Background: Workplace violence (WV) towards psychiatric staff has commonly been associated with Posttraumatic Stress Disorder (PTSD). However, prospective studies have shown that not all psychiatric staff who experien...Background: Workplace violence (WV) towards psychiatric staff has commonly been associated with Posttraumatic Stress Disorder (PTSD). However, prospective studies have shown that not all psychiatric staff who experience workplace violence experience post-traumatic stress. Purpose: We want to examine the longitudinal trajectories of PTSD in this population to identify possible subgroups that might be more at risk. Furthermore, we need to investigate whether certain risk factors of PTSD might identify membership in the subgroups. Method: In a sample of psychiatric staff from 18 psychiatric wards in Denmark who had reported an incident of WV, we used Latent Growth Mixture Modelling (LGMM) and further logistic regression analysis to investigate this. Results: We found three separate PTSD trajectories: a recovering, a delayed-onset, and a moderate-stable trajectory. Higher social support and negative cognitive appraisals about oneself, the world and self-blame predicted membership in the delayed-onset trajectory, while higher social support and lower accept coping predicted membership in the delayed-onset trajectory. Conclusion: Although most psychiatric staff go through a natural recovery, it is important to be aware of and identify staff members who might be struggling long-term. More focus on the factors that might predict these groups should be an important task for psychiatric departments to prevent posttraumatic symptomatology from work.展开更多
Background This paper presents a retrospective study to classify patients into subtypes of the treatment according to baseline and longitudinally observed values considering heterogenity in migraine prognosis. In the ...Background This paper presents a retrospective study to classify patients into subtypes of the treatment according to baseline and longitudinally observed values considering heterogenity in migraine prognosis. In the classical prospective clinical studies, participants are classified with respect to baseline status and followed within a certain time period. However, latent growth mixture model is the most suitable method, which considers the population heterogenity and is not affected drop-outs if they are missing at random. Hence, we planned this comprehensive study to identify prognostic factors in migraine.展开更多
This paper examines city growth patterns and the corresponding city size distribution evolution over long periods of time using a simple New Economic Geography(NEG) model and urban population data from Canada. The mai...This paper examines city growth patterns and the corresponding city size distribution evolution over long periods of time using a simple New Economic Geography(NEG) model and urban population data from Canada. The main findings are twofold. First, there is a transition from sequential to parallel growth of cities over long periods of time: city growth shows a sequential mode in the stage of rapid urbanization, i.e., the cities with the best development conditions will take the lead in growth, after which the cities with higher ranks will become the fastest-growing cities; in the late stage of urbanization, city growth converges according to Gibrat′s law, and exhibits a parallel growth pattern. Second, city size distribution is found to have persistent structural characteristics: the city system is self-organized into multiple discrete size groups; city growth shows club convergence characteristics, and the cities with similar development conditions eventually converge to a similar size. The results will not only enhance our understanding of urbanization process, but will also provide a timely and clear policy reference for promoting the healthy urbanization of developing countries.展开更多
Objective:To preliminarily construct and apply a longitudinal trajectory model for the prognosis of intracerebral hemorrhage(ICH)based on blood urea nitrogen(BUN)characteristics.Methods:Clinical data from 320 ICH pati...Objective:To preliminarily construct and apply a longitudinal trajectory model for the prognosis of intracerebral hemorrhage(ICH)based on blood urea nitrogen(BUN)characteristics.Methods:Clinical data from 320 ICH patients admitted to our hospital between 2020 and 2024 were collected,including demographic information,National Institutes of Health Stroke Scale(NIHSS)scores at admission,dynamic changes in BUN levels during treatment,and 30-day survival outcomes.A latent class growth model(LCGM)was first used for preliminary modeling,followed by a latent growth mixture modeling(GMM)approach to determine the final model.Three classes of BUN trajectories for ICH prognosis were identified,and latent classes were established.GMM modeling was then performed on these latent classes,considering linear,quadratic,and cubic polynomial forms;six GMM models were constructed and individuals were assigned to latent trajectory groups for validation.Results:LCGM analysis ultimately identified three dynamic BUN trajectory groups:Sustained low-level group(76 cases,23.8%):BUN remained stable between 3.1-9.0 mmol/L,with the highest 