Organizational culture and turnover intention among primary care providers: a multilevel study in four large cities in China

BACKGROUND

Primary health care plays an important role in providing populations with access to health care. However, it is currently facing unprecedented workforce shortages and high turnover worldwide.

OBJECTIVE

This study examined the relationship between organizational culture and turnover intention among primary care providers in China.

METHODS

A cross-sectional survey was administered in four large cities in China, Tianjin, Jinan, Shanghai, and Shenzhen, comprising 38 community health centers and 399 primary care providers. Organizational culture was measured using the Competing Value Framework model, which is divided into four culture types: group, development, hierarchy, and rational culture. Turnover intention was measured using one item assessing participants' intention to leave their current position in the following year. We compared the turnover intention among different organizational culture types using a Chi-square test, while the hierarchical logistic regression was used to examine the relationship between organizational culture and turnover intention.

RESULTS

The study found that 32% of primary care providers indicated an intention to leave. Primary care providers working in a hierarchical culture reported higher turnover intention (43.18%) compared with those in other cultures (p < 0.05). Hierarchical culture was a predictor of turnover intention (OR = 3.453, p < 0.001), whereas rational culture had a negative effect on turnover intention (OR = 0.319, p < 0.05).

CONCLUSIONS

Our findings inform organizational management strategies to retain a healthy workforce in primary health care.

Factors associated with the COVID-19 booster vaccine intentions of young adults in the United States

Young adults experience high coronavirus disease 2019 (COVID-19) incidence yet have the lowest vaccination and booster rates among adults. Understanding the factors influencing their intentions regarding boosters is essential for crafting effective public health strategies. We examined the psychosocial factors (attitudes, norms, perceived behavioral control) associated with their intentions to receive a COVID-19 booster. This cross-sectional study included 292 young adults aged 18-25 residing in Philadelphia who completed an online survey from September 2021 and February 2022 (mean age 21.98, standard deviation 2.25; 51% racial/ethnic minorities). The survey included measures of attitudes, norms, and perceived behavioral control related to COVID-19 vaccination. We employed structural equation modeling analysis to examine the intention of young adults to receive the COVID-19 booster and their vaccine-related attitudes, norms, and perceived behavioral control. Covariates included race/ethnicity and gender. Subjective norms were significantly associated with the intention to receive a COVID-19 booster (standardized β̂ = 0.685, p = .018). Attitudes and perceived behavioral control showed no significant association with intention. Subgroup analyses based on race/ethnicity revealed that attitudes (standardized β̂ = 0.488, p = .004) and subjective norms (standardized β̂ = 0.451, p = .050) were predictors among young adults from racial and ethnic minority backgrounds, while only subjective norms (standardized β̂ = 1.104, p = .002) were significant for non-Hispanic White young adults. Public health efforts should prioritize engaging healthcare providers and peer groups in order to influence subjective norms and promote collective responsibility and acceptance for vaccination. Tailored interventions and diverse communication strategies targeting specific subgroups of young adults may be useful to ensure comprehensive and effective vaccination initiatives.

Using an extended information-motivation-behavioral skills model to explain HPV vaccination intention among men who have sex with men only and men who have sex with men and women

Men who have sex with men and women (MSMW) have been reported to differ in psychosocial and sexual behavior patterns from men who have sex with men only (MSMO). However, results regarding the differences in HPV vaccination intention/behavior were inconclusive. We compared HPV vaccination intention between MSMO and MSMW and analyzed the differences in potentially associated factors in China. MSM participants were recruited online using a snowball sampling method. Cross-sectional data were collected via a questionnaire based on the extended information-motivation-behavioral skills model. Structural equation modeling was conducted to examine the relationship between the variables, followed by multi-group analysis to test differences between groups. Of 914 MSM, 77.68% were MSMO and 22.32% were MSMW. MSMW had a higher rate of reluctance to vaccinate than MSMO (23.53% vs. 16.20%, p = .016). Differences between the two groups were statistically significant in risky sexual behavior, behavioral skills, and promotional attitude. In both groups, promotional attitude was the most significant predictor of vaccination intention. Vaccination intention was directly influenced by motivation and indirectly by risky sexual behavior in MSMO, but not significantly in MSMW. Additionally, the direct effect of information on behavioral skills in MSMW was significantly greater than that in MSMO, but we did not find any effect of behavioral skills on vaccination intention. MSMW had lower vaccination intention than MSMO. MSMO may be influenced by risky sexual behavior and motivation, positively impacting their vaccination intention, unlike MSMW. Targeted strategies could help promote HPV vaccination, especially in MSMW.

Vaginal bleeding in pregnancy and adverse clinical outcomes: a systematic review and meta-analysis

Background: Vaginal bleeding during pregnancy has been recognised as a significant risk factor for adverse pregnancy outcomes. This study aimed to investigate the association between vaginal bleeding during the first trimester of pregnancy and clinical adverse effects using a systematic review and meta-analysis.Methods: Databases of Scopus, Web of Science, PubMed (including Medline), Cochrane Library and Science Direct were searched until June of 2023. Data analysis using statistical test fixed- and random-effects models in the meta-analysis, Cochran and meta-regression. The quality of the eligible studies was assessed by using the Newcastle-Ottawa Scale checklist (NOS).Results: A total of 46 relevant studies, with a sample size of 1,554,141 were entered into the meta-analysis. Vaginal bleeding during the first trimester of pregnancy increases the risk of preterm birth (OR: 1.8, CI 95%: 1.6-2.0), low birth weight (LBW; OR: 2.0, CI 95%: 1.5-2.6), premature rupture of membranes (PROMs; OR: 2.3, CI 95%: 1.8-3.0), abortion (OR: 4.3, CI 95%: 2.0-9.0), stillbirth (OR: 2.5, CI 95%: 1.2-5.0), placental abruption (OR: 2.2, CI 95%: 1.4-3.3) and placenta previa (OR: 1.9, CI 95%: 1.5-2.4).Conclusions: Vaginal bleeding in the first trimester of pregnancy is associated with preterm birth, LBW, PROMs, miscarriage, stillbirth, placental abruption and placenta previa. Therefore, physicians or midwives need to be aware of the possibility of these consequences and manage them when they occur.

Network analysis of smoking-related sleep characteristics in Chinese adults

The associations between multiple sleep characteristics and smoking behavior are inconsistent, and it is unclear which sleep characteristics are most crucial for tobacco prevention. This study aimed to explore the associations between smoking status/intensity and multiple sleep characteristics and to identify the potential core domain of smoking-related sleep using network analysis. Data were obtained from a survey of cancer-related risk factors among Chinese adults. Logistic regression models were used to quantify the associations between sleep characteristics and smoking status/intensity. Network analyses were employed to identify the core sleep characteristics. A total of 5,228 participants with a median age of 44 years old were included in the study. Current smoking was significantly positively associated with long nap time, difficulty falling asleep, late bedtime, getting up after 7 am, and waking up earlier than expected. There was significant positive association between current smoking and short sleep duration in young adults under 45 years old. Late bedtime and getting up after 7 am were only associated with current heavy smoking, but not current light smoking. Network analyses showed that multiple smoking-related sleep characteristics were interconnected, with difficulty falling asleep and late bedtime as central characteristics in the network. The study found that the associations between sleep characteristics and smoking varied by age and smoking intensity and highlights the potential benefits of sleep health promotion in smoking cessation, with a particular focus on difficulty falling asleep and late bedtime.

