Gender differences in Dutch research funding over time: A statistical investigation of the innovation scheme 2012-2021.

BACKGROUND: In 2015, the Dutch research council, NWO, took measures to combat gender bias disadvantaging female applicants in a popular three-tiered funding scheme called the Talent Programme. The innovation scheme consists of three grants for different career stages, called Veni, Vidi and Vici. OBJECTIVES: This paper studies the question whether or not NWO has been successful in removing gender differences in their funding procedure. METHODS: Using all available data from 2012 onwards of grant applications in the Talent Programme (16,249 applications of which 2,449 received funding), we study whether these measures had an effect using binomial generalized linear models. RESULTS: We find strong statistical evidence of a shift in gender effects in favour of female applicants in the first tier, the Veni (p < .001). Significant gender differences are not found in the two other tiers, the Vidi and Vici schemes. CONCLUSIONS: In recent years, female applicants are more likely to be awarded with a Veni grant than male applicants and this gender gap has increased over time. This suggests that gender differences still exist in the assessment of Talent Programme submissions, albeit in a different direction than a decade ago.

Mixture multilevel vector-autoregressive modeling.

With the rising popularity of intensive longitudinal research, the modeling techniques for such data are increasingly focused on individual differences. Here we present mixture multilevel vector-autoregressive modeling, which extends multilevel vector-autoregressive modeling by including a mixture, to identify individuals with similar traits and dynamic processes. This exploratory model identifies mixture components, where each component refers to individuals with similarities in means (expressing traits), autoregressions, and cross-regressions (expressing dynamics), while allowing for some interindividual differences in these attributes. Key issues in modeling are discussed, where the issue of centering predictors is examined in a small simulation study. The proposed model is validated in a simulation study and used to analyze the affective data from the COGITO study. These data consist of samples for two different age groups of over 100 individuals each who were measured for about 100 days. We demonstrate the advantage of exploratory identifying mixture components by analyzing these heterogeneous samples jointly. The model identifies three distinct components, and we provide an interpretation for each component motivated by developmental psychology. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

The dynamics of social activation and suspiciousness in individuals at ultra-high risk for psychosis.

INTRODUCTION: Social functioning is often impaired during the ultra-high risk (UHR) phase for psychosis, but group-level studies regarding the role of social functioning in transition to psychosis are inconsistent. Exploring the inter-individual differences which underlie the association between social functioning and psychotic symptoms in this phase could yield new insights. OBJECTIVE: To examine the idiographic and dynamic association between social activation and suspiciousness in individuals at UHR for psychosis using time-series analysis. METHODS: Twenty individuals at UHR for psychosis completed a diary application every evening for 90 days. Two items on social activation (quantity: 'time spent alone' and quality: 'feeling supported') and two items on suspiciousness ('feeling suspicious' and 'feeling disliked') were used. Time series (T = 90) of each individual were analyzed using vector auto regression analysis (VAR), to estimate the lagged (over 1 day) effect of social activation on suspiciousness, and vice versa, as well as their contemporaneous associations. RESULTS: Heterogeneous person-specific associations between social activation and suspiciousness were found in terms of strength, direction and temporal aspects. CONCLUSIONS: The association between social activation and suspiciousness differs amongst individuals who are at UHR for psychosis. These findings underline the importance of tailoring psychosocial interventions to the individual. Future studies may examine whether using results of single-subject studies in clinical practice to personalize treatment goals leads to better treatment outcomes.

Congruency of multimodal data-driven personalization with shared decision-making for StayFine: individualized app-based relapse prevention for anxiety and depression in young people.

