocational colleges in the Wuling Mountain Area (Xiangxi in Hunan, Enshi in Hubei, and Tongren in Guizhou). The study employed the Health risk Behavior Questionnaire for College Students, the Parent-Child Relationship Scale, the Social Support Scale, and the School Connection Scale for data collection. Latent class analysis (LCA) was used to examine the heterogeneous distribution characteristics of health risk behaviors among vocational students, and multivariate Logistic regression was applied to analyze the related factors of latent classes. Results: The LCA results identified three latent classes based on 12 health risk behaviors: the comprehensive high risk group (151 students, 8.7% ), the psychological distress group (883 students, 50.8%), and the low risk group (703 students, 40.5%). The distribution of latent classes showed statistically significant differences in gender and only child status ( χ 2=121.25, 9.85, both P <0.05). The low risk group scored higher in parent-child relationship (29.26±6.19), social support (63.98±18.16), and school connection (35.97± 7.71 ) compared to the comprehensive high risk group (27.28±6.03, 57.67±15.60, 32.97±7.55) and the psychological distress group (27.52±5.19, 62.06±14.54, 33.80±6.14) ( F =20.37, 23.51, 9.89, all P <0.05). Multivariate Logistic regression revealed that boys( OR =3.29) were more likely to belong to the comprehensive high risk group, social support ( OR =0.03, 0.21) and school connection ( OR =0.92, 0.96) were less likely to belong to both the comprehensive high risk and psychological distress groups (all P < 0.05). Conclusions There are three potential categories of healthharmful behaviors among vocational college students in Wuling Mountain Area. Schools, families and society should enhance the levels of parent-child relationship, school connections and social support for vocational college students of different categories to promote their physical and mental health development.

Objective: To understand the potential categories of health risk behaviors among adolescents in the Wuling Mountain Area and their association with school connectedness, so as to provide reference for formulating classified and effective intervention measures. Methods: From March to June 2023, 3 386 middle and high school students from eight schools in the Wuling Mountain Area were selected using the multistage stratified cluster random sampling method. A basic information questionnaire, health risk behaviors questionnaire, and school connectedness scale were utilized for the survey. The latent classes of adolescent health risk behaviors in the Wuling Mountain Area were investigated by using latent class analysis, while an multinomial Logistic regression model was employed to analyze the association between latent classes and school connectedness. Results: Adolescent health risk behaviors in the Wuling Mountains Area were classified into three latent classes: high risk class of episodic behaviors ( 5.64 %), high risk class of implicit behaviors (26.90%), and low risk class of implicit behaviors (67.45%). Gender, ethnicity, and family type revealed significant differences in the distribution of the three latent classes ( χ 2=117.91, 22.55, 21.51, P <0.05). The results of the regression model analysis showed that, with the low risk class as the reference category, high school connectedness scores were associated with the high risk class of episodic behaviors ( OR=0.89, 95%CI = 0.88- 0.91) and the high risk class of implicit behaviors ( OR=0.90, 95%CI =0.89-0.91)( P <0.05). Conclusions The characteristics of adolescent health risk behavior classes in the Wuling Mountain Area are obvious. The high risk class of episodic behaviors and the high risk class of implicit behaviors are negatively correlated with school connectedness. Corresponding measures should be taken to enhance adolescents sense of belonging in school and reduce the aggregation and co occurrence of health risk behaviors.

Objective: To analyze the developmental trajectories of middle school students loneliness and social support, as well as to explore the interaction between loneliness and social support, so as to provide the evidence based support for the mental health development of adolescents. Methods: A total of 989 first year students from four public middle schools in Xiangxi Tujia and Miao Autonomous Prefecture, Hunan Province were selected for three follow up surveys by a cluster random sampling method (T1:March 2023, T2:June 2023, T3:December 2023). The UCLA Loneliness Scale-20 (ULS-20) and Social Support Scale for University Students (SSSUS) were employed for questionnaire data collection. The growth mixture modeling was utilized to test the developmental trajectories of loneliness and social support among middle school students, while the cross lagged analysis was performed to investigate their mutual influence. Results: The scores for loneliness and social support in T1, T2 and T3 were (43.1±5.8, 42.5± 6.8 , 42.0±6.9; 55.9±12.0, 60.7±15.7, 60.4±16.7), respectively. Correlational analysis revealed a significant negative correlation between loneliness levels (T1, T2, T3) and social support (T1, T2, T3) ( r =-0.47 to -0.36, P <0.01). Growth mixture modeling indicated a linear declining trend of middle school students loneliness, and the developmental trajectory of social support showed a linear increasing trend, with significant individual differences in initial levels and rates of change ( P <0.05). Cross lagged analyses revealed that loneliness levels at T1 negatively predicted social support scores at T2 ( β =-0.16), and loneliness levels at T2 negatively predicted social support scores at T3 ( β =-0.12) ( P <0.05). Additionally, prior loneliness positively predicted its subsequent levels, with path coefficients of 0.58 and 0.47, respectively ( P <0.05). Social support scores at T1 negatively predicted loneliness levels at T2 ( β =-0.10), while scores at T2 negatively predicted loneliness levels at T3 ( β =-0.15) ( P <0.05). Prior loneliness also positively predicted its subsequent levels, with path coefficients of 0.43 and 0.44, respectively ( P <0.05). Conclusion The developmental trajectory of middle school students loneliness demonstrates a decreasing trend, while that of social support exhibits a linear increasing trend, indicating a longitudinal causal relationship between loneliness and social support.