Contract governance to reduce antimicrobial overuse in hog farms in China

Antimicrobial resistance caused by the overuse of antimicrobials by farmers is an essential health issue. Currently, signing contracts between farmers and cooperatives has become crucial for small farmers to integrate into the modern agricultural industry chain. Unlike vertical government governance, which aims to intervene in the overuse of antimicrobials by farmers through many mandatory or incentive measures, contract governance refers to the social governance model that clarifies rights and obligations between farmers and cooperatives to ensure the adequate performance of contracts, which is especially beneficial to regulate farmers' safe production behavior such as the standard use of antimicrobials. However, there is a research gap concerning the effectiveness of contract governance in inhibiting farmers' overuse of animal antimicrobials. This study applies propensity score matching and mediation analysis method to assess how contract governance affects the overuse of antimicrobials by hog farmers. The paper uses data from 498 hog farmers of China's Hebei, Henan, and Hubei provinces to explore the impact and underlying mechanisms of contract governance on antimicrobial overuse in agriculture. The results showed that contract governance was found to inhibit the overuse of antimicrobials, with the amount paid for antimicrobials reduced by 118 yuan/household. We found evidence that this effect is exerted through three mechanisms: cooperative supervision (CS), technical guidance (TG), and product premium perception (PP). The CS, TG, and PP mediation effects accounted for 19.94%, 27.90%, and 26.93% of the total impact, respectively. Therefore, policy implications of these results include strengthening the standard of contract signing procedures and the integrity of executing contracts, increasing the market premium for products that meet the antimicrobial residue standards, and enhancing farmers' knowledge and contracting ability.

Associations between patterns of social support and perinatal mental health among Chinese mother: the mediating role of social trust

This study aims to explore the correlation between different social support patterns and perinatal mental health, and the mediating role of social trust in this. A cross-sectional survey was conducted in Jiangsu, China, with a sample size of 1705 pregnant respondents. Latent class analysis (LCA) was utilized to identify various social support patterns, while a multiple regression model was employed to analyze the mediating effect of social trust on the relationship between social support patterns and perinatal mental health. The study found four distinct social support patterns among the respondents: primary relationship-centric support, overall weak support, primary-secondary relationship-balanced support, and overall strong support. In the relationship between social support patterns and perinatal mental health, social trust played both a partial and full mediating role. The findings indicate that a social support system that enhances maternal trust and promotes honest disclosure of symptoms can effectively promote perinatal mental health.

Exploring symptom clusters in Chinese patients with peritoneal dialysis: a network analysis

BACKGROUND

In recent years, the research on symptom management in peritoneal dialysis (PD) patients has shifted from a single symptom to symptom clusters and network analysis. This study collected and evaluated unpleasant symptoms in PD patients and explored groups of symptoms that may affect PD patients with a view to higher symptom management.

METHODS

The symptoms of PD patients were measured using the modified Dialysis Symptom Index. The symptom network and node characteristics were assessed by network analysis, and symptom clusters were explored by factor analysis.

RESULTS

In this study of 602 PD patients (mean age 47.8 ± 16.8 years, 47.34% male), most had less than 2 years of dialysis experience. Five symptom clusters were obtained from factor analysis, which were body symptom cluster, gastrointestinal symptom cluster, mood symptom cluster, sexual disorder symptom cluster, and skin-sleep symptom cluster. Itching and decreased interest in sex may be sentinel symptoms, and being tired or lack of energy and feeling anxious are core symptoms in PD patients.

CONCLUSIONS

This study emphasizes the importance of recognizing symptom clusters in PD patients for better symptom management. Five clusters were identified, with key symptoms including itching, decreased interest in sex, fatigue, and anxiety. Early intervention focused on these symptom clusters in PD patients holds promise for alleviating the burden of symptoms.

Methodological quality of systematic reviews on atopic dermatitis treatments: a cross-sectional study

BACKGROUND

Systematic reviews (SRs) could offer the best evidence supporting interventions, but methodological flaws limit their trustworthiness in decision-making. This cross-sectional study appraised the methodological quality of SRs on atopic dermatitis (AD) treatments.

METHODS

We searched MEDLINE, EMBASE, PsycINFO, and Cochrane Database for SRs on AD treatments published in 2019-2022. We extracted SRs' bibliographical data and appraised SRs' methodological quality with AMSTAR (A MeaSurement Tool to Assess systematic Reviews) 2. We explored associations between methodological quality and bibliographical characteristics.

RESULTS

Among the 52 appraised SRs, only one (1.9%) had high methodological quality, while 45 (86.5%) critically low. For critical domains, only five (9.6%) employed comprehensive search strategy, seven (13.5%) provided list of excluded studies, 17 (32.7%) considered risk of bias in primary studies, 21 (40.4%) contained registered protocol, and 24 (46.2%) investigated publication bias. Cochrane reviews, SR updates, SRs with European corresponding authors, and SRs funded by European institutions had better overall quality. Impact factor and author number positively associated with overall quality.

CONCLUSIONS

Methodological quality of SRs on AD treatments is unsatisfactory. Future reviewers should improve the above critical methodological aspects. Resources should be devolved into upscaling evidence synthesis infrastructure and improving critical appraisal skills of evidence users.

Severity of depressive and anxious symptoms and its association with birth outcomes among pregnant women during the COVID-19 pandemic: a prospective case-control study

AIM

Comparing the anxiety and depression severity and their impact on subsequent birth outcomes in pregnant women before and during Omicron wave in Shanghai in 2022.

METHODS

The depression-anxiety symptoms networks were compared between the pregnant women during the outbreak period (outbreak group; n = 783) and a matched control group of pregnant women before the outbreak (pre-outbreak group; n = 783). The impact of baseline mental state on follow-up pregnancy and neonatal outcomes was also explored by logistic regression.

FINDINGS

Levels of depression and anxiety between the two groups were not significant different. Network analysis showed that central symptom "trouble relaxing" and bridge symptom "depressed mood" shared by both groups. Different symptom associations in different periods of the pandemic. Total scores and sub-symptom scores of prenatal depressive and anxious severities increased the odds ratios of maternal and neonatal syndromes. The influence of mental state on gestational and neonatal outcomes differed across different pandemic periods.

CONCLUSION

The Omicron wave did not have a significant negative impact on the depressive and anxious mood in pregnant women. Targeting central and bridge symptoms intervention may be effective in reducing their adverse effects on co-occurring of anxious and depressive mood and birth outcomes.

Clustering broiler farmers based on their behavioural differences towards biosecurity to prevent highly pathogenic avian influenza

Highly pathogenic avian influenza (HPAI) is an important zoonotic disease. The study aims to identify farmer behaviour types to inform the design of behaviour change programmes for mitigating the transmission of HPAI. Therefore, the study utilised multivariate statistical analysis for gaining a better understanding of the relationships among farmers' 30 biosecurity behaviours, the risk of HPAI infection, and distinct features of commercial broiler farmers, which is different from using simple and few binary biosecurity measures. Convenience sampling was used to collect data from 303 Taiwan's farmers among which 40 farmers (13.2%) self-reported having had a HPAI outbreak in the study year while 16 farmers (5.3%) self-reported having had a HPAI outbreak in the past two years. Using categorical principal components analysis and a two-stage cluster analysis, four farmer clusters were identified with distinct features: 1)'Reserved' (4.6%) tended to choose 'No idea' for answering specific questions about HPAI; 2)'Secure' (76.3%) had a higher biosecurity status than the other farms; 3) 'Jeopardised' (16.8%) had a lower biosecurity status than the other farms; 4) 'No-response' (2.3%) tended to skip specific questions about HPAI. The biosecurity status of the 'Reserved' and 'No-response' clusters was undetermined, placing these farms at risk of HPAI infection. Compared to the 'Secure' cluster, the 'Jeopardised' cluster exhibited higher odds of self-reported HPAI in the study year (OR: 2.61, 95% CI: 1.22-5.58) and in the past two years (OR: 4.28, 95% CI: 1.39-13.19). Additionally, the 'Jeopardised' cluster showed increased odds of HPAI recurrence (OR: 4.01, 95% CI: 1.41-11.43). Our study demonstrates that inadequate biosecurity practices can elevate the occurrence or recurrence of HPAI outbreaks. The findings underscore the importance of distinguishing between these clusters to accurately assess the risk of HPAI infection across farms. Furthermore, understanding farmers' behaviours can inform the development of strategies aimed at behaviour change among farmers.

