Mariëlle Zondervan-Zwijnenburg

The desire of older adults to remain in their homes despite health challenges, as well as the aging population and rising health care costs, has prompted a shift from institutional care to home-based care. This change places district nurses at the forefront of supporting older adults’ independent functioning. However, limited evidence exists on effective interventions by district nurses and using routine patient care data to enhance independence. To address this, a user-centered design approach was applied to develop and validate a dashboard providing district nurses with actionable insights to improve care quality and promote independence. The process followed a 4-phase iterative framework: user and domain analysis through focus groups and interviews, data preparation with anonymized patient data sets, dashboard prototyping, and evaluation through feedback from nurses and IT specialists. Iterative end-user involvement with district nurses, nursing assistants, researchers, and managers ensured relevance and usability. The final prototype includes functionalities for monitoring changes over time and linking problems to practical aids and intervention targets. The dashboard, tailored for district nurses, provides a tool to monitor and improve the independent functioning of older adults. Future research will evaluate the dashboard’s impact on nursing interventions and its effectiveness in enhancing independent functioning.

The last 25 years have shown a steady increase in attention for the Bayes factor as a tool for hypothesis evaluation and model selection. The present review highlights the potential of the Bayes factor in psychological research. We discuss six types of applications: Bayesian evaluation of point null, interval, and informative hypotheses, Bayesian evidence synthesis, Bayesian variable selection and model averaging, and Bayesian evaluation of cognitive models. We elaborate what each application entails, give illustrative examples, and provide an overview of key references and software with links to other applications. The article is concluded with a discussion of the opportunities and pitfalls of Bayes factor applications and a sketch of corresponding future research lines. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Prior studies have indicated that prosocial behavior might be a protective factor for developing internalizing and externalizing behavioral problems. However, little research has been conducted on within-person changes of prosocial behavior and behavioral problems over time. With random-intercept cross-lagged panel models (RI-CLPMs), the current study analyzed longitudinal associations between prosocial behavior and behavioral problems in two twin cohorts (98% Western European): in early childhood (age M = 4.77, SD = .58, 52% girls, N = 440) and middle childhood (age M = 7.94, SD = .67, 51% girls, N = 512). To obtain robust results, two parental reported questionnaires and an observational task were used as prosocial behavior assessments. In line with the literature, we found a significant between-person association between externalizing behavior and parent reported prosocial behavior in middle childhood, but not in early childhood. Some evidence indicated that changes in externalizing problems affect later prosocial behavior in middle childhood. Overall, however, the RI-CLPMs provided most support for the hypothesis that within-person changes in prosocial behavior are not related to within-person changes in behavioral problems. Thus, our findings did not support the hypothesis that increased prosocial behavior directly results in decreased behavioral problems, but emphasizes the need to take into account the multifaceted nature of prosocial behavior. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

In the current study, we introduce the prior predictive p-value as a method to test replication of an analysis of variance (ANOVA). The prior predictive p-value is based on the prior predictive distribution. If we use the original study to compose the prior distribution, then the prior predictive distribution contains datasets that are expected given the original results. To determine whether the new data resulting from a replication study deviate from the data in the prior predictive distribution, we need to calculate a test statistic for each dataset. We propose to use ̄F, which measures to what degree the results of a dataset deviate from an inequality constrained hypothesis capturing the relevant features of the original study: HRF. The inequality constraints in HRF are based on the findings of the original study and can concern, for example, the ordering of means and interaction effects. The prior predictive p-value consequently tests to what degree the new data deviates from predicted data given the original results, considering the findings of the original study. We explain the calculation of the prior predictive p-value step by step, elaborate on the topic of power, and illustrate the method with examples. The replication test and its integrated power and sample size calculator are made available in an R-package and an online interactive application. As such, the current study supports researchers that want to adhere to the call for replication studies in the field of psychology.