Tailoring interventions to the individual has been hypothesized to improve treatment efficacy. Personalization of target-specific underlying mechanisms might improve treatment effects as well as adherence. Data-driven personalization of treatment, however, is still in its infancy, especially concerning the integration of multiple sources of data-driven advice with shared decision-making. This study describes an innovative type of data-driven personalization in the context of StayFine, a guided app-based relapse prevention intervention for 13- to 21-year-olds in remission of anxiety or depressive disorders (n = 74). Participants receive six modules, of which three are chosen from five optional modules. Optional modules are Enhancing Positive Affect, Behavioral Activation, Exposure, Sleep, and Wellness. All participants receive Psycho-Education, Cognitive Restructuring, and a Relapse Prevention Plan. The personalization approach is based on four sources: (1) prior diagnoses (diagnostic interview), (2) transdiagnostic psychological factors (online self-report questionnaires), (3) individual symptom networks (ecological momentary assessment, based on a two-week diary with six time points per day), and subsequently, (4) patient preference based on shared decision-making with a trained expert by experience. This study details and evaluates this innovative type of personalization approach, comparing the congruency of advised modules between the data-driven sources (1-3) with one another and with the chosen modules during the shared decision-making process (4). The results show that sources of data-driven personalization provide complementary advice rather than a confirmatory one. The indications of the modules Exposure and Behavioral Activation were mostly based on the diagnostic interview, Sleep on the questionnaires, and Enhancing Positive Affect on the network model. Shared decision-making showed a preference for modules improving positive concepts rather than combating negative ones, as an addition to the data-driven advice. Future studies need to test whether treatment outcomes and dropout rates are improved through personalization.

An experience sampling study on the links between daily teacher self-efficacy, need-supportive teaching and student intrinsic motivation.

Introduction: Why are some teachers more successful at motivating students than others? We know from previous literature that teachers' self-efficacy relates to the extent in which they engage in need-supportive teaching in the classroom, which in turn relates to student intrinsic motivation. However, teachers' self-efficacy is hypothesized to be dependent on their previous mastery experiences, e.g., of engaging students in the classroom. This "feedback loop" where the teacher not only influences the student but also the other way around, in a process unfolding over time, can only be investigated empirically with an intensive longitudinal design. This is precisely what we did in the current study. Methods: Secondary school teachers (n = 4) and students (n = 90) participated in an experience sampling study throughout one school year, resulting in a unique dataset with 48-59 repeated measurement points per class. Results: Visual exploration of the time series revealed that teacher self-efficacy can vary substantially from lesson to lesson, with characteristic patterns of stabilization and de-stabilization. We conducted Vector Autoregressive Analysis (VAR) for each of the four cases to test whether, and how, the variables relate to each other over time. We found an "overspill effect" for student motivation, meaning that students' motivation in today's lesson predicts their motivation in tomorrow's lesson. Furthermore, in two cases we found that today's student motivation predicts tomorrow's teacher self-efficacy, but not the other way around.

General Mental Health, Loneliness, and Life Satisfaction in the Context of COVID-19 Policies: A 2-Year Cohort Study in the Netherlands, April 2020-January 2022.

OBJECTIVE: Although the COVID-19 pandemic has affected mental health, understanding who has been affected most and why is incomplete. We sought to understand changes in mental health in the context of transmission numbers and pandemic (social) restrictions and whether changes in mental health varied among population groups. METHODS: We analyzed data from 92 062 people (aged ≥16 years and able to read Dutch) who participated in the Corona Behavioral Unit cohort study at the National Institute for Public Health and the Environment, the Netherlands, from April 17, 2020, through January 25, 2022. Participants self-reported mental well-being through multiple rounds of surveys. We used a multivariable linear mixed-effects model to analyze loneliness, general mental health, and life satisfaction. RESULTS: As strictness of pandemic prevention measures and social restrictions increased, people's feelings of loneliness increased and mental health and life satisfaction decreased. As restrictions were relaxed, loneliness decreased and general mental health improved. Younger people (aged 16-24 y) versus older people (aged ≥40 y), people with low (vs high) education levels, and people living alone (vs living together) were more likely to have negative well-being outcomes. We observed that trajectories over time differed considerably only by age, with participants aged 16-24 years affected substantially more than participants aged ≥40 years by pandemic social restrictions. These patterns were consistent across multiple waves of SARS-CoV-2 infection. CONCLUSIONS: Our findings suggest that the social restrictions imposed by the Dutch government during the study period were associated with reduced mental well-being, especially among younger people. However, people appeared resilient as they recovered during periods when restrictions were relaxed. Monitoring and supporting well-being, in particular to reduce loneliness, may help younger people during periods of intense social restrictions.

