Social media has transformed how people engage with the world around them. The positivity bias on social media, in particular, warrants in-depth investigation. This is particularly true as previous research has concentrated on one specific platform, Facebook. Based on a pilot study of 279 university students, this pre-registered experimental research used a mixed design to examine the positivity bias on Facebook, Instagram and Twitter/X. After recalling a personal event, 312 participants were randomly assigned to imagine telling this event to a group of friends and sharing it on social media (Facebook vs. Instagram vs. Twitter/X). Several characteristics were examined through repeated measures ANCOVAs including the texts’ valence and the usage of emoji. Contrary to the pilot results, we found no significant differences in how events were reported. We, however, found significant differences between platforms. Specifically, messages posted on Twitter/X had a more negative valence than those posted on Instagram and Facebook. The positivity bias, often associated with social media, may therefore not necessarily represent a bias per se, but only a form of positive self-presentation that is consistent across contexts. These findings highlight the importance of focusing on platform characteristics, such as architecture, affordances, and socio-cultural context. This study advances our understanding of the positivity bias across multiple platforms through a pre-