Remote sensing estimates of global sea surface nitrate: Methodology and validation

Information about sea surface nitrate (SSN) concentrations is crucial for estimating oceanic new productivity and for carbon cycle studies. Due to the absence of optical properties in SSN and the intricate relationships with environmental factors affecting spatiotemporal dynamics, developing a more representative and widely applicable remote sensing inversion algorithm for SSN is challenging. Most methods for the remote estimation of SSN are based on data-driven neural networks or deep learning and lack mechanistic descriptions. Since fitting functions between the SSN and sea surface temperature (SST), mixed layer depth (MLD), and chlorophyll (Chl) content have been established for the open ocean, it is important to include the remote sensing indicator photosynthetically active radiation (PAR), which is critical in nitrate biogeochemical processes. In this study, we employed an algorithm for estimating the monthly average SSN on a global 1° by 1° resolution grid; this algorithm relies on the empirical relationship between the World Ocean Atlas 2018 (WOA18) monthly interpolated climatology of nitrate in each 1° × 1° grid and the estimated monthly SST and PAR datasets from Moderate Resolution Imaging Spectroradiometer (MODIS) and MLD from the Hybrid Coordinate Ocean Model (HYCOM). These results indicated that PAR potentially affects SSN. Furthermore, validation of the SSN model with measured nitrate data from different months and locations for the years 2018-2023 yielded a high prediction accuracy (N = 12,846, R2 = 0.93, root mean square difference (RMSE) = 3.12 μmol/L, and mean absolute error (MAE) = 2.22 μmol/L). Further independent validation and sensitivity tests demonstrated the validity of the algorithm for retrieving SSN.

A behavioural exploration of language aptitude and experience, cognition and more using Graph Analysis

Language aptitude has recently regained interest in cognitive neuroscience. Traditional language aptitude testing included phonemic coding ability, associative memory, grammatical sensitivity and inductive language learning. Moreover, domain-general cognitive abilities are associated with individual differences in language aptitude, together with factors that have yet to be elucidated. Beyond domain-general cognition, it is also likely that aptitude and experience in domain-specific but non-linguistic fields (e.g. music or numerical processing) influence and are influenced by language aptitude. We investigated some of these relationships in a sample of 152 participants, using exploratory graph analysis, across different levels of regularisation, i.e. sensitivity. We carried out a meta cluster analysis in a second step to identify variables that are robustly grouped together. We discuss the data, as well as their meta-network groupings, at a baseline network sensitivity level, and in two analyses, one including and the other excluding dyslexic readers. Our results show a stable association between language and cognition, and the isolation of multilingual language experience, musicality and literacy. We highlight the necessity of a more comprehensive view of language and of cognition as multivariate systems.

OSMR is a potential driver of inflammation in amyotrophic lateral sclerosis

JOURNAL/nrgr/04.03/01300535-202419110-00031/figure1/v/2024-03-08T184507Z/r/image-tiff Amyotrophic lateral sclerosis is a neurodegenerative disease, and the molecular mechanism underlying its pathology remains poorly understood. However, inflammation is known to play an important role in the development of this condition. To identify driver genes that affect the inflammatory response in amyotrophic lateral sclerosis, as well as potential treatment targets, it is crucial to analyze brain tissue samples from patients with both sporadic amyotrophic lateral sclerosis and C9orf72-related amyotrophic lateral sclerosis. Therefore, in this study we used a network-driven gene analysis tool, NetBID2.0, which is based on SJARACNe, a scalable algorithm for the reconstruction of accurate cellular networks, to experimentally analyze sequencing data from patients with sporadic amyotrophic lateral sclerosis. The results showed that the OSMR gene is pathogenic in amyotrophic lateral sclerosis and participates in the progression of amyotrophic lateral sclerosis by mediating the neuroinflammatory response. Furthermore, there were differences in OSMR activity and expression between patients with sporadic amyotrophic lateral sclerosis and those with C9orf72-related amyotrophic lateral sclerosis. These findings suggest that OSMR may be a diagnostic and prognostic marker for amyotrophic lateral sclerosis.

Exercise training mode effects on myokine expression in healthy adults: A systematic review with meta-analysis

BACKGROUND

The benefits of exercise are well known; however, many of the underlying molecular mechanisms are not fully understood. Skeletal muscle secretes myokines, which mediate muscle-organ crosstalk. Myokines regulate satellite-cell proliferation and migration, inflammatory cascade, insulin secretion, angiogenesis, fatty oxidation, and cancer suppression. To date, the effects of different exercise modes (namely, aerobic and resistance exercise) on myokine response remain to be elucidated. This is crucial considering the clinical implementation of exercise to enhance general health and wellbeing and as a medical treatment.

METHODS

A systematic search was undertaken in PubMed, MEDLINE, CINAHL, Embase, SPORTDiscus, and Web of Science in April 2023. Eligible studies examining the effects of a single bout of exercise on interleukin15 (IL-15), irisin, secreted protein acidic and rich in cysteine (SPARC), oncostatin M (OSM), and decorin were included. A random-effects meta-analysis was also undertaken to quantify the magnitude of change.

RESULTS

Sixty-two studies were included (n = 1193). Overall, exercise appeared to induce small to large increases in myokine expression, with effects observed immediately after to 60 min post-exercise, although these were mostly not statistically significant. Both aerobic and resistance exercise resulted in changes in myokine levels, without any significant difference between training modes, and with the magnitude of change differing across myokines. Myokine levels returned to baseline levels within 180 min to 24 h post-exercise. However, owing to potential sources of heterogeneity, most changes were not statistically significant, indicating that precise conclusions cannot be drawn.

CONCLUSION

Knowledge is limited but expanding with respect to the impact of overall and specific effects of exercise on myokine expression at different time points in the systemic circulation. Further research is required to investigate the effects of different exercise modes at multiple time points on myokine response.

The role of borderline personality disorder traits in predicting longitudinal variability of major depressive symptoms among a sample of depressed adults

BACKGROUND

Major depressive disorder (MDD) and borderline personality disorder (BPD) often co-occur, with 20 % of adults with MDD meeting criteria for BPD. While MDD is typically diagnosed by symptoms persisting for several weeks, research suggests a dynamic pattern of symptom changes occurring over shorter durations. Given the diagnostic focus on affective states in MDD and BPD, with BPD characterized by instability, we expected heightened instability of MDD symptoms among depressed adults with BPD traits. The current study examined whether BPD symptoms predicted instability in depression symptoms, measured by ecological momentary assessments (EMAs).

METHODS

The sample included 207 adults with MDD (76 % White, 82 % women) recruited from across the United States. At the start of the study, participants completed a battery of mental health screens including BPD severity and neuroticism. Participants completed EMAs tracking their depression symptoms three times a day over a 90-day period.

RESULTS

Using self-report scores assessing borderline personality disorder (BPD) traits along with neuroticism scores and sociodemographic data, Bayesian and frequentist linear regression models consistently indicated that BPD severity was not associated with depression symptom change through time.

LIMITATIONS

Diagnostic sensitivity and specificity may be restricted by use of a self-report screening tool for capturing BPD severity. Additionally, this clinical sample of depressed adults lacks a comparison group to determine whether subclinical depressive symptoms present differently among individuals with BPD only.

CONCLUSIONS

The unexpected findings shed light on the interplay between these disorders, emphasizing the need for further research to understand their association.

Introducing prediction intervals for sample means

Researchers and practitioners are typically familiar with descriptive statistics and statistical inference. However, outside of regression techniques, little attention may be given to questions around prediction. In the current paper, we introduce prediction intervals using fundamental concepts that are learned in descriptive and inferential statistical training (i.e., sampling error, standard deviation). We walk through an example using simple hand calculations and reference a simple R package that can be used to calculate prediction intervals.

Identifying pathways from childhood adversity to suicidal thoughts and behaviors among sexual minority adults: An exploratory mediation analysis

BACKGROUND

The current study uses a nationally representative longitudinal dataset of sexual minority adults in the US to investigate the pathways from adverse childhood experiences (ACEs) to adulthood suicidal thoughts and behaviors.

METHODS

ACEs were measured at year one, potential mediators at year two, and suicidal thoughts and behaviors (suicidal ideation, intent, plan, and attempt) at year three. We conducted an exploratory mediation analysis to identify potential mediating factors linking ACEs to suicidal thoughts and behaviors. Ten candidate mediators were examined: social well-being, felt stigma, experiences of everyday discrimination, social support, psychological distress, alcohol and drug use, importance of sexual identity, community connection, and internalized homophobia.

