Latent Structural Modeling Research Articles

Are there possible issues that we overlook when selecting green infrastructure?

Promoting green infrastructure (GN) as a tool for reducing natural disaster risk is promising, particularly in depopulated areas worldwide. Images of GN as being multifunctional and environment friendly seem to enhance citizens’ positive attitudes toward it. While conventional types of disaster prevention infrastructure, such as concrete levees, can completely prevent damage, GN can relieve the effects of natural hazards. By switching from conventional infrastructure to GN, societies could reduce the costs of disaster prevention infrastructure. However, the risk of disaster damage may increase in some areas. This study aimed to better understand citizens’ preference for GN. It was hypothesized that those who prefer GN are those who do not take the initiative to learn about it and that some citizens are not fully aware of the heterogeneous risks when selecting GN. To test the hypotheses, structural and measurement models, including latent variables, were developed, and structural equation modeling was performed. Data for constructing the measurement and structural equations were obtained using a web questionnaire administered by a professional research company in 2022 with 1,200 participants across Japan. Data from the portal site for the official statistics of Japan (e-Stat) were also used. The results confirm the hypothesis that individuals who favor GN are those who do not proactively seek to acquire knowledge concerning it. The findings highlight the importance of considering, in future policymaking, the possibility that citizens who prefer GN without knowledge about it may face high risks as a result of its uninformed adoption and use.

Does Trait Rumination Moderate the Path From Chronic Stress Adversity Exposure to Internalizing Symptoms in First-Generation and Continuing-Generation College Students?

ABSTRACT First-generation college students face greater life adversity and demonstrate higher internalized distress versus their continuing-generation peers. In 598 undergraduate students, this study tests: links between life adversity and internalized distress, moderation by trait rumination, and whether effects differ for first-generation versus continuing-generation students. Four of six latent moderated structural equation models supported the hypothesis that trait rumination (the tendency to engage in negative, self-focused thought) moderates the effects of family and financial chronic stress to predict depression and anxiety. Effects were similar in first-generation and continuing-generation students. Implications include offering trait rumination interventions especially to those with high chronic stress.

A Comparison of Mixed and Partial Membership Diagnostic Classification Models with Multidimensional Item Response Models

Diagnostic classification models (DCM) are latent structure models with discrete multivariate latent variables. Recently, extensions of DCMs to mixed membership have been proposed. In this article, ordinary DCMs, mixed and partial membership models, and multidimensional item response theory (IRT) models are compared through analytical derivations, three example datasets, and a simulation study. It is concluded that partial membership DCMs are similar, if not structurally equivalent, to sufficiently complex multidimensional IRT models.

A tripartite model of the psychotherapy relationship: Interrelations among its components and their unfolding across sessions.

The tripartite model of the therapy relationship, which includes the working alliance, real relationship, and transference-countertransference configuration, has been a useful way to conceptualize the complexity of the connection between a therapist and a client. However, little research has focused on the interrelationships between these three components over time. This study sought to replicate the findings of Bhatia and Gelso (2018) by examining the between-person relationships among each of the three elements averaged across all sessions. Additionally, we extended earlier work by examining the within-person relationship between the working alliance, the real relationship, and transference-countertransference with themselves as well as with each of the other elements across sessions. Using 5,931 sessions across 142 clients and 36 therapists, we examined time-ordered associations among the cocreated working alliance, cocreated real relationship, and the therapist-rated transference-countertransference configuration using latent variable dynamic structural equation modeling. Results replicated the findings of Bhatia and Gelso (2018), demonstrating that in one session, the working alliance and the real relationship were positively related, and both the working alliance and the real relationship were negatively related to the transference-countertransference configuration. Regarding the interrelations over time, the findings revealed that the working alliance in the previous session had a significant and positive relationship with real relationship in the current session, and the real relationship in the previous session was related to reduced transference-countertransference in the current session. These findings provide support for complex interrelations among the components over time. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Pathways to prevent e-cigarette use: Examining the effectiveness of the truth antivaping campaign.

