A network analysis of the General Health Questionnaire

Simulation-Based Performance Evaluation of Missing Data Handling in Network Analysis

Network analysis has gained popularity as an approach to investigate psychological constructs. However, there are currently no guidelines for applied researchers when encountering missing values. In this simulation study, we compared the performance of a two-step EM algorithm with separated steps for missing handling and regularization, a combined direct EM algorithm, and pairwise deletion. We investigated conditions with varying network sizes, numbers of observations, missing data mechanisms, and percentages of missing values. These approaches are evaluated with regard to recovering population networks in terms of loss in the precision matrix, edge set identification and network statistics. The simulation showed adequate performance only in conditions with large samples (n ≥ 500 ) or small networks (p = 10). Comparing the missing data approaches, the direct EM appears to be more sensitive and superior in nearly all chosen conditions. The two-step EM yields better results when the ratio of n/p is very large - being less sensitive but more specific. Pairwise deletion failed to converge across numerous conditions and yielded inferior results overall. Overall, direct EM is recommended in most cases, as it is able to mitigate the impact of missing data quite well, while modifications to two-step EM could improve its performance.

Network visualization to interpret which healthcare services are central to people living with HIV

AIM

To employ network analysis to identify the central healthcare service needs of people living with HIV (PLWH) for integrated care.

DESIGN

Cross-sectional survey.

METHODS

A list of healthcare services was identified through literature reviews, expert workshops and validity evaluations by PLWH. A total of 243 PLWH participated at five hospitals and self-reported their need for healthcare services on a four-point Likert scale. Centrality of healthcare service needs was analysed using network analysis.

RESULTS

The mean score for 20 healthcare service needs was 3.53 out of 4. The highest scoring need, "Precaution for interaction between antiretroviral therapy and other drugs," received a rating of 3.73 but had a centrality of only 0.31. The most central node in the network of healthcare service needs, "Information and coping with opportunistic infections," had a strength centrality of 1.63 and showed significant relationships with "non-HIV-related medical services (e.g., health check-ups)" and "Regular dental services." The correlation stability coefficient, which quantifies the stability of centrality, was 0.44 with an acceptable value.

CONCLUSIONS

The most central need was information on opportunistic infections that had connections with many nodes in network analysis. By interpreting the relationships between needs, healthcare providers can design interventions with an integrative perspective.

IMPLICATIONS FOR PATIENT CARE

Network visualization provides dynamic relationships between needs that are unknown from the score scale by presenting them graphically and qualitatively.

IMPACT

Using network analysis to interpret need assessment offers an integrated nursing perspective. Coping with opportunistic infection is central to connecting the chain of healthcare. This study highlights the multifaceted understanding of patients' needs that nurses gain when they conduct network analysis.

REPORTING METHOD

We adhered to the STROBE checklist.

PATIENT OR PUBLIC CONTRIBUTION

No patient or public contribution.

Mental health status of pregnant women during COVID-19 in healthcare centers of Iran: A cross-sectional study

BACKGROUND AND OBJECTIVE

The COVID-19 pandemic impacted every single aspect of life. In addition to being a public health emergency, the COVID-19 outbreak impacted the mental health of individuals, especially pregnant women. This study aimed to examine the mental health status of pregnant women and also the effect of sociodemographic factors on their mental health status during COVID-19 in healthcare centers of Iran.

METHODS

This cross-sectional, analytical-descriptive study was conducted among pregnant women referring to healthcare centers in Shoushtar, Iran, in 2021. Multistage cluster sampling was used to select participants. Data were collected using the General Health Questionnaire-28 (GHQ-28). Data were analyzed using SPSS software version 22. The Pearson correlation coefficient was used to examine the association between quantitative variables. A generalized linear model (GLM) was applied to estimate the effect of independent variables on the dependent variable (mental health).

RESULTS

A total of 197 participants with a mean ± SD age of 27.85 ± 6.37 years took part in this study. The total mean score of mental health was estimated at 17.47±8.20. The highest mean ± SD score was, respectively, related to social dysfunction (6.63 ± 2.86), anxiety and insomnia (5.28 ± 3.53), and somatic symptoms (4.17 ± 3.27). Mental health disorder was significantly correlated with participants' age (R = .223, P = .00), number of pregnancy (gravida) (R = .272, P = .00), number of births (para) (R = 0.272, P = .00), and number of abortions (R = .172, P = .015). About 80% of pregnant women did not reveal impaired mental health conditions or psychological distress, while 19.3% showed scores that indicate probable mental health conditions.

CONCLUSION

Social dysfunction was the most common mental health problem among pregnant women. It is necessary to pay more attention to the mental health status of pregnant women during a pandemic. Interventions such as practical strategies to promote social support and improve pregnant women's mental health during pregnancy are highly important.