RESULTS

Participants were 1518 adults who identified as lesbian or gay (n = 833; 55 %), bisexual (n = 493; 33 %), or with other sexual minority identities (n = 181; 12 %) and were on average 36.48 years (SD = 14.7) of age. Psychological distress served as a common mediator between ACEs and suicidal ideation, intent, plan, and attempt. Additionally, experiences of everyday discrimination emerged as a specific mediator leading to suicidal intent, whereas social support uniquely mediated the relation between ACEs and suicide plan.

LIMITATIONS

Potential recall bias due to retrospective reporting of ACEs may be a limitation. Future studies should broaden the measurement scope of ACEs and implement intersectional methods.

CONCLUSION

The current findings underscore the urgent need for targeted interventions that address the specific mental health needs of sexual minority individuals, particularly focusing on mitigating psychological distress, combating systemic discrimination, and enhancing social support.

Assessing the influence of the digital economy on carbon emissions: Evidence at the global level

This paper discusses the influence of the digital economy (DE) on carbon emissions based on evidence at the global level. Specifically, based on the panel data from 80 countries from 2010 to 2020, this paper creates a DE measurement index and uses the System-GMM model to assess the influence of DE on carbon emissions. The results show that: (1) The development of DE significantly promotes carbon emissions reduction. (2) The development of DE significantly promotes carbon emissions reduction through technological advancement, structural optimization, and educational enhancement; (3) Regulatory quality and financial development play a positively moderating role in DE's promoting effect on carbon emissions reduction; (4) DE of European and North American nations have stronger promoting effect on carbon emissions reduction than DE of other countries. Compared to DE of developing countries, DE of developed countries has a stronger promoting effect on carbon emissions reduction. Additionally, this paper also finds that institutional differences can impact the carbon emission reduction effects of DE. Based on the results, this paper suggests that governments globally should promote the development of DE and foster international cooperation to enhance DE's driving role in promoting carbon emissions reduction.

Psychological responses to the COVID-19 pandemic and situational impact: A network analysis in Hong Kong residents

BACKGROUND

The COVID-19 pandemic has had situational impacts and induced various psychological responses among residents. The present study aims to analyze the network structure of psychological responses to the pandemic and their relationships with situational impact, resilience, and well-being through a network approach.

METHODS

An online survey recruited 1122 residents (mean age = 32.5 years, 65.4 % female) in Hong Kong from October 2020 to October 2021. The participants completed the Mental Impact and Distress Scale: Psychological Response (MIDc-PR) and other validated measures. Gaussian graphical modeling was conducted in R to identify the central MIDc-PR symptoms and their bridge linkages with situational impact, resilience, and well-being.

RESULTS

In the MIDc-PR network, the central symptoms were 'Routine' and 'Images' for Anticipation and 'Tense' and 'Lonely' for Modulation, with 12 bridge edges between the two clusters. In the combined network, the MIDc-PR nodes showed 12 bridge edges with situational impact, particularly in the health domain. 'Concerned', 'Sleep', and 'Lonely' showed negative linkages with resilience; 'Unmotivated' and 'Trapped' showed negative linkages with well-being. The network models showed good levels of stability and did not differ significantly in network structure and global strength across gender and age groups.

LIMITATIONS

The cross-sectional study design only allowed undirected associations in the network and could not model the within-subject effects.

CONCLUSIONS

The study findings contributed to the literature by elucidating the network structure of psychological responses to the pandemic. The central and bridge symptoms have clinical implications as potential targets for future interventions.

Everything, altogether, all at once: Addressing data challenges when measuring speech intelligibility through entropy scores

When investigating unobservable, complex traits, data collection and aggregation processes can introduce distinctive features to the data such as boundedness, measurement error, clustering, outliers, and heteroscedasticity. Failure to collectively address these features can result in statistical challenges that prevent the investigation of hypotheses regarding these traits. This study aimed to demonstrate the efficacy of the Bayesian beta-proportion generalized linear latent and mixed model (beta-proportion GLLAMM) (Rabe-Hesketh et al., Psychometrika, 69(2), 167-90, 2004a, Journal of Econometrics, 128(2), 301-23, 2004c, 2004b; Skrondal and Rabe-Hesketh 2004) in handling data features when exploring research hypotheses concerning speech intelligibility. To achieve this objective, the study reexamined data from transcriptions of spontaneous speech samples initially collected by Boonen et al. (Journal of Child Language, 50(1), 78-103, 2023). The data were aggregated into entropy scores. The research compared the prediction accuracy of the beta-proportion GLLAMM with the normal linear mixed model (LMM) (Holmes et al., 2019) and investigated its capacity to estimate a latent intelligibility from entropy scores. The study also illustrated how hypotheses concerning the impact of speaker-related factors on intelligibility can be explored with the proposed model. The beta-proportion GLLAMM was not free of challenges; its implementation required formulating assumptions about the data-generating process and knowledge of probabilistic programming languages, both central to Bayesian methods. Nevertheless, results indicated the superiority of the model in predicting empirical phenomena over the normal LMM, and its ability to quantify a latent potential intelligibility. Additionally, the proposed model facilitated the exploration of hypotheses concerning speaker-related factors and intelligibility. Ultimately, this research has implications for researchers and data analysts interested in quantitatively measuring intricate, unobservable constructs while accurately predicting the empirical phenomena.

Customizing Bayesian multivariate generalizability theory to mixed-format tests

Mixed-format tests, which typically include dichotomous items and polytomously scored tasks, are employed to assess a wider range of knowledge and skills. Recent behavioral and educational studies have highlighted their practical importance and methodological developments, particularly within the context of multivariate generalizability theory. However, the diverse response types and complex designs of these tests pose significant analytical challenges when modeling data simultaneously. Current methods often struggle to yield reliable results, either due to the inappropriate treatment of different types of response data separately or the imposition of identical covariates across various response types. Moreover, there are few software packages or programs that offer customized solutions for modeling mixed-format tests, addressing these limitations. This tutorial provides a detailed example of using a Bayesian approach to model data collected from a mixed-format test, comprising multiple-choice questions and free-response tasks. The modeling was conducted using the Stan software within the R programming system, with Stan codes tailored to the structure of the test design, following the principles of multivariate generalizability theory. By further examining the effects of prior distributions in this example, this study demonstrates how the adaptability of Bayesian models to diverse test formats, coupled with their potential for nuanced analysis, can significantly advance the field of psychometric modeling.

Refining Bayesian hierarchical MPT modeling: Integrating prior knowledge and ordinal expectations

Multinomial processing tree (MPT) models are a broad class of statistical models used to test sophisticated psychological theories. The research questions derived from these theories often go beyond simple condition effects on parameters and involve ordinal expectations (e.g., the same-direction effect on the memory parameter is stronger in one experimental condition than another) or disordinal expectations (e.g., the effect reverses in one experimental condition). Here, we argue that by refining common modeling practices, Bayesian hierarchical models are well suited to estimate and test these expectations. Concretely, we show that the default priors proposed in the literature lead to nonsensical predictions for individuals and the population distribution, leading to problems not only in model comparison but also in parameter estimation. Rather than relying on these priors, we argue that MPT modelers should determine priors that are consistent with their theoretical knowledge. In addition, we demonstrate how Bayesian model comparison may be used to test ordinal and disordinal interactions by means of Bayes factors. We apply the techniques discussed to empirical data from Bell et al. Journal of Experimental Psychology: Learning, Memory, and Cognition, 41, 456-472 (2015).

Exploration of the MCMC Wald test with linear regression

Recently, Asparouhov and Muthén Structural Equation Modeling: A Multidisciplinary Journal, 28, 1-14, (2021a, 2021b) proposed a variant of the Wald test that uses Markov chain Monte Carlo machinery to generate a chi-square test statistic for frequentist inference. Because the test's composition does not rely on analytic expressions for sampling variation and covariation, it potentially provides a way to get honest significance tests in cases where the likelihood-based test statistic's assumptions break down (e.g., in small samples). The goal of this study is to use simulation to compare the new MCM Wald test to its maximum likelihood counterparts, with respect to both their type I error rate and power. Our simulation examined the test statistics across different levels of sample size, effect size, and degrees of freedom (test complexity). An additional goal was to assess the robustness of the MCMC Wald test with nonnormal data. The simulation results uniformly demonstrated that the MCMC Wald test was superior to the maximum likelihood test statistic, especially with small samples (e.g., sample sizes less than 150) and complex models (e.g., models with five or more predictors). This conclusion held for nonnormal data as well. Lastly, we provide a brief application to a real data example.