Mass media campaigns have been designed to counter a rise in e-cigarette use among young people. No studies to date have established pathways from campaign exposure to e-cigarette use behaviors. This study examines the mechanisms through which exposure to the truth® campaign may prevent the progression of e-cigarette use among youth and young adults. Data included four waves of the truth longitudinal cohort, a probability-based, nationally representative survey: Wave 1: September 2020-March 2021; Wave 2: July-October 2021; Wave 3: January-May 2022; and Wave 4: October 2022-January 2023. The sample (N = 4,744) was aged 15-24 years and nicotine naive at Wave 1. Latent growth structural equation modeling techniques examined the pathway from cumulative frequency of ad exposure (CFE) to the e-cigarette use progression via campaign-targeted attitudes. The direct effect from CFE to e-cigarette use progression was not significant. The overall indirect pathway shows that CFE was significantly associated with lower progression of e-cigarette use (β = -.01, p < .0001). CFE had a significant positive association with each campaign-targeted attitude, and each attitude was significantly associated with stronger perceived norms against e-cigarette use. Stronger perceived norms were significantly associated with a slower progression to e-cigarette use (β = -.21, p < .0001). Awareness of the truth antivaping campaign follows a pathway of targeted attitudes and perceptions of acceptability, then to slowed progression toward initiation of e-cigarette use. Antivaping campaigns should focus on shifting perceptions of acceptability to reduce e-cigarette use among young people. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Acceptance of smart sensing, its determinants, and the efficacy of an acceptance-facilitating intervention in people with diabetes: results from a randomized controlled trial.

Mental health problems are prevalent among people with diabetes, yet often under-diagnosed. Smart sensing, utilizing passively collected digital markers through digital devices, is an innovative diagnostic approach that can support mental health screening and intervention. However, the acceptance of this technology remains unclear. Grounded on the Unified Theory of Acceptance and Use of Technology (UTAUT), this study aimed to investigate (1) the acceptance of smart sensing in a diabetes sample, (2) the determinants of acceptance, and (3) the effectiveness of an acceptance facilitating intervention (AFI). A total of N = 132 participants with diabetes were randomized to an intervention group (IG) or a control group (CG). The IG received a video-based AFI on smart sensing and the CG received an educational video on mindfulness. Acceptance and its potential determinants were assessed through an online questionnaire as a single post-measurement. The self-reported behavioral intention, interest in using a smart sensing application and installation of a smart sensing application were assessed as outcomes. The data were analyzed using latent structural equation modeling and t-tests. The acceptance of smart sensing at baseline was average (M = 12.64, SD = 4.24) with 27.8% showing low, 40.3% moderate, and 31.9% high acceptance. Performance expectancy (γ = 0.64, p < 0.001), social influence (γ = 0.23, p = .032) and trust (γ = 0.27, p = .040) were identified as potential determinants of acceptance, explaining 84% of the variance. SEM model fit was acceptable (RMSEA = 0.073, SRMR = 0.059). The intervention did not significantly impact acceptance (γ = 0.25, 95%-CI: -0.16-0.65, p = .233), interest (OR = 0.76, 95% CI: 0.38-1.52, p = .445) or app installation rates (OR = 1.13, 95% CI: 0.47-2.73, p = .777). The high variance in acceptance supports a need for acceptance facilitating procedures. The analyzed model supported performance expectancy, social influence, and trust as potential determinants of smart sensing acceptance; perceived benefit was the most influential factor towards acceptance. The AFI was not significant. Future research should further explore factors contributing to smart sensing acceptance and address implementation barriers.

Short and long(er)-term returns of self-efficacy’s latent growth: class attendance and longitudinal impact on knowledge, proximal and distal interest

The broad relationship between students’ self-efficacy and interest has been highlighted for decades. This, along with the inherently developmental nature of learning, calls for a more thorough examination of the way fluctuations in students’ self-efficacy influence their interest in learning within a single course over an academic year and beyond. The present study investigated how the latent growth of students’ self-efficacy beliefs over time influenced their interest in a particular subject area, both short-term (3 weeks) and long-term (47 weeks). Data were collected at eight intervals: four times for self-efficacy within one academic year and three times for domain interest during the following academic year, primarily through weekly e-learning activities at a Japanese university. Attendance over three academic semesters, standardised achievement over one academic year (with prior knowledge control), and domain interest were modelled as outcomes in a latent structural equation model that encompassed a latent curve for self-efficacy beliefs throughout the course of study. The distinct contributions of both the baseline and the growth rate of self-efficacy beliefs to the variance in students’ domain interest over the short and longer term were confirmed by the study. Over the course of two academic years, it was found that the growth rate of self-efficacy, relative to its baseline, became increasingly more important for key learning outcomes such as students’ domain interest.