30-day survival rate(98.7%).Fluctuating-declining group(222 cases,69.4%):BUN initially increased and then slowly decreased(peak at day 3:15.2 mmol/L),with a 30-day mortality of 8.1%(18/222),higher than the sustained low-level group.Sustained high-level group(22 cases,6.9%):BUN mean>9.0 mmol/L,with a 30-day mortality of 41.7%(P=0.000).GMM model fitting showed that the cubic polynomial GMM model was optimal(AIC=6754.474,BIC=6852.450,Entropy=0.905).Incorporating gender,age,and BMI as covariates revealed significant effects for gender(Estimate=0.045,-0.011,P=0.000,0.000).The AUC for predicting 30-day mortality was 0.88(sensitivity 82.8%,specificity 77.9%),which increased to 0.89 when combined with admission NIHSS scores.Conclusion:The LCGM+GMM model based on dynamic BUN trajectories effectively distinguishes prognostic subgroups in ICH patients.Patients with persistently elevated or fluctuating-rising BUN levels have a significantly higher mortality risk compared to those with sustained low levels.This model provides a new quantitative tool for early identification of high-risk patients and poor prognoses.展开更多
Objective:In this research,we tried to explore how short-term mindfulness(STM)intervention affects adoles-cents’anxiety,depression,and negative and positive emotion during the COVID-19 pandemic.Design:10 classes were...Objective:In this research,we tried to explore how short-term mindfulness(STM)intervention affects adoles-cents’anxiety,depression,and negative and positive emotion during the COVID-19 pandemic.Design:10 classes were divided into experiment groups(5 classes;n=238)and control(5 classes;n=244)randomly.Hospital Anxi-ety and Depression Scale(HADS)and Positive and Negative Affect Schedule(PANAS)were used to measure par-ticipants’dependent variables.In the experiment group,we conducted STM practice interventions every morning in theirfirst class from March to November 2020.No interventions were conducted in the control group.Methods:Paired-sample t-tests were used to identify if a significant difference exists between every time point of the experimental and control groups.Repeated ANOVA and Growth Mixture Model(GMM)were used to analyze the tendency of positive and negative emotions,anxiety,and depression in the experimental group.Results and Conclusions:(1)With the intervention of STM,there was a significant decrease in negative emotions and an increase in positive emotions in the experimental group,whereas there were non-significant differences in the control group.(2)To explore the heterogeneity trajectories of dependent variables,we built a GMM and found there were two latent growth classes in the trajectories.(3)The results of the models showed their trajec-tories were downward,which meant that the levels of anxiety,depression,and negative emotions of participants decreased during the STM training period.Nonetheless,the score of positive affect showed upward in three loops of intervention,which indicated that the level of the participants’positive affect increased through the STM inter-vention.(4)This research indicated that STM should be given increasing consideration to enhance mental health during the worldwide outbreak of COVID-19.展开更多
文摘Background: Workplace violence (WV) towards psychiatric staff has commonly been associated with Posttraumatic Stress Disorder (PTSD). However, prospective studies have shown that not all psychiatric staff who experience workplace violence experience post-traumatic stress. Purpose: We want to examine the longitudinal trajectories of PTSD in this population to identify possible subgroups that might be more at risk. Furthermore, we need to investigate whether certain risk factors of PTSD might identify membership in the subgroups. Method: In a sample of psychiatric staff from 18 psychiatric wards in Denmark who had reported an incident of WV, we used Latent Growth Mixture Modelling (LGMM) and further logistic regression analysis to investigate this. Results: We found three separate PTSD trajectories: a recovering, a delayed-onset, and a moderate-stable trajectory. Higher social support and negative cognitive appraisals about oneself, the world and self-blame predicted membership in the delayed-onset trajectory, while higher social support and lower accept coping predicted membership in the delayed-onset trajectory. Conclusion: Although most psychiatric staff go through a natural recovery, it is important to be aware of and identify staff members who might be struggling long-term. More focus on the factors that might predict these groups should be an important task for psychiatric departments to prevent posttraumatic symptomatology from work.
文摘Background This paper presents a retrospective study to classify patients into subtypes of the treatment according to baseline and longitudinally observed values considering heterogenity in migraine prognosis. In the classical prospective clinical studies, participants are classified with respect to baseline status and followed within a certain time period. However, latent growth mixture model is the most suitable method, which considers the population heterogenity and is not affected drop-outs if they are missing at random. Hence, we planned this comprehensive study to identify prognostic factors in migraine.