A comprehensive model framework for between-individual differences in longitudinal data.

Across different fields of research, the similarities and differences between various longitudinal models are not always eminently clear due to differences in data structure, application area, and terminology. Here we propose a comprehensive model framework that will allow simple comparisons between longitudinal models, to ease their empirical application and interpretation. At the within-individual level, our model framework accounts for various attributes of longitudinal data, such as growth and decline, cyclical trends, and the dynamic interplay between variables over time. At the between-individual level, our framework contains continuous and categorical latent variables to account for between-individual differences. This framework encompasses several well-known longitudinal models, including multilevel regression models, growth curve models, growth mixture models, vector-autoregressive models, and multilevel vector-autoregressive models. The general model framework is specified and its key characteristics are illustrated using famous longitudinal models as concrete examples. Various longitudinal models are reviewed and it is shown that all these models can be united into our comprehensive model framework. Extensions to the model framework are discussed. Recommendations for selecting and specifying longitudinal models are made for empirical researchers who aim to account for between-individual differences. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Visualizing Uncertainty to Promote Clinicians' Understanding of Measurement Error.

Measurement error is an inherent part of any test score. This uncertainty is generally communicated in ways that can be difficult to understand for clinical practitioners. In this empirical study, we evaluate the impact of several communication formats on the interpretation of measurement accuracy and its influence on the decision-making process in clinical practice. We provided 230 clinical practitioners with score reports in five formats: textual, error bar, violin plot, diamond plot, and quantile dot plot. We found that quantile dot plots significantly increased accuracy in the assessment of measurement uncertainty compared with other formats. However, a direct relation between visualization format and decision quality could not be found. Although traditional confidence intervals and error bars were favored by many participants due to their familiarity, responses revealed several misconceptions that make the suitability of these formats for communicating uncertainty questionable. Our results indicate that new visualization formats can successfully reduce errors in interpretation.

A Network Approach to Trauma, Dissociative Symptoms, and Psychosis Symptoms in Schizophrenia Spectrum Disorders.

BACKGROUND: Dissociative experiences commonly occur in schizophrenia spectrum disorders (SSD). Yet little is known about how dissociative experiences in SSD are related to SSD symptoms. Accordingly, we investigated the relations between dissociative experiences and SSD symptoms, focusing on symptoms bridging these 2 symptom clusters as well as their relation to reported trauma history. STUDY DESIGN: Network analyses were conducted on the responses of 248 individuals with an SSD who enrolled from multiple mental health centers in The Netherlands. Dissociative experiences were assessed via the Dissociative Experience Scale, SSD symptoms using the Positive and Negative Syndrome Scale, and trauma history through the Trauma History Questionnaire. STUDY RESULTS: The results indicated that dissociative symptoms in SSD are mostly independent of other symptoms, but that emotional distress bridges between the dissociative and SSD symptom clusters. Furthermore, results revealed associations between positive and negative SSD symptoms and trauma through emotional distress, whereas dissociative symptoms remained relatively isolated. CONCLUSION: Because SSD symptoms and dissociative experiences clustered relatively independent from each other, our findings promote the idea of tailored treatment approaches for individuals with an SSD with frequent dissociative experiences, specifically targeting these symptoms.

Regaining control of your emotions? Investigating the mechanisms underlying effects of cognitive control training for remitted depressed patients.

Studies suggest that cognitive control training shows potential as a preventive intervention for depression. At the same time, little is known regarding the mechanisms underlying effects of cognitive control training. Informed by theoretical frameworks of cognitive risk for recurrent depression (De Raedt & Koster, 2010; Siegle et al., 2007), the current study sought to model direct effects of cognitive control training on the complex interplay between affect, emotion regulation, residual symptomatology, and resilience in a sample of remitted depressed patients (n = 92). Combining a 4-week experience sampling procedure with an experimental manipulation of cognitive control, we observed beneficial effects of cognitive control training on deployment of rumination. In addition, we obtained evidence for the causal involvement of cognitive control in efficacy of emotion regulation. In contrast to our expectations, cognitive control training did not exert immediate effects on residual symptomatology or resilience when compared with an active control condition, nor did cognitive control training impact the complex interplay between these variables. Overall, immediate effects of cognitive control training on functioning in daily life were limited. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Personalised app-based relapse prevention of depressive and anxiety disorders in remitted adolescents and young adults: a protocol of the StayFine RCT.

