A latent profile analysis of emotional expression among patients with unintentional injuries

Wang, Xinlu; Tu, Qiongyao; Huang, Dongdong; Jin, Pinpin; Chen, Xue; Zhao, Haifeng; Lu, Zhongqiu; Dong, Chaoqun

doi:10.1186/s12888-022-04390-4

Cited by 6 publications

(5 citation statements)

References 65 publications

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“…The appropriate number of subgroups was determined based on a range of criteria including Aikaike’s Information Criterion (AIC), Bayesian Information Criterion (BIC), adjusted Bayesian Information Criterion (aBIC), Entropy, Lo-Mendell Rubin Likelihood Ratio Test (LMRT), and Bootstrap Likelihood Ratio Test (BLRT). A model was considered a better fit with smaller AIC, BIC, and aBIC values [ 31 ]. The larger the entropy value, the more accurate the class classification, with values ≥ 0.8 indicating a good profile solution [ 32 ].…”

Section: Methodsmentioning

confidence: 99%

Health impacts of lifestyle and ambient air pollution patterns on all-cause mortality: a UK Biobank cohort study

Pu,

Zhu,

Shi

et al. 2024

BMC Public Health

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Background Extensive evidence indicates that both lifestyle factors and air pollution are strongly associated with all-cause mortality. However, little studies in this field have integrated these two factors in order to examine their relationship with mortality and explore potential interactions. Methods A cohort of 271,075 participants from the UK Biobank underwent analysis. Lifestyles in terms of five modifiable factors, namely smoking, alcohol consumption, physical activity, diet, and sleep quality, were classified as unhealthy (0–1 score), general (2–3 score), and healthy (4–5 score). Air pollution, including particle matter with a diameter ≤ 2.5 μm (PM2.5), particulate matter with a diameter ≤ 10 μm (PM10), particulate matter with a diameter 2.5–10 μm (PM2.5−10), nitrogen dioxide (NO2), and nitrogen oxides (NOx), was divided into three levels (high, moderate, and low) using Latent Profile Analysis (LPA). Cox proportional hazard regression analysis was performed to examine the links between lifestyle, air pollution, and all-cause mortality before and after adjustment for potential confounders. Restricted cubic spline curves featuring three knots were incorporated to determine nonlinear relationships. The robustness of the findings was assessed via subgroup and sensitivity analyses. Results With unhealthy lifestyles have a significantly enhanced risk of death compared to people with general lifestyles (HR = 1.315, 95% CI, 1.277–1.355), while people with healthy lifestyles have a significantly lower risk of death (HR = 0.821, 95% CI, 0.785–0.858). Notably, the difference in risk between moderate air pollution and mortality risk remained insignificant (HR = 0.993, 95% CI, 0.945–1.044). High air pollution, on the other hand, was independently linked to increased mortality risk as compared to low air pollution (HR = 1.162, 95% CI, 1.124–1.201). The relationship between NOx, PM10, and PM2.5−10 and all-cause mortality was found to be nonlinear (p for nonlinearity < 0.05). Furthermore, no significant interaction was identified between lifestyle and air pollution with respect to all-cause mortality. Conclusions Exposure to ambient air pollution elevated the likelihood of mortality from any cause, which was impacted by individual lifestyles. To alleviate this hazard, it is crucial for authorities to escalate environmental interventions, while individuals should proactively embrace and sustain healthy lifestyles.

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Section: Methodsmentioning

confidence: 99%

Health impacts of lifestyle and ambient air pollution patterns on all-cause mortality: a UK Biobank cohort study

Pu,

Zhu,

Shi

et al. 2024

BMC Public Health

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“…Mplus8.3 was used for potential profile analysis, and the scores of six dimensions of postpartum depression literacy were taken as explicit variables, and 1–4 categories were selected for model fitting estimation of potential profile analysis. The Aikeike information criterion (AIC), Bayesian information criterion (BIC), adjusted Bayesian information criterion (aBIC), entropy index, Lo–Mendell–Rubin adjusted likelihood ratio test (LMR-A), and bootstrapped likelihood ratio test (BLRT) were used as model fitting index; the smaller the AIC, BIC, and aBIC values, the better the model fitting [ 27 ]. An entropy index was used to evaluate classification accuracy.…”

Section: Methodsmentioning

confidence: 99%

Maternal postpartum depression literacy subtypes: A latent profile analysis

Li,

Lu,

Pang

et al. 2023

Heliyon

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“…Latent analysis has been widely used in scienti c research such as psychology, and it is a statistical method capable of identifying individual attributes in different populations [19].This study aimed to identify the work addiction pro les of nurses using LPA, and then identify the relationship between work addiction of latent categories and job burnout .In this study, the following three hypotheses are proposed.…”

Section: Conceptual Framework and Study Objectivesmentioning

confidence: 99%

Work addiction in nurses: A cross-sectional correlational study of latent profile analysis and burnout

Liang,

Yin,

Liu

et al. 2024

Preprint

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Objective To identify the distinct profiles of work addiction among clinical nurses, examine the demographic factors associated with profile memberships, and then explore the relationship between latent categories of work addiction and job burnout. Methods Latent Characteristic Analysis (LPA) was used to identify the different characteristics of nurses' work addiction from five dimensions: emotional restraint, work focus, busyness, results-oriented, and self-focused. The study employed binary logistic regression analysis to investigate the factors associated with various job addiction profiles. Stratified regression analyses were used to verify the correlation between general personal data and job burnout and job addiction. Results The study identified two possible attributes of nurse work addiction: the low addiction group (level 1) and the high addiction group (level 2). These two groups comprised 67.9% and 32.1%, respectively, of the entire sample of nurses. The work addiction categories of clinical nurses were found to be influenced by nurses' job titles and emotional exhaustion (P<0.05). Additionally, distinct work addiction categories were found to be significant predictors of nurses' burnout, which could account for the 18.70% variation in clinical nurses' work addiction independently. Conclusions Work addiction has an important predictive effect on burnout in clinical nurses. hospital administrators should actively monitor nurses who exhibit a high degree of work addiction and implement focused interventions to reduce the degree of burnout of nurses and ensure the quality of nursing work.

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A latent profile analysis of emotional expression among patients with unintentional injuries

Cited by 6 publications

References 65 publications

Health impacts of lifestyle and ambient air pollution patterns on all-cause mortality: a UK Biobank cohort study

Health impacts of lifestyle and ambient air pollution patterns on all-cause mortality: a UK Biobank cohort study

Maternal postpartum depression literacy subtypes: A latent profile analysis

Work addiction in nurses: A cross-sectional correlational study of latent profile analysis and burnout

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