基金Under the auspices of Key Program of Chinese Academy of Sciences(No.KZZD-EW-06-01)
文摘This paper examines city growth patterns and the corresponding city size distribution evolution over long periods of time using a simple New Economic Geography(NEG) model and urban population data from Canada. The main findings are twofold. First, there is a transition from sequential to parallel growth of cities over long periods of time: city growth shows a sequential mode in the stage of rapid urbanization, i.e., the cities with the best development conditions will take the lead in growth, after which the cities with higher ranks will become the fastest-growing cities; in the late stage of urbanization, city growth converges according to Gibrat′s law, and exhibits a parallel growth pattern. Second, city size distribution is found to have persistent structural characteristics: the city system is self-organized into multiple discrete size groups; city growth shows club convergence characteristics, and the cities with similar development conditions eventually converge to a similar size. The results will not only enhance our understanding of urbanization process, but will also provide a timely and clear policy reference for promoting the healthy urbanization of developing countries.
文摘Objective:To preliminarily construct and apply a longitudinal trajectory model for the prognosis of intracerebral hemorrhage(ICH)based on blood urea nitrogen(BUN)characteristics.Methods:Clinical data from 320 ICH patients admitted to our hospital between 2020 and 2024 were collected,including demographic information,National Institutes of Health Stroke Scale(NIHSS)scores at admission,dynamic changes in BUN levels during treatment,and 30-day survival outcomes.A latent class growth model(LCGM)was first used for preliminary modeling,followed by a latent growth mixture modeling(GMM)approach to determine the final model.Three classes of BUN trajectories for ICH prognosis were identified,and latent classes were established.GMM modeling was then performed on these latent classes,considering linear,quadratic,and cubic polynomial forms;six GMM models were constructed and individuals were assigned to latent trajectory groups for validation.Results:LCGM analysis ultimately identified three dynamic BUN trajectory groups:Sustained low-level group(76 cases,23.8%):BUN remained stable between 3.1-9.0 mmol/L,with the highest 30-day survival rate(98.7%).Fluctuating-declining group(222 cases,69.4%):BUN initially increased and then slowly decreased(peak at day 3:15.2 mmol/L),with a 30-day mortality of 8.1%(18/222),higher than the sustained low-level group.Sustained high-level group(22 cases,6.9%):BUN mean>9.0 mmol/L,with a 30-day mortality of 41.7%(P=0.000).GMM model fitting showed that the cubic polynomial GMM model was optimal(AIC=6754.474,BIC=6852.450,Entropy=0.905).Incorporating gender,age,and BMI as covariates revealed significant effects for gender(Estimate=0.045,-0.011,P=0.000,0.000).The AUC for predicting 30-day mortality was 0.88(sensitivity 82.8%,specificity 77.9%),which increased to 0.89 when combined with admission NIHSS scores.Conclusion:The LCGM+GMM model based on dynamic BUN trajectories effectively distinguishes prognostic subgroups in ICH patients.Patients with persistently elevated or fluctuating-rising BUN levels have a significantly higher mortality risk compared to those with sustained low levels.This model provides a new quantitative tool for early identification of high-risk patients and poor prognoses.
基金Regional Science Fund Project of Northwest Normal University,Grant No.31660281.
文摘Objective:In this research,we tried to explore how short-term mindfulness(STM)intervention affects adoles-cents’anxiety,depression,and negative and positive emotion during the COVID-19 pandemic.Design:10 classes were divided into experiment groups(5 classes;n=238)and control(5 classes;n=244)randomly.Hospital Anxi-ety and Depression Scale(HADS)and Positive and Negative Affect Schedule(PANAS)were used to measure par-ticipants’dependent variables.In the experiment group,we conducted STM practice interventions every morning in theirfirst class from March to November 2020.No interventions were conducted in the control group.Methods:Paired-sample t-tests were used to identify if a significant difference exists between every time point of the experimental and control groups.Repeated ANOVA and Growth Mixture Model(GMM)were used to analyze the tendency of positive and negative emotions,anxiety,and depression in the experimental group.Results and Conclusions:(1)With the intervention of STM,there was a significant decrease in negative emotions and an increase in positive emotions in the experimental group,whereas there were non-significant differences in the control group.(2)To explore the heterogeneity trajectories of dependent variables,we built a GMM and found there were two latent growth classes in the trajectories.(3)The results of the models showed their trajec-tories were downward,which meant that the levels of anxiety,depression,and negative emotions of participants decreased during the STM training period.Nonetheless,the score of positive affect showed upward in three loops of intervention,which indicated that the level of the participants’positive affect increased through the STM inter-vention.(4)This research indicated that STM should be given increasing consideration to enhance mental health during the worldwide outbreak of COVID-19.