Methods for online calibration of Q-matrix and item parameters for polytomous responses in cognitive diagnostic computerized adaptive testing

The ability to rapidly provide examinees with detailed and effective diagnostic information is a critical topic in psychology. Knowing what diagnostic criteria the examinees have met enables the practitioner to seek the solution to help them in a timely manner, and this can be achieved by cognitive diagnostic computerized adaptive testing (CD-CAT). However, the pervasive challenge of replenishing items in the CD-CAT item bank limits its practical application. Online calibration is a means to address item replenishment, but in CD-CAT, most existing online calibration methods that jointly calibrate the Q-matrix and item parameters of the new items are developed only for dichotomous responses and are time-consuming. Notably, previous studies pay no attention to polytomously scored items that are frequently observed in testing, even though they can offer additional evidence for the examinees' diagnosis. To fill this gap, we propose a SCAD-based method (SCAD-EM) to calibrate the Q-matrix and item parameters of the new items with polytomous response data in order to promote the application of CD-CAT in practice. The performance of the SCAD-EM was investigated in two comprehensive simulation studies and compared against the revised single-item estimation method (SIE-BIC). Results indicated that the SCAD-EM produces a higher calibration accuracy for the category-level Q-matrix and is computationally more efficient across all conditions, but it produces a lower calibration accuracy for the item-level Q-matrix. An empirical study further demonstrated the utility of the SCAD-EM and the SIE-BIC methods in calibrating new items with a real dataset. The advantages of the proposed method, its limitations, and possible future research directions are offered at the end.

The effect of transcranial direct current and magnetic stimulation on fear extinction and return of fear: A meta-analysis and systematic review

BACKGROUND

We conducted a meta-analysis and qualitative review on the randomized controlled trials investigating the effects of transcranial direct current stimulation and transcranial magnetic stimulation on fear extinction and the return of fear in non-primate animals and humans.

METHODS

The meta-analysis was conducted by searching PubMed, Web of science, PsycINFO, and Cochrane Library and extracting fear response in the active and sham groups in the randomized controlled trials. The pooled effect size was quantified by Hedges' g using a three-level meta-analytic model in R.

RESULTS

We identified 18 articles on the tDCS effect and 5 articles on the TMS effect, with 466 animal subjects and 621 human subjects. Our findings show that tDCS of the prefrontal cortex significantly inhibit fear retrieval in animal models (Hedges' g = -0.50). In human studies, TMS targeting the dorsolateral/ventromedial prefrontal cortex has an inhibiting effect on the return of fear (Hedges' g = -0.24).

LIMITATIONS

The limited number of studies and the heterogeneous designs of the selected studies made cross-study and cross-species comparison difficult.

CONCLUSIONS

Our findings shed light on the optimal non-invasive brain stimulation protocols for targeting the neural circuitry of threat extinction in humans.

Suicidal ideation among non-physician hospital system staff: Prevalence and workplace correlates

BACKGROUND

Research suggests that healthcare workers are at greater risk for suicide than other occupations, but most published studies focus on physicians. This study examines the prevalence of suicidal ideation (SI) and associated occupational factors among a broad group of non-physician healthcare staff.

METHODS

An anonymous online survey was sent to a random sample of 30 % of non-physician healthcare staff at a large urban healthcare system between September and November 2022. Weighted multivariable binary logistic regressions were conducted to determine the workplace and mental health factors associated with SI.

RESULTS

The 1084 respondents included nurses, administrative staff, research staff, medical assistants, nurse practitioners, physician assistants, and other roles. Of the sample, 8.8 % endorsed having SI over the prior two weeks. Results of the regression indicated that, after adjusting for demographic factors, greater odds of SI were associated with physical violence experienced from a patient or visitor (odds ratio [OR] = 2.15, 95 % confidence interval [CI] = 1.06-4.37), lower perceived leadership support (OR = 0.95, 95 % CI = 0.92-0.98), and positive screening for depression (OR = 4.66, 95 % CI = 2.45-8.86). Exploratory analysis suggests that depression may be a mediating factor between workplace stressors and SI.

LIMITATIONS

Limitations include the response rate, the use of a single item to assess SI, and the cross-sectional design.

CONCLUSION

Findings suggest that workplace violence and leadership support are important occupational factors associated with SI among healthcare workers. Reducing and mitigating workplace violence, enhancing leadership support, and improving access to mental health care should be considered targets for interventions to decrease suicide risk in this population.

A network analysis of positive psychosocial factors and indication of suboptimal HIV care outcomes among Black women living with HIV

Black women living with HIV (BWLWH) face barriers that impact health outcomes. However, positive psychosocial indicators may influence HIV care outcomes. Among this cross-sectional study of 119 BWLWH, a network analysis was utilized to examine relationships between positive psychosocial factors and HIV-related health outcomes. A preliminary polychoric analysis was conducted to examine correlations between the variables, and the network analyzed connections between resilience, self-efficacy, self-esteem, perceived social support, religious coping, post-traumatic growth, and an indicator variable for suboptimal HIV care outcomes (low medication adherence, detectable viral load, and missed HIV-related health visits) and determined the centrality measures within the network. Seven significant associations were found among the factors: self-efficacy and self-esteem, post-traumatic growth and resilience, post-traumatic growth and self-efficacy, post-traumatic growth and religious coping, perceived social support and resilience, self-esteem and resilience, self-esteem and perceived social support (bootstrapped 95% CI did not contain zero). Self-efficacy was the strongest indicator associated with the other factors. Although not statistically significant, the indicator for suboptimal HIV care outcomes was negatively associated with perceived social support and religious coping. Future interventions incorporating self-efficacy may be beneficial to the overall well-being of Black women.

Meta-analysis in a digitalized world: A step-by-step primer

In recent years, much research and many data sources have become digital. Some advantages of digital or Internet-based research, compared to traditional lab research (e.g., comprehensive data collection and storage, availability of data) are ideal for an improved meta-analyses approach.In the meantime, in meta-analyses research, different types of meta-analyses have been developed to provide research syntheses with accurate quantitative estimations. Due to its rich and unique palette of corrections, we recommend to using the Schmidt and Hunter approach for meta-analyses in a digitalized world. Our primer shows in a step-by-step fashion how to conduct a high quality meta-analysis considering digital data and highlights the most obvious pitfalls (e.g., using only a bare-bones meta-analysis, no data comparison) not only in aggregation of the data, but also in the literature search and coding procedure which are essential steps in any meta-analysis. Thus, this primer of meta-analyses is especially suited for a situation where much of future research is headed to: digital research. To map Internet-based research and to reveal any research gap, we further synthesize meta-analyses on Internet-based research (15 articles containing 24 different meta-analyses, on 745 studies, with 1,601 effect sizes), resulting in the first mega meta-analysis of the field. We found a lack of individual participant data (e.g., age and nationality). Hence, we provide a primer for high-quality meta-analyses and mega meta-analyses that applies to much of coming research and also basic hands-on knowledge to conduct or judge the quality of a meta-analyses in a digitalized world.

Psychometric benefits of self-chosen rating scales over given rating scales

Rating scales are susceptible to response styles that undermine the scale quality. Optimizing a rating scale can tailor it to individuals' cognitive abilities, thereby preventing the occurrence of response styles related to a suboptimal response format. However, the discrimination ability of individuals in a sample may vary, suggesting that different rating scales may be appropriate for different individuals. This study aims to examine (1) whether response styles can be avoided when individuals are allowed to choose a rating scale and (2) whether the psychometric properties of self-chosen rating scales improve compared to given rating scales. To address these objectives, data from the flourishing scale were used as an illustrative example. MTurk workers from Amazon's Mechanical Turk platform (N = 7042) completed an eight-item flourishing scale twice: (1) using a randomly assigned four-, six-, or 11-point rating scale, and (2) using a self-chosen rating scale. Applying the restrictive mixed generalized partial credit model (rmGPCM) allowed examination of category use across the conditions. Correlations with external variables were calculated to assess the effects of the rating scales on criterion validity. The results revealed consistent use of self-chosen rating scales, with approximately equal proportions of the three response styles. Ordinary response behavior was observed in 55-58% of individuals, which was an increase of 12-15% compared to assigned rating scales. The self-chosen rating scales also exhibited superior psychometric properties. The implications of these findings are discussed.