INTRODUCTION: Youth in remission of depression or anxiety have high risks of relapse. Relapse prevention interventions may prevent chronicity. Aim of the study is therefore to (1) examine efficacy of the personalised StayFine app for remitted youth and (2) identify high-risk groups for relapse and resilience. METHOD AND ANALYSIS: In this Dutch single-blind parallel-group randomised controlled trial, efficacy of app-based monitoring combined with guided app-based personalised StayFine intervention modules is assessed compared with monitoring only. In both conditions, care as usual is allowed. StayFine modules plus monitoring is hypothesised to be superior to monitoring only in preventing relapse over 36 months. Participants (N=254) are 13-21 years and in remission of depression or anxiety for >2 months. Randomisation (1:1) is stratified by previous treatment (no treatment vs treatment) and previous episodes (1, 2 or >3 episodes). Assessments include diagnostic interviews, online questionnaires and monitoring (ecological momentary assessment with optional wearable) after 0, 4, 12, 24 and 36 months. The StayFine modules are guided by certified experts by experience and based on preventive cognitive therapy and ingredients of cognitive behavioural therapy. Personalisation is based on shared decision-making informed by baseline assessments and individual symptom networks. Time to relapse (primary outcome) is assessed by the Kiddie Schedule for Affective Disorders and Schizophrenia-lifetime version diagnostic interview. Intention-to-treat survival analyses will be used to examine the data. Secondary outcomes are symptoms of depression and anxiety, number and duration of relapses, global functioning, and quality of life. Mediators and moderators will be explored. Exploratory endpoints are monitoring and wearable outcomes. ETHICS, FUNDING AND DISSEMINATION: The study was approved by METC Utrecht and is funded by the Netherlands Organisation for Health Research and Development (636310007). Results will be submitted to peer-reviewed scientific journals and presented at (inter)national conferences. TRIAL REGISTRATION NUMBER: NCT05551468; NL8237.

Misleading graphs in context: Less misleading than expected.

Misleading graphs are a source of misinformation that worry many experts. Especially people with a low graph literacy are thought to be persuaded by graphs that misrepresent the underlying data. But we know little about how people interpret misleading graphs and how these graphs influence their opinions. In this study we focus on the effect of truncating the y-axis for a line chart which exaggerates an upgoing trend. In a randomized controlled trial, we showed participants either a normal or a misleading chart, and we did so in two different contexts. After they had seen the graphs, we asked participants their opinion on the trend and to give an estimation of the increase. Finally we measured their graph literacy. Our results show that context is the only significant factor in opinion-forming; the misleading graph and graph literacy had no effect. None of these factors had a significant impact on estimations for the increase. These results show that people might be less susceptible to misleading graphs than we thought and that context has more impact than a misleading y-axis.

Species identification skills predict in-depth knowledge about species.

To raise biodiversity awareness effectively, communicators should be aware of knowledge levels in their audiences. Species identification skills have been used in the past as a measure of what people know about species, yet it is not known whether they serve as good indicators. To study the link between species identification and in-depth species knowledge, we presented an animal knowledge test to an online audience of over 7,000 Dutch adults, and used correlation and regression analyses to determine the extent to which species identification predicts in-depth knowledge about species' origin, habitat, diet, and behavior. We found that in-depth knowledge was higher in those who correctly identified species as compared with those who did not correctly identify species, for all four types of in-depth knowledge. Moreover, as compared to alternative variables (work, age, gender, and educational level), species identification was by far the best predictor for in-depth knowledge about species. However, species identification levels were generally higher than levels of in-depth knowledge, and knowledge gaps and misconceptions were uncovered. The results confirm the value of species identification tests, but also highlight limitations and challenges that should be taken into account when establishing knowledge levels and communicating biodiversity.