Objective: To understand the potential categories of harmful behaviors of college students in Wuling Mountain Area and its relationship with campus bullying, so as to provide a theoretical basis for promoting the development of college students physical and mental health. Methods: The stratified random cluster sampling method was used to select 3 042 college students from six universities in Wuling Mountain Area from October to December, 2022. The "Chinese Youth Health Related/Risk Behaviors Questionnaire" (University Edition) compiled by the National Youth Health Related/Risk Behaviors Survey Group was used to investigate the health risk behaviors. The potential category analysis method was used to analyze the food preference, insecurity, fighting, loneliness, depression, insomnia, heartbreak, suicidal ideation, smoking, drinking, game addiction, Internet addiction of health risk behaviors were used to further analyze the relationship between different categories and campus bullying by using multi category Logistic regression method. Results: College students in Wuling Mountain Area were classified into low risk group of category 1 (44.2%), category 2 (5.4%) substance dependent group, category 3(50.4%) emotional disorder group. The distribution of potential categories of health risk behaviors among college students was statistically significant by gender( χ 2=31.44, 11.69， P < 0.05 ). Logistic regression analysis showed that after controlling demographic variables, campus bullying was the risk factor of category 3 emotional disorder group( OR =1.88, P <0.01). Conclusion bullying. Colleges and universities should attach great importance to the occurrence of campus bullying, formulate intervention programs for different categories of health hazard behaviors, and promote the healthy development of college students physical and mental health.

ObjectiveTo investigate the satisfaction of both service providers and users in community health service centers through questionnaires， and to analyze the reasons， so as to put forward suggestions for improving satisfaction. MethodsSampling was conducted in 247 community health service centers in 16 districts of Shanghai， and data were collected from employees and patients through on-site questionnaires. ResultsA total of 10 334 eligible questionnaires were collected from community health service centers， of which 7 712 were from patients and 2 622 were from employees. In 2021， the public's satisfaction with community health service centers was 96.51 points， and the satisfaction of employees was 94.08 points. The public expressed lower satisfaction with the availability of essential drugs for outpatient services， while the staff were less satisfied with their salary and benefits， work recognition， and career development. Regarding family doctor services， 73.38% of the public had signed up for family doctor services， while 23.57% of the public did not know about the family doctor services， and 16.18% believed it was necessary to improve the publicity of basic public health services. ConclusionThe public's overall satisfaction with the service of community health services is gradually increased. The basic medical services of community health are recognized by residents. Drug allocation needs to be improved urgently， and publicity and promotion need to be further strengthened. The overall satisfaction of employees is high， but further improvement is needed in terms of salary and benefits， work recognition， and career development.

Objective:To construct the evaluation index system of core competence of specialized gynecological nurses, and to provide reference for the curriculum setting and effect evaluation of standardized training for specialized gynecological nurses.Methods:The draft evaluation index system was prepared through literature research and group discussion. From May to June 2021, two rounds of Delphi letter consultation was used among 18 experts, to complete the selection and modification of evaluation index, and to determine the index weight.Results:The effective response rate of the two rounds of Delphi expert consultation was 100%, the expert authority coefficient was 0.938, and the Kendall harmony coefficient of the two rounds of expert letter consultation was 0.117 and 0.304, respectively (both P<0.01). Finally, the evaluation index system of core competence of specialized gynecological nurses was formed, including 4 first-level indexes, 11 second-level indexes and 48 third-level indexes. Conclusions:The evaluation index system of core competence of specialized gynecological nurses established in this study is scientific and reliable, which can provide reference for the curriculum setting and effect evaluation of standardized training for specialized gynecological nurses.