Examining time-varying dynamics of co-occurring depressed mood and anxiety

BACKGROUND

Dimensional frameworks of psychopathology call for multivariate approaches to map co-occurring disorders to index what symptoms emerge when and for whom. Ecological momentary assessment (EMA) offers a method for assessing and differentiating the dynamics of co-occurring symptoms with greater temporal granularity and naturalistic context. The present study used multivariate mixed effects location-scale modeling to characterize the time-varying dynamics of depressed mood and anxiety for women diagnosed with social anxiety disorder (SAD) and major depression (MDD).

METHODS

Women completed five daily EMA surveys over 30 days (150 EMA surveys/woman, T ≈ 5250 total observations) and two clinical diagnostic and retrospective self-report measures administered approximately two months apart.

RESULTS

There was evidence of same-symptom lagged effects (bs = 0.08-0.09), but not cross-symptom lagged effects (bs < 0.01) during EMA. Symptoms co-varied such that momentary spikes from one's typical level of anxiety were associated with increases in momentary depressed mood (b = 0.19) and greater variability of depressed mood (b = 0.06). Similarly, spikes from one's typical levels of depressed mood were associated with increases in momentary anxiety (b = 0.19). Furthermore, the presence and magnitude of effects demonstrated person-specific heterogeneity.

LIMITATIONS

Our findings are constrained to the dynamics of depressed and anxious mood among cisgender women with primary SAD and current or past MDD.

CONCLUSIONS

Findings from this work help to characterize how daily experiences of co-occurring mood and anxiety fluctuate and offer insight to aid the development of momentary, person-specific interventions designed to regulate symptom fluctuations.

Effects of game-based digital interventions for mental disorders: A meta-analysis

With increasing research attention on game-based digital interventions for mental disorders, a number of studies have been conducted to explore the effectiveness of digital game-based interventions on mental disorders. However, findings from previous research were inconsistent. Thus, we conducted a comprehensive meta-analytic review of the effectiveness of game-based digital interventions for mental disorders. By searching the articles in databases, we identified 53 studies in which 2433 participants were involved, and 282 effect sizes were extracted. Among the 53 studies, 14 employed within-group (pre/post) designs, and the remaining 39 utilized controlled trial designs. Using a three-level random-effects meta-analytic model, a medium effect size of game-based digital interventions (g = 0.47, 95 % CI: 0.33, 0.61) was revealed in the controlled trial designs and a close-to-medium effect size (g = 0.45, 95 % CI: 0.32, 0.58) was found in the within-group (pre/post) designs, indicating close-to-medium-sized efficacy of game-based digital interventions for mental disorders. Moderator analyses showed that age in the controlled trial designs had contributed to the heterogeneity in previous studies, suggesting that interventions might be more effective for the elderly. However, given that only a limited number of studies were focused on the elderly, more studies with older participants should be conducted in the future to provide more robust evidence and explore the mechanisms of how digital gaming interventions can be more effective in improving mental disorders symptoms.

Measuring object recognition ability: Reliability, validity, and the aggregate z-score approach

Measurement of domain-general object recognition ability (o) requires minimization of domain-specific variance. One approach is to model o as a latent variable explaining performance on a battery of tests which differ in task demands and stimuli; however, time and sample requirements may be prohibitive. Alternatively, an aggregate measure of o can be obtained by averaging z-scores across tests. Using data from Sunday et al., Journal of Experimental Psychology: General, 151, 676-694, (2022), we demonstrated that aggregate scores from just two such object recognition tests provide a good approximation (r = .79) of factor scores calculated from a model using a much larger set of tests. Some test combinations produced correlations of up to r = .87 with factor scores. We then revised these tests to reduce testing time, and developed an odd one out task, using a unique object category on nearly every trial, to increase task and stimuli diversity. To validate our measures, 163 participants completed the object recognition tests on two occasions, one month apart. Providing the first evidence that o is stable over time, our short aggregate o measure demonstrated good test-retest reliability (r = .77). The stability of o could not be completely accounted for by intelligence, perceptual speed, and early visual ability. Structural equation modeling suggested that our tests load significantly onto the same latent variable, and revealed that as a latent variable, o is highly stable (r = .93). Aggregation is an efficient method for estimating o, allowing investigation of individual differences in object recognition ability to be more accessible in future studies.

Statistical indices of masculinity-femininity: A theoretical and practical framework

Statistical indices of masculinity-femininity (M-F) summarize multivariate profiles of sex-related traits as positions on a single continuum of individual differences, from masculine to feminine. This approach goes back to the early days of sex differences research; however, a systematic discussion of alternative M-F indices (including their meaning, their mutual relations, and their psychometric properties) has been lacking. In this paper I present an integrative theoretical framework for the statistical assessment of masculinity-femininity, and provide practical guidance to researchers who wish to apply these methods to their data. I describe four basic types of M-F indices: sex-directionality, sex-typicality, sex-probability, and sex-centrality. I examine their similarities and differences in detail, and consider alternative ways of computing them. Next, I discuss the impact of measurement error on the validity of these indices, and outline some potential remedies. Finally, I illustrate the concepts presented in the paper with a selection of real-world datasets on body morphology, brain morphology, and personality. An R function is available to easily calculate multiple M-F indices from empirical data (with or without correction for measurement error) and draw summary plots of their individual and joint distributions.

Comparing the Clique Percolation algorithm to other overlapping community detection algorithms in psychological networks: A Monte Carlo simulation study

In psychological networks, one limitation of the most used community detection algorithms is that they can only assign each node (symptom) to a unique community, without being able to identify overlapping symptoms. The clique percolation (CP) is an algorithm that identifies overlapping symptoms but its performance has not been evaluated in psychological networks. In this study, we compare the CP with model parameters chosen based on fuzzy modularity (CPMod) with two other alternatives, the ratio of the two largest communities (CPRat), and entropy (CPEnt). We evaluate their performance to: (1) identify the correct number of latent factors (i.e., communities); and (2) identify the observed variables with substantive (and equally sized) cross-loadings (i.e., overlapping symptoms). We carried out simulations under 972 conditions (3x2x2x3x3x3x3): (1) data categories (continuous, polytomous and dichotomous); (2) number of factors (two and four); (3) number of observed variables per factor (four and eight); (4) factor correlations (0.0, 0.5, and 0.7); (5) size of primary factor loadings (0.40, 0.55, and 0.70); (6) proportion of observed variables with substantive cross-loadings (0.0%, 12.5%, and 25.0%); and (7) sample size (300, 500, and 1000). Performance was evaluated through the Omega index, Mean Bias Error (MBE), Mean Absolute Error (MAE), sensitivity, specificity, and mean number of isolated nodes. We also evaluated two other methods, Exploratory Factor Analysis and the Walktrap algorithm modified to consider overlap (EFA-Ov and Walk-Ov, respectively). The Walk-Ov displayed the best performance across most conditions and is the recommended option to identify communities with overlapping symptoms in psychological networks.

How ready is speech-to-text for psychological language research? Evaluating the validity of AI-generated English transcripts for analyzing free-spoken responses in younger and older adults

For the longest time, the gold standard in preparing spoken language corpora for text analysis in psychology was using human transcription. However, such standard comes at extensive cost, and creates barriers to quantitative spoken language analysis that recent advances in speech-to-text technology could address. The current study quantifies the accuracy of AI-generated transcripts compared to human-corrected transcripts across younger (n = 100) and older (n = 92) adults and two spoken language tasks. Further, it evaluates the validity of Linguistic Inquiry and Word Count (LIWC)-features extracted from these two kinds of transcripts, as well as transcripts specifically prepared for LIWC analyses via tagging. We find that overall, AI-generated transcripts are highly accurate with a word error rate of 2.50% to 3.36%, albeit being slightly less accurate for younger compared to older adults. LIWC features extracted from either transcripts are highly correlated, while the tagging procedure significantly alters filler word categories. Based on these results, automatic speech-to-text appears to be ready for psychological language research when using spoken language tasks in relatively quiet environments, unless filler words are of interest to researchers.