Psychopathological networks: Theory, methods and practice.

In recent years, network approaches to psychopathology have sparked much debate and have had a significant impact on how mental disorders are perceived in the field of clinical psychology. However, there are many important challenges in moving from theory to empirical research and clinical practice and vice versa. Therefore, in this article, we bring together different points of view on psychological networks by methodologists and clinicians to give a critical overview on these challenges, and to present an agenda for addressing these challenges. In contrast to previous reviews, we especially focus on methodological issues related to temporal networks. This includes topics such as selecting and assessing the quality of the nodes in the network, distinguishing between- and within-person effects in networks, relating items that are measured at different time scales, and dealing with changes in network structures. These issues are not only important for researchers using network models on empirical data, but also for clinicians, who are increasingly likely to encounter (person-specific) networks in the consulting room.

Mindful yoga intervention as add-on to treatment as usual for young women with major depressive disorder: Results from a randomized controlled trial.

OBJECTIVE: To examine the added value of a 9-week mindful yoga intervention (MYI) as add-on to treatment as usual (TAU) in reducing depression for young women (18-34 years) with major depressive disorder (MDD). METHOD: Randomized controlled trial (RCT; n = 171) comparing TAU + MYI with TAU-only. Assessments were at baseline, postintervention, and at 6- and 12-month follow-up. Primary outcome measures were clinician-rated and self-reported symptoms of depression, together with a diagnostic interview to establish MDD diagnosis that was restricted to the baseline and 12-month follow-up assessments. Quality of life in various domains was assessed as secondary outcome measure. As potential mediators for treatment efficacy, we included self-report measures of rumination, self-criticism, self-compassion, intolerance of uncertainty, perceived body awareness and dispositional mindfulness, together with behavioral measures of attentional bias (AB) and depression-related self-associations. RESULTS: Adding MYI to TAU did not lead to greater reduction of depression symptoms, lower rate of MDD diagnosis or increase in quality of life in various domains of functioning at post and follow-up assessments. There were no indirect effects through any of the potential mediators, with the exception of self-compassion. CONCLUSION: Adding MYI to TAU appeared not more efficacious than TAU-only in reducing depression symptoms in young women. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Consensus-based guidance for conducting and reporting multi-analyst studies.

Any large dataset can be analyzed in a number of ways, and it is possible that the use of different analysis strategies will lead to different results and conclusions. One way to assess whether the results obtained depend on the analysis strategy chosen is to employ multiple analysts and leave each of them free to follow their own approach. Here, we present consensus-based guidance for conducting and reporting such multi-analyst studies, and we discuss how broader adoption of the multi-analyst approach has the potential to strengthen the robustness of results and conclusions obtained from analyses of datasets in basic and applied research.

The Visual Dictionary of Antimicrobial Stewardship, Infection Control, and Institutional Surveillance Data.

Objectives: Data and data visualization are integral parts of (clinical) decision-making in general and stewardship (antimicrobial stewardship, infection control, and institutional surveillance) in particular. However, systematic research on the use of data visualization in stewardship is lacking. This study aimed at filling this gap by creating a visual dictionary of stewardship through an assessment of data visualization (i.e., graphical representation of quantitative information) in stewardship research. Methods: A random sample of 150 data visualizations from published research articles on stewardship were assessed (excluding geographical maps and flowcharts). The visualization vocabulary (content) and design space (design elements) were combined to create a visual dictionary. Additionally, visualization errors, chart junk, and quality were assessed to identify problems in current visualizations and to provide improvement recommendations. Results: Despite a heterogeneous use of data visualization, distinct combinations of graphical elements to reflect stewardship data were identified. In general, bar (n = 54; 36.0%) and line charts (n = 42; 28.1%) were preferred visualization types. Visualization problems comprised color scheme mismatches, double y-axis, hidden data points through overlaps, and chart junk. Recommendations were derived that can help to clarify visual communication, improve color use for grouping/stratifying, improve the display of magnitude, and match visualizations to scientific standards. Conclusion: Results of this study can be used to guide data visualization creators in designing visualizations that fit the data and visual habits of the stewardship target audience. Additionally, the results can provide the basis to further expand the visual dictionary of stewardship toward more effective visualizations that improve data insights, knowledge, and clinical decision-making.