Prior studies have indicated that prosocial behavior might be a protective factor for developing internalizing and externalizing behavioral problems. However, little research has been conducted on within-person changes of prosocial behavior and behavioral problems over time. With random-intercept cross-lagged panel models (RI-CLPMs), the current study analyzed longitudinal associations between prosocial behavior and behavioral problems in two twin cohorts (98% Western European): in early childhood (age M=4.77, SD=0.58, 52% girls, N=440) and middle childhood (age M=7.94, SD=0.67, 51% girls, N=512). To obtain robust results, two parental reported questionnaires and an observational task were used as prosocial behavior assessments. In line with the literature, we found a significant between-person association between externalizing behavior and parent reported prosocial behavior in middle childhood, but not in early childhood. Some evidence indicated that changes in externalizing problems affect later prosocial behavior in middle childhood. Overall, however, the RI-CLPMs provided most support for the hypothesis that within-person changes in prosocial behavior are not related to within-person changes in behavioral problems. Thus, our findings did not support the hypothesis that increased prosocial behavior directly results in decreased behavioral problems, but emphasizes the need to take into account the multifaceted nature of prosocial behavior.

The last 25 years have shown a steady increase in attention for the Bayes factor as a tool for hypothesis evaluation and model selection. The present review highlights the potential of the Bayes factor in psychological research. We discuss six types of applications: Bayesian evaluation of point null, interval, and informative hypotheses, Bayesian evidence synthesis, Bayesian variable selection and model averaging, and Bayesian evaluation of cognitive models. We elaborate what each application entails, give illustrative examples, and provide an overview of key references and software with links to other applications. The paper is concluded with a discussion of the opportunities and pitfalls of Bayes factor applications and a sketch of corresponding future research lines.

The 4th Industrial Revolution has provided several digital platforms through which to disseminate scalable and cost-effective interventions (e.g. Apps and Social media). Instagram, a popular visual-ethnographic social media platform, could be employed to implement and scale interventions aimed at aiding individuals in discovering meaning in life and gratitude through capturing and reflecting upon photographs of meaningful moments. The purpose of this study was to evaluate the long-term effectiveness of a brief photo-ethnographic meaningful-moments intervention aimed at enhancing wellbeing (life satisfaction) and managing common mental health problems (stress/depression/anxiety) through Instagram. A 4 × 1 treatment-only intervention design was used to assess the immediate and long-term changes in meaning, gratitude, life satisfaction, and depression/stress/anxiety. Within-person development on the subscales was evaluated with Bayesian level and shape models. The results showed significant improvements in all factors directly after the intervention. Over the long term, significant changes with baseline measures for the presence of meaning, appreciation for others, and life satisfaction was found. Participants also reported a significant but small change in depression over the long term. Instagram could therefore be an interesting tool to consider when the aim is to enhance wellbeing and manage common mental health problems in the short-, medium- and long-term.

Objective: Advanced parenthood increases the risk of severe neurodevelopmental disorders like autism, Down syndrome and schizophrenia. Does advanced parenthood also negatively impact offspring’s general neurodevelopment? Method: We analyzed child-, father-, mother- and teacher-rated attention-problems (N = 38,024), and standardized measures of intelligence (N = 10,273) and educational achievement (N = 17,522) of children from four Dutch population-based cohorts. The mean age over cohorts varied from 9.73–13.03. Most participants were of Dutch origin, ranging from 58.7%-96.7% over cohorts. We analyzed 50% of the data to generate hypotheses and the other 50% to evaluate support for these hypotheses. We aggregated the results over cohorts with Bayesian research synthesis. Results: We mostly found negative linear relations between parental age and attention-problems, meaning that offspring of younger parents tended to have more attention problems. Maternal age was positively and linearly related to offspring’s IQ and educational achievement. Paternal age showed an attenuating positive relation with educational achievement and an inverted U-shape relation with IQ, with offspring of younger and older fathers at a disadvantage. Only the associations with maternal age remained after including SES. The inclusion of child gender in the model did not affect the relation between parental age and the study outcomes. Conclusions: Effects were small but significant, with better outcomes for children born to older parents. Older parents tended to be of higher SES. Indeed, the positive relation between parental age and offspring neurodevelopmental outcomes was partly confounded by SES.