Enabling analytical power calculations for multilevel models with autocorrelated errors through deriving and approximating the precision matrix

To unravel how within-person psychological processes fluctuate in daily life, and how these processes differ between persons, intensive longitudinal (IL) designs in which participants are repeatedly measured, have become popular. Commonly used statistical models for those designs are multilevel models with autocorrelated errors. Substantive hypotheses of interest are then typically investigated via statistical hypotheses tests for model parameters of interest. An important question in the design of such IL studies concerns the determination of the number of participants and the number of measurements per person needed to achieve sufficient statistical power for those statistical tests. Recent advances in computational methods and software have enabled the computation of statistical power using Monte Carlo simulations. However, this approach is computationally intensive and therefore quite restrictive. To ease power computations, we derive simple-to-use analytical formulas for multilevel models with AR(1) within-person errors. Analytic expressions for a model family are obtained via asymptotic approximations of all sample statistics in the precision matrix of the fixed effects. To validate this analytical approach to power computation, we compare it to the simulation-based approach via a series of Monte Carlo simulations. We find comparable performances making the analytic approach a useful tool for researchers that can drastically save them time and resources.

Estimating the proportions and latencies of reaction time outliers: A pooling method and case study of lexical decision tasks

A methodological problem in most reaction time (RT) studies is that some measured RTs may be outliers-that is, they may be very fast or very slow for reasons unconnected to the task-related processing of interest. Numerous ad hoc methods have been suggested to discriminate between such outliers and the valid RTs of interest, but it is extremely difficult to determine how well these methods work in practice because virtually nothing is known about the actual characteristics of outliers in real RT datasets. This article proposes a new method of pooling cumulative distribution function values for examining empirical RT distributions to assess both the proportions of outliers and their latencies relative to those of the valid RTs. As the method is developed, its strengths and weaknesses are examined using simulations based on previously suggested ad hoc models for RT outliers with particular assumed proportions and distributions of valid RTs and outliers. The method is then applied to several large RT datasets from lexical decision tasks, and the results provide the first empirically based description of outlier RTs. For these datasets, fewer than 1% of the RTs seem to be outliers, and the median outlier latency appears to be approximately 4-6 standard deviations of RT above the mean of the valid RT distribution.

Knowing what to know: Implications of the choice of prior distribution on the behavior of adaptive design optimization

Adaptive design optimization (ADO) is a state-of-the-art technique for experimental design (Cavagnaro et al., 2010). ADO dynamically identifies stimuli that, in expectation, yield the most information about a hypothetical construct of interest (e.g., parameters of a cognitive model). To calculate this expectation, ADO leverages the modeler's existing knowledge, specified in the form of a prior distribution. Informative priors align with the distribution of the focal construct in the participant population. This alignment is assumed by ADO's internal assessment of expected information gain. If the prior is instead misinformative, i.e., does not align with the participant population, ADO's estimates of expected information gain could be inaccurate. In many cases, the true distribution that characterizes the participant population is unknown, and experimenters rely on heuristics in their choice of prior and without an understanding of how this choice affects ADO's behavior. Our work introduces a mathematical framework that facilitates investigation of the consequences of the choice of prior distribution on the efficiency of experiments designed using ADO. Through theoretical and empirical results, we show that, in the context of prior misinformation, measures of expected information gain are distinct from the correctness of the corresponding inference. Through a series of simulation experiments, we show that, in the case of parameter estimation, ADO nevertheless outperforms other design methods. Conversely, in the case of model selection, misinformative priors can lead inference to favor the wrong model, and rather than mitigating this pitfall, ADO exacerbates it.

A new sample-size planning approach for person-specific VAR(1) studies: Predictive accuracy analysis

Researchers increasingly study short-term dynamic processes that evolve within single individuals using N = 1 studies. The processes of interest are typically captured by fitting a VAR(1) model to the resulting data. A crucial question is how to perform sample-size planning and thus decide on the number of measurement occasions that are needed. The most popular approach is to perform a power analysis, which focuses on detecting the effects of interest. We argue that performing sample-size planning based on out-of-sample predictive accuracy yields additional important information regarding potential overfitting of the model. Predictive accuracy quantifies how well the estimated VAR(1) model will allow predicting unseen data from the same individual. We propose a new simulation-based sample-size planning method called predictive accuracy analysis (PAA), and an associated Shiny app. This approach makes use of a novel predictive accuracy metric that accounts for the multivariate nature of the prediction problem. We showcase how the values of the different VAR(1) model parameters impact power and predictive accuracy-based sample-size recommendations using simulated data sets and real data applications. The range of recommended sample sizes is smaller for predictive accuracy analysis than for power analysis.

Measuring mind wandering with experience sampling during task performance: An item response theory investigation

The tendency for individuals to mind wander is often measured using experience sampling methods in which probe questions embedded within computerized cognitive tasks attempt to catch episodes of off-task thought at random intervals during task performance. However, mind-wandering probe questions and response options are often chosen ad hoc and vary between studies with extant little guidance as to the psychometric consequences of these methodological decisions. In the present study, we examined the psychometric properties of several common approaches for assessing mind wandering using methods from item response theory (IRT). IRT latent modeling demonstrated that measurement information was generally distributed across the range of trait estimates according to when probes were presented in time. Probes presented earlier in time provided more information about individuals with greater tendency to mind wandering than probes presented later. Furthermore, mind-wandering ratings made on a continuous scale or using multiple categorical rating options provided more information about individuals' latent mind-wandering tendency - across a broader range of the trait continuum - than ratings dichotomized into on-task and off-task categories. In addition, IRT provided evidence that reports of "task-related thoughts" contribute to the task-focused dimension of the construct continuum, providing justification for studies conceptualizing these responses as a kind of task-related focus. Together, we hope these findings will help guide researchers hoping to maximize the measurement precision of their mind wandering assessment procedures.

Detecting inattentive respondents by machine learning: A generic technique that substitutes for the directed questions scale and compensates for its shortcomings

Web surveys are often used to collect data for psychological research. However, the inclusion of many inattentive respondents can be a problem. Various methods for detecting inattentive respondents have been proposed, most of which require the inclusion of additional items in the survey for detection or the calculation of variables for detection after data collection. This study proposes a method for detecting inattentive respondents in web surveys using machine learning. The method requires only the collection of response time and the inclusion of a Likert scale, eliminating the need to include special detection items in the survey. Based on data from 16 web surveys, a method was developed using predictor variables not included in existing methods. While previous machine learning methods for detecting inattentive respondents can only be applied to the same surveys as the data on which the models were developed, the proposed model is generic and can be applied to any questionnaire as long as response time is available, and a Likert scale is included. In addition, the proposed method showed partially higher accuracy than existing methods.

What every intensivist should know about: The value of limitations in clinical research

Not applicable.

Examining network structure of impulsivity and depression in adolescents and young adults: A two-sample study

BACKGROUND

Depression ranks as one of the top five contributors to ill health in youth, the most formative period in life. Extensive research has highlighted the significant role of impulsivity in understanding depression. However, there has been limited exploration into how each dimension of impulsivity uniquely affect depressive symptoms, especially across crucial developmental stages like adolescence and young adulthood.

METHODS

This study investigates the unique relationships between impulsivity (assessed by the short UPPS-P scale) and depression (assessed by the Patient Health Questionnaire-9) via network analysis. We analysed data from a total of 2296 participants, comprising 858 adolescents aged 14-17 years and 1438 young adults aged 18-25 years, to estimate both a combined network and age-group specific networks. Key features of the networks, including their structure, global connectivity, and bridge nodes, were compared.