Seven steps toward more transparency in statistical practice.

We argue that statistical practice in the social and behavioural sciences benefits from transparency, a fair acknowledgement of uncertainty and openness to alternative interpretations. Here, to promote such a practice, we recommend seven concrete statistical procedures: (1) visualizing data; (2) quantifying inferential uncertainty; (3) assessing data preprocessing choices; (4) reporting multiple models; (5) involving multiple analysts; (6) interpreting results modestly; and (7) sharing data and code. We discuss their benefits and limitations, and provide guidelines for adoption. Each of the seven procedures finds inspiration in Merton's ethos of science as reflected in the norms of communalism, universalism, disinterestedness and organized scepticism. We believe that these ethical considerations-as well as their statistical consequences-establish common ground among data analysts, despite continuing disagreements about the foundations of statistical inference.

A cross-sectional study of lactation room quality and Dutch working mothers' satisfaction, perceived ease of, and perceived support for breast milk expression at work.

BACKGROUND: The challenge of combining professional work and breastfeeding is a key reason why women choose not to breastfeed or to stop breastfeeding early. We posited that having access to a high-quality lactation room at the workplace could influence working mothers' satisfaction and perceptions related to expressing breast milk at work, which could have important longer term consequences for the duration of breastfeeding. Specifically, we aimed to (1) develop a checklist for assessing the quality of lactation rooms and (2) explore how lactation room quality affects lactating mothers' satisfaction and perceptions. Drawing on social ecological insights, we hypothesized that the quality of lactation rooms (operationalized as any space used for expressing milk at work) would be positively related to mothers' satisfaction with the room, perceived ease of, and perceived support for milk expression at work. METHODS: We conducted two studies. In Study 1 we developed a lactation room quality checklist (LRQC) and assessed its reliability twice, using samples of 33 lactation rooms (Study 1a) and 31 lactation rooms (Study 1b). Data were collected in the Northern part of the Netherlands (between December 2016 and April 2017). Study 2 comprised a cross-sectional survey of 511 lactating mothers, working in a variety of Dutch organizations. The mothers were recruited through the Facebook page of a popular Dutch breastfeeding website. They completed online questionnaires containing the LRQC and measures aimed at assessing their satisfaction and perceptions related to milk expression at work (in June and July 2017). RESULTS: The LRQC was deemed reliable and easy to apply in practice. As predicted, we found that objectively assessed higher-quality lactation rooms were associated with increased levels of satisfaction with the lactation rooms, perceived ease of milk expression at work, and perceived support from supervisors and co-workers for expressing milk in the workplace. CONCLUSIONS: The availability of a high-quality lactation room could influence mothers' decisions regarding breast milk expression at work and the commencement and/or continuation of breastfeeding. Future studies should explore whether and how lactation room quality affects breastfeeding choices, and which aspects are most important to include in lactation rooms.

Bayesian Gaussian distributional regression models for more efficient norm estimation.

A test score on a psychological test is usually expressed as a normed score, representing its position relative to test scores in a reference population. These typically depend on predictor(s) such as age. The test score distribution conditional on predictors is estimated using regression, which may need large normative samples to estimate the relationships between the predictor(s) and the distribution characteristics properly. In this study, we examine to what extent this burden can be alleviated by using prior information in the estimation of new norms with Bayesian Gaussian distributional regression. In a simulation study, we investigate to what extent this norm estimation is more efficient and how robust it is to prior model deviations. We varied the prior type, prior misspecification and sample size. In our simulated conditions, using a fixed effects prior resulted in more efficient norm estimation than a weakly informative prior as long as the prior misspecification was not age dependent. With the proposed method and reasonable prior information, the same norm precision can be achieved with a smaller normative sample, at least in empirical problems similar to our simulated conditions. This may help test developers to achieve cost-efficient high-quality norms. The method is illustrated using empirical normative data from the IDS-2 intelligence test.