Factors associated with antiretroviral treatment adherence among people living with HIV in Guangdong Province, China: a cross sectional analysis

BackgroundUnderstanding factors associated with antiretroviral treatment (ART) adherence is crucial for ART success among people living with HIV (PLHIV) in the “test and treat” era. Multiple psychosocial factors tend to coexist and have a syndemic effect on ART adherence. We aimed to explore factors associated with ART adherence and the syndemic effect of multiple psychosocial factors on ART adherence among PLHIV newly starting ART in Guangdong Province, China.MethodsNewly diagnosed PLHIV from six cities in Guangdong Province were recruited between May 2018 and June 2019, and then followed up from May 2019 to August 2020. Baseline and follow-up data were collected from a questionnaire and the national HIV surveillance system, the follow-up data of which were analyzed in this study. A Center for Adherence Support Evaluation (CASE) index > 10 points was defined as optimal ART adherence, which was measured via participants’ self-reported adherence during follow-up survey. Multivariable logistic regression was used to identify factors associated with ART adherence. Exploratory factor analysis (EFA) and multi-order latent variable structural equation modeling (SEM) were performed to explore the syndemic effect of multiple psychosocial factors on ART adherence.ResultsA total of 734 (68.53%) follow-up participants were finally included in this study among the 1071 baseline participants, of whom 91.28% (670/734) had self-reported optimal ART adherence. Unemployment (aOR = 1.75, 95%CI: 1.01–3.02), no medication reminder (aOR = 2.28, 95%CI: 1.09–4.74), low medication self-efficacy (aOR = 2.28, 95%CI: 1.27–4.10), low social cohesion (aOR = 1.82, 95%CI: 1.03–3.19), no social participation (aOR = 5.65, 95%CI: 1.71–18.63), and ART side effects (aOR = 0.46, 95%CI: 0.26–0.81) were barriers to optimal ART adherence. The EFA and second-order latent variable SEM showed a linear relationship (standardized coefficient = 0.43, P < 0.001) between ART adherence and the latent psychosocial (syndemic) factor, which consisted of the three latent factors of medication beliefs and self-efficacy (standardized coefficient = 0.65, P < 0.001), supportive environment (standardized coefficient = 0.50, P < 0.001), and negative emotions (standardized coefficient=-0.38, P < 0.01). The latent factors of medication beliefs and self-efficacy, supportive environment, and negative emotions explained 42.3%, 25.3%, and 14.1% of the variance in the latent psychosocial factor, respectively.ConclusionsAbout nine out of ten PLHIV on ART in Guangdong Province self-reported optimal ART adherence. However, more efforts should be made to address barriers to optimal ART adherence.

Self-supervised Bipartite Graph Representation Learning: A Dirichlet Max-margin Matrix Factorization Approach

Bipartite graph representation learning aims to obtain node embeddings by compressing sparse vectorized representations of interactions between two types of nodes, e.g., users and items. Incorporating structural attributes among homogeneous nodes, such as user communities, improves the identification of similar interaction preferences, namely, user/item embeddings, for downstream tasks. However, existing methods often fail to proactively discover and fully utilize these latent structural attributes. Moreover, the manual collection and labeling of structural attributes is always costly. In this article, we propose a novel approach called Dirichlet Max-margin Matrix Factorization (DM3F), which adopts a self-supervised strategy to discover latent structural attributes and model discriminative node representations. Specifically, in self-supervised learning, our approach generates pseudo group labels (i.e., structural attributes) as a supervised signal using the Dirichlet process without relying on manual collection and labeling, and employs them in a max-margin classification. Additionally, we introduce a Variational Markov Chain Monte Carlo algorithm (Variational MCMC) to effectively update the parameters. The experimental results on six real datasets demonstrate that, in the majority of cases, the proposed method outperforms existing approaches based on matrix factorization and neural networks. Furthermore, the modularity analysis confirms the effectiveness of our model in capturing structural attributes to produce high-quality user embeddings.

大学生手机成瘾对学习倦怠的影响

To explore the relationship and mechanism of mobile phone addiction, self-control and learning burnout among college students, 1077 college students were measured by College Students’ Mobile Phone Addiction Tendency Scale, College Students’ Learning Burnout Scale and Self-Control Scale, and the data were analyzed by latent variable structural equation model in this paper. It finds that (1) Mobile phone addiction was positively correlated with learning burnout and its sub-dimensions, and negatively correlated with self-control and its sub-dimensions (P &lt; 0.001). (2) Self-control played a significant moderating role between mobile phone addiction and learning burnout (β = 0.11, P &lt; 0.001); (3) When self-control was high, mobile phone addiction had no significant effect on learning burnout (β = 0.06, P &gt; 0.05). When self-control was low, mobile phone addiction had a significant effect on learning burnout (β = -0.16, P &lt; 0.001). With the increase of mobile phone addiction, learning burnout decreased to a certain extent, but there was always strong learning burnout in the case of low self-control. College students’ mobile phone addiction has a positive impact on learning burnout, and self-control can regulate the relationship between mobile phone addiction and learning burnout.