RESULTS

The results indicated that age differentially impacts individual depression symptoms, both directly and indirectly, via impulsivity dimensions. The comparison test revealed consistent network structures between the two age groups, with several robust pathways, such as lack of perseverance to concentration difficulties, sensation seeking to suicidal ideation, and negative urgency to feelings of worthlessness. Negative urgency and lack of perseverance were identified as bridge nodes across the two networks.

LIMITATIONS

The study employed a cross-sectional design, which limits the ability to estimate causal or temporal relationships.

CONCLUSIONS

The current findings highlight the significance of tailoring intervention strategies to individual symptom profiles and assessing negative urgency and lack of perseverance as potential early targets for depression among youth.

Weight stigma as a stressor: A preliminary multi-wave, longitudinal study testing the biobehavioral pathways of the cyclic obesity/weight-based stigma (COBWEBS) model

Higher weight individuals often face significant weight stigma. According to the Cyclic Obesity/Weight-Based Stigma (COBWEBS) model, weight stigma operates as a stressor that increases the stress hormone cortisol and promotes comfort eating, thus resulting in weight gain. Such weight gain is harmful as it exposes individuals to further stigmatization. Thus far, no study has yet tested the mechanistic pathways of the COBWEBS model and prospective longitudinal studies are severely lacking. To fill this gap, the current study tested the biobehavioral pathways of the COBWEBS model using a 4-wave yearlong longitudinal study comprising 348 higher weight individuals. Using a structural equation modeling framework, we tested three cross lagged panel models for the putative mediator, comfort eating. The models examined either synchronous and/or lagged effects across weight stigma, perceived stress, comfort eating, weight, and future weight stigma. The best fitting model revealed significant associations between baseline weight stigma, perceived stress, and comfort eating within the same month. However, comfort eating did not significantly predict weight four months later. Weight status and baseline weight stigma both predicted future weight stigma as expected. Additionally, a separate path model with hair cortisol found that weight stigma predicted perceived stress four months later, but stress did not predict aggregate cortisol levels from months 10 and 11. Hair cortisol also did not predict later weight. This preliminary work lays the foundation for identifying modifiable targets of weight stigma, thereby offering potential avenues to reduce weight stigma's harm on higher weight individuals.

Co-occurring dominance and ideal point processes: A general IRTree framework for multidimensional item responding

Responding to rating scale items is a multidimensional process, since not only the substantive trait being measured but also additional personal characteristics can affect the respondents' category choices. A flexible model class for analyzing such multidimensional responses are IRTree models, in which rating responses are decomposed into a sequence of sub-decisions. Different response processes can be involved in item responding both sequentially across those sub-decisions and as co-occurring processes within sub-decisions. In the previous literature, modeling co-occurring processes has been exclusively limited to dominance models, where higher trait levels are associated with higher expected scores. However, some response processes may rather follow an ideal point rationale, where the expected score depends on the proximity of a person's trait level to the item's location. Therefore, we propose a new multidimensional IRT model of co-occurring dominance and ideal point processes (DI-MIRT model) as a flexible framework for parameterizing IRTree sub-decisions with multiple dominance processes, multiple ideal point processes, and combinations of both. The DI-MIRT parameterization opens up new application areas for the IRTree model class and allows the specification of a wide range of theoretical assumptions regarding the cognitive processing of item responding. A simulation study shows that IRTree models with DI-MIRT parameterization provide excellent parameter recovery and accurately reflect co-occurring dominance and ideal point processes. In addition, a clear advantage over traditional IRTree models with purely sequential processes is demonstrated. Two application examples from the field of response style analysis highlight the benefits of the general IRTree framework under real-world conditions.

Comparing type 1 and type 2 error rates of different tests for heterogeneous treatment effects

Psychologists are increasingly interested in whether treatment effects vary in randomized controlled trials. A number of tests have been proposed in the causal inference literature to test for such heterogeneity, which differ in the sample statistic they use (either using the variance terms of the experimental and control group, their empirical distribution functions, or specific quantiles), and in whether they make distributional assumptions or are based on a Fisher randomization procedure. In this manuscript, we present the results of a simulation study in which we examine the performance of the different tests while varying the amount of treatment effect heterogeneity, the type of underlying distribution, the sample size, and whether an additional covariate is considered. Altogether, our results suggest that researchers should use a randomization test to optimally control for type 1 errors. Furthermore, all tests studied are associated with low power in case of small and moderate samples even when the heterogeneity of the treatment effect is substantial. This suggests that current tests for treatment effect heterogeneity require much larger samples than those collected in current research.

Investigating motivations to eat: Refining and validating a framework of Eating Behaviour Traits in dieters and the general population

Due to relationships between diet and health including obesity, there is a need to examine the explanatory power of factors that motivate people to (over or under) eat. In a previous investigation, a four-factor subscale-based model of eating behaviour traits (EBTs) was developed which identified individual differences in psychological factors influencing motivations to eat and some residual uncertainties. The current study used a data-driven and theory-driven approach, including individual items to refine and extend previous EBT models. The aim was to examine and validate the domain structure of a framework for EBTs. The analysis used two samples including a representative sample of the UK population (n = 2010, 51% female, 49% male, 18-88 years), and members of a weight management program (n = 2317, 96.6% female, 2.8% male, 21-84 years), who completed 5 questionnaires including 10 EBTs. The results found some support for a 6-factor model, encompassing reactive eating, negative emotional eating, positive emotional eating, restricted eating, homeostatic eating, and body-food choice congruence (data-driven model) or eating for health (theory-driven model). There were differences between the data-driven model and the theory-driven model regarding the 6th factor. Additionally, the data-driven model did not distinguish between eating for pleasure and reactive eating. The models demonstrated that the eating behaviour factors were significantly associated with BMI category. Overall, this research contributes to a more structured understanding of the dimensions of motivation underlying EBTs, emphasising the utility of this framework for identifying at-risk individuals and tailoring interventions to meet specific individual needs.

Passive sensing of smartphone use, physical activity and sedentary behavior among adolescents and young adults during the COVID-19 pandemic

The research that links excessive screen time to adverse health outcomes is based on self-reported screen use. Few studies have documented how passively-sensed smartphone and app use relate to health behaviors like activity and sitting. Furthermore, they have not considered that daily fluctuations in smartphone/app use may have different relationships to these behaviors than a person's usual smartphone/app use. This study evaluated whether physical activity or sedentary (sitting) behavior are associated with either smartphone screen time or specific smartphone app use by adolescents and young adults during the COVID-19 pandemic. Adolescents and young adults aged 13-29 years wore activPAL4 micro activity monitors while their smartphones logged daily screen time and app use durations for nine days. Data were collected in 2020-2021 and analyzed in 2022-2023. Participants (N = 125) had a mean (SD) age of 19.7 (4.3) years. Participants' usual smartphone screen time was negatively associated with daily step counts. Daily deviations in smartphone screen time were negatively associated with daily step counts and moderate-vigorous physical activity durations. Time spent on Instagram, YouTube and, to a lesser extent, TikTok were linked with reduced activity levels. Daily sedentary behavior was not associated with usual or daily screen time. Interventions to promote physical activity during the transition into adulthood may benefit from limiting excessive smartphone screen time. Specific intervention targets could include limiting use of apps with infinite scrolling feeds algorithmically tuned to maintain user engagement, such as Instagram, YouTube and TikTok.

Best practices for your confirmatory factor analysis: A JASP and lavaan tutorial

Confirmatory factor analysis (CFA) is a fundamental method for evaluating the internal structural validity of measurement instruments. In most CFA applications, the measurement model serves as a means to an end rather than an end in itself. To select the appropriate model, prior validity evidence is crucial, and items are typically assessed on an ordinal scale, which has been used in the applied social sciences. However, textbooks on structural equation modeling (SEM) often overlook this specific case, focusing on applications estimable using maximum likelihood (ML) instead. Unfortunately, several popular commercial SEM software packages lack suitable solutions for handling this 'typical CFA', leading to confusion and suboptimal decision-making when conducting CFA in this context. This article conceptually contributes to this ongoing discussion by presenting a set of guidelines for conducting a typical CFA, drawing from recent empirical research. We provide a practical contribution by introducing and developing a tutorial example within the JASP and lavaan software platforms. Supplementary materials such as videos, files, and scripts are freely available.