The use of psychological network analysis in informal dementia care: an empirical illustration

Objective: Theoretical models in informal dementia care have been developed to understand how risk and protective factors interact to cause caregiver's distress. The development of psychological network analysis provides a rich complement to our current models, as explores how different variables (or nodes) are associated using graph theories. Methods: The present study explored the use of network analysis using data from 125 informal caregivers of their partner with dementia (PwD). The included variables were recipient's dependency, self-efficacy, conflict within the family, dyadic adjustment, and caregiver's distress. Results: The analysis suggests a complex network of interacting variables. The core variable was not the caregiver's distress but rather their dyadic adjustment with their PwD. Variables were associated with caregiver distress through a large array of direct and indirect pathways and were associated with each other in the form of an asymmetric spider's web.Conclusion: The results show the complex interplay of variables in a psychological network. The central role of distress suggests a complex and dynamic role, notably through a bidirectional influence with quality of interactions. In the same way, quality of interactions appeared as one of the strongest nodes, its connectivity suggesting a crucial role to consider in our models and interventions.

Development and Evaluation of Health Recommender Systems: Systematic Scoping Review and Evidence Mapping

BACKGROUND

Health recommender systems (HRSs) are information retrieval systems that provide users with relevant items according to the users' needs, which can motivate and engage users to change their behavior.

OBJECTIVE

This study aimed to identify the development and evaluation of HRSs and create an evidence map.

METHODS

A total of 6 databases were searched to identify HRSs reported in studies from inception up to June 30, 2022, followed by forward citation and grey literature searches. Titles, abstracts, and full texts were screened independently by 2 reviewers, with discrepancies resolved by a third reviewer, when necessary. Data extraction was performed by one reviewer and checked by a second reviewer. This review was conducted in accordance with the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) statement.

RESULTS

A total of 51 studies were included for data extraction. Recommender systems were used across different health domains, such as general health promotion, lifestyle, and generic health service. A total of 23 studies had reported the use of a combination of recommender techniques, classified as hybrid recommender systems, which are the most commonly used recommender techniques in HRSs. In the HRS design stage, only 10 of 51 (19.6%) recommender systems considered personal preferences of end users in the design or development of the system; a total of 29 studies reported the user interface of HRSs, and most HRSs worked on users' mobile interfaces, usually a mobile app. Two categories of HRS evaluations were used, and evaluations of HRSs varied greatly; 62.7% (32/51) of the studies used the offline evaluations using computational methods (no user), and 33.3% (17/51) of the studies included end users in their HRS evaluation.

CONCLUSIONS

Through this scoping review, nonmedical professionals and policy makers can visualize and better understand HRSs for future studies. The health care professionals and the end users should be encouraged to participate in the future design and development of HRSs to optimize their utility and successful implementation. Detailed evaluations of HRSs in a user-centered approach are needed in future studies.

Network approach in health behavior research: how can we explore new questions?

Background: The network approach has recently been introduced to clinical psychology and provides a powerful framework for analyzing variables in a system. Since then, its applications have rapidly spread to various fields of social sciences. Unlike in the case of clinical psychology, the peculiarities of the phenomena under study in social sciences have not received sufficient attention. In this paper, along with practical illustrations, we discuss what a system of psychological variables represents and what the interrelationships between the variables mean in the context of health behavior research. Additionally, we explore the structural analysis of the system which has not been the focus of the recent applications of network analysis in health psychology. Discussion: In this paper, we illustrate two approaches of incorporating observable behavioral variables in a system and strategies for investigating structural components of the system. We illustrate these two approaches with an analysis of cross-sectional data on adolescents' beliefs and behavior with respect to registering their choice regarding organ donation in the Netherlands. Furthermore, with this paper, we wish to facilitate a larger discussion on conceptualizing networks of psychological variables, which will guide the analysis and the interpretation of node level interactions as well as network level structures.

Satter Eating Competence Inventory Subscale Restructure After Confirmatory Factor Analysis

OBJECTIVE

To examine the structural validity of the empirically and theoretically derived Satter Eating Competence Inventory (ecSI 2.0).

METHODS

Confirmatory factor analyses with 3 databases and a merger with 3 additional data sets (n = 2,010) from demographically heterogeneous samples.

RESULTS

Findings supported retaining all 16 items and migration of the item I trust myself to eat enough for me from the Internal Regulation to Eating Attitudes subscales, providing 4 distinct but correlated subscales and an adequately fitting model, χ²(98) = 1,364.27, P < .001, comparative fit index = .91, root mean square error of approximation = .07, 95% confidence interval, 0.07-0.08, standardized root mean square residual = .05.

CONCLUSION AND IMPLICATIONS

Psychometric integrity of the 16-item ecSI 2.0 was affirmed. Continued examination is recommended with cultural and linguistically diverse samples and network analyses to identify dynamic interactions among items.