The impact of leader humor on employee job crafting: a self-determination perspective

PurposeThe purpose of the research is to examine the impact of leader humor on employee job crafting. Using the insights from self-determination theory (SDT), we investigate the underlying mechanism of employees’ flow at work and the moderating role of employees’ playfulness trait.Design/methodology/approachWe adopted a three-wave field survey of 306 employees recruited from various industries. The moderated mediation model was examined using latent structural equation model analysis.FindingsResults revealed that leader humor positively affected employees’ flow at work and subsequent job crafting. Moreover, both the direct effect of leader humor on employees’ flow at work and the indirect effect of leader humor on employees’ job crafting via flow at work were amplified by employees’ playfulness trait.Practical implicationsLeaders are encouraged to use jokes and humorous language to facilitate job crafting among playful subordinates. Organizations can create a work environment conducive to flow at work through job redesign, regardless of employees’ levels of playfulness trait.Originality/valueThe paper uncovers the critical role of flow in the relationship between leader humor and employee job crafting and identifies employees’ playfulness trait as a boundary condition in which leader humor works.

Multilevel Latent Differential Structural Equation Model with Short Time Series and Time-Varying Covariates: A Comparison of Frequentist and Bayesian Estimators

Continuous-time modeling using differential equations is a promising technique to model change processes with longitudinal data. Among ways to fit this model, the Latent Differential Structural Equation Modeling (LDSEM) approach defines latent derivative variables within a structural equation modeling (SEM) framework, thereby allowing researchers to leverage advantages of the SEM framework for model building, estimation, inference, and comparison purposes. Still, a few issues remain unresolved, including performance of multilevel variations of the LDSEM under short time lengths (e.g., 14 time points), particularly when coupled multivariate processes and time-varying covariates are involved. Additionally, the possibility of using Bayesian estimation to facilitate the estimation of multilevel LDSEM (M-LDSEM) models with complex and higher-dimensional random effect structures has not been investigated. We present a series of Monte Carlo simulations to evaluate three possible approaches to fitting M-LDSEM, including: frequentist single-level and two-level robust estimators and Bayesian two-level estimator. Our findings suggested that the Bayesian approach outperformed other frequentist approaches. The effects of time-varying covariates are well recovered, and coupling parameters are the least biased especially using higher-order derivative information with the Bayesian estimator. Finally, an empirical example is provided to show the applicability of the approach.

Artificial intelligence and sustainable development during the pandemic: An overview of the scientific debates

The current work aims to analyze the main themes related to artificial intelligence (AI) and sustainable development during the pandemic period. This study provides an overview of the specialized literature related to AI and sustainability from the beginning of the pandemic through 2023. The present paper analyses scientific literature emphasizing both artificial intelligence's positive and negative impacts on sustainable development objectives (SDGs). To conduct the research, we employed bibliometric analysis and text-mining techniques to identify the major themes in the literature indexed in the Web of Science and Scopus databases. Firstly, we used descriptive measures to identify the authors' impact, the article production by country, the main keywords used, and other descriptive data. We further used data reduction methods based on co-word analysis (such as multiple correspondence analysis) on authors' keywords to show patterns in the themes explored in the literature. Bibliometric analysis was supplemented by text mining using Latent Dirichlet allocation (LDA) and structural topic modeling on abstracts to provide a comprehensive view of scientific debates on AI and sustainable development. Our research has identified various themes in the literature related to AI and sustainable development. These themes include social sustainability, health-related issues, AI technologies for energy efficiency, sustainability in industry and innovation, IoT technologies for smart and sustainable cities, urban planning, technologies for education and knowledge production, and the impact of technologies on SDGs. We also found that there is a significant positivity bias in the literature when discussing the impact of AI on sustainable development. Despite acknowledging certain risks, the literature tends to focus on the potential benefits of AI across various sectors. In addition, the analysis shows a growing emphasis on energy efficiency, which is facilitated by the use of AI technologies. Our study contributes to a better understanding of current scholarly discussion trends and emerging scientific avenues regarding AI and sustainable development. It also highlights the areas where research is needed and the implications for practitioners and policymakers.

Rules of prescriptions for heart failure of dilated cardiomyopathy based on latent structure model and association rules

The latent structure model and association rules analysis were employed to explore the compatibility rules of prescriptions for heart failure of dilated cardiomyopathy, with a view to providing theoretical support for the clinical treatment of this disease based on syndrome differentiation and the formulation of guidelines. The articles about the treatment of heart failure of dilated cardiomyopathy were retrieved from CNKI, Wanfang, VIP, and SinoMed. The database was established in Microsoft Excel 2019. Lantern 5.0 and Rstudio were used to analyze the latent structure and association rules of Chinese medicine with the frequency greater than 4.00%. Furthermore, the frequency structure model was used to mine the rules of prescriptions for heart failure of dilated cardiomyopathy. The study included 175 traditional Chinese medicine(TCM) prescriptions, involving 128 Chinese medicines, with the cumulative frequency of 1 847. High-frequency medicines included Astragali Radix, Salviae Miltiorrhizae Radix et Rhizoma, Poria, Cinnamomi Ramulus, Glycyrrhizae Radix et Rhizoma, with the main effects of tonifying, activating blood, resolving stasis, and releasing exterior. A total of 17 hidden variables, 34 hidden categories, and 6 comprehensive cluster models, along with 15 core prescriptions, were obtained. According to the prescriptions, the patients mainly had the syndromes of heart-Yang and Qi deficiency, Qi deficiency and blood stasis, heart-kidney Yang deficiency or Qi-Yin deficiency. Fifty-four strong association rules were obtained through association rule analysis. The highest degree of support was observed for the combination of Salviae Miltiorrhizae Radix et Rhizoma-Astragali Radix, while the highest degree of confidence was found for the combination of Salviae Miltiorrhizae Radix et Rhizoma-Cinnamomi Ramulus-Ophiopogonis Radix-Astragali Radix. The heart failure of dilated cardiomyopathy, characterized by internal deficiency and excess manifestations, is attributed to deficiency, stasis, and water. These factors are closely associated with the heart, lung, and spleen. The treatment should follow the principle of invigorating Qi and warming Yang, and meanwhile the method of activating blood and resolve stasis or moving Qi and promoting urination can be adopted according to the specific syndrome of patients.

How Does Interorganizational Inclusive Leadership influence Affiliated Sport Organization Employees? A bifactor Structural Model Approach with a Moderator of Power Distance

PURPOSE Inclusive leadership has become increasingly important in sport organizations. Accordingly, this study examines interorganizational inclusive leadership effects on organizational trust and affiliated organizations’ employees depending on power distance.METHODS A total of 250 affiliated sport organization employees participated, and latent moderated structural equation modeling with a bifactor structural model was employed to test the hypotheses.RESULTS The results indicated that macro inclusive leadership as well as its two components significantly enhance organizational trust. Interestingly, power distance positively moderated the macro effect of interorganizational inclusive leadership on organizational trust. Lastly, organizational trust enhanced job performance and well-being, and reduced turnover intention.CONCLUSIONS The results provide meaningful insight into the relationship between lead and affiliated organizations in the context of highly competitive and collaborative sport organizations.

Relations between linguistic diversity, classroom social climate, and reading comprehension in Norwegian elementary classrooms

ABSTRACT Linguistic diversity creates both opportunities and challenges for peer relations in today’s classrooms. However, how class linguistic diversity relates to peer relations as a dimension of classroom social climate and its relation to academic achievement is unclear. Furthermore, previous research has largely ignored cultural and linguistic proximities among students. Drawing on social identity and self-determination theories and applying lexicostatistical percentages as a proxy of linguistic diversity, this cross-sectional study used multi-level latent structural equation modeling to examine the associations between class linguistic diversity, classroom social climate, and reading comprehension, while controlling for socioeconomic status and gender. While class linguistic diversity and classroom social climate were not significantly related to class average reading comprehension, analyses revealed a negative association between class linguistic diversity and classroom social climate. This finding suggests linguistic diversity may instigate a unique social discord within classrooms as early as the elementary level which warrants additional research.

Identifying Dynamic Shifts to Careless and Insufficient Effort Behavior in Questionnaire Responses; a Novel Approach and Experimental Validation

Careless and insufficient effort responding (C/IER) is a situation where participants respond to survey instruments without considering the item content. This phenomena adds noise to data leading to erroneous inference. There are multiple approaches to identifying and accounting for C/IER in survey settings, of these approaches the best performing are model based classification techniques. Classic approaches to accounting for C/IER treat it as a person level phenomena. They first use some method to identify participants who exhibit C/IER, then use list-wise deletion to remove them prior to analysis. We argue that C/IER is actually a state that participants may exhibit for a portion of a survey instrument. In other words, participants start a survey with the intention to follow instructions, but at some point transition to a C/IER state and no longer respond in line with item contents. Accounting for C/IER at the item level, as opposed to the person level preserves data resulting in increased power. In this article we present a Bayesian Dynamic Latent Class Structural Equation Modeling (DLCSEM) approach for simultaneously accounting for C/IER at the item level and estimating a model of interest. We use a simulation study to establish the approaches performance under empirically relevant conditions and to compare it to other methods. We then conducted an experimental validation in which we induce C/IER like responses from human subjects and investigate the approaches ability to identify the point in which participants transition to a C/IER state. We also compare the model to existing approaches. In both the simulation and experimental validation the DLCSEM outperforms the alternative approaches. We conclude that the approach should be used by applied researchers for the pragmatic benefits of the method. Conclusions, limitations, and future directions are discussed.

Counseling trainees’ academic burnout, meaningful work, and career choice satisfaction: A resilience framework

AbstractThis study focused on examining counseling trainees’ perceptions of meaningful work as a resilience factor against decreased career choice satisfaction, which is related to burnout experience. As a result of latent moderated structural equation modeling, academic burnout and meaningful work were significantly related to career choice satisfaction. Furthermore, meaningful work significantly buffered the negative relationship between academic burnout and career choice satisfaction. Our findings suggest that meaningful work plays both compensatory and protective roles against the negative effect of academic burnout on career choice satisfaction. We provided implications for counselor training and preparation.

No evidence for relationship between paternal post-partum depressive symptoms and testosterone or cortisol in first-time fathers.

Male life history strategies are regulated by the neuroendocrine system. Testosterone (T) and cortisol regulate male behaviors including parenting and facilitate managing tradeoffs at key transitions in development such as first-time fatherhood. Both hormones demonstrate marked fluctuations in the postnatal period, and this presents an opportunity to investigate the role of T and cortisol in postpartum depressive symptoms-comparably less studied in fathers than in mothers in the evolutionary literature. Prior work on depressive symptoms has yet to integrate insights from the "dual hormone hypothesis (DHH)" which has focused on how T and cortisol interact to jointly regulate traits associated with dominance and status-seeking (i.e., mating effort) but has yet to be included in models of parenting effort. In this research, we use secondary data to investigate the relationship between DHH and traits ostensibly opposed to status seeking (i.e., depressive symptoms). First-time fathers (n = 193) provided morning saliva samples 10 months following parturition and reported on the presence of depressive symptoms (BDI-II). Responses were decomposed into three factors: cognitive, affective, and somatic. Using hybrid latent variable structural equation modeling, we did not find evidence that T predicted variability in cognitive, affective, or somatic depressive symptom factors. We found a null effect for cortisol as well. Finally, we could not find evidence that the DHH variable (T × cortisol interaction) predicted any variability in cognitive, affective, or somatic depressive symptoms. While we did not find evidence to support our hypotheses using a secondary data set, this study contributes to research on the neuroendocrinology of depression in fathers. Discussion focuses on the limitations of sample demographics, timing of saliva and self-report collection, and the lack of extant theory specific to paternal postpartum depression.

Measuring Hypochondria with Short Health Anxiety Inventory: Psychometric Properties in Colombian University Students

Introduction. The Short Health Anxiety Inventory is a commonly used tool for assessing health anxiety, but its psychometric properties and internal structure have not been examined in a Latin American Spanish-speaking population. This study aimed to establish the psychometric properties among Colombian university students. Method. The goodness of fit of four latent structure models of the Short Health Anxiety Inventory was tested using confirmatory factor analysis in a sample of 1004 Colombian university students. Results. The results show that the original model's structure does not fit well (CFI = .808; RMSEA = .074), and the reliability was .796 and .703 for the original two variables. Conclusions. The findings do not support the utilization of the Colombian version of the Short Health Anxiety Inventory by researchers and clinicians among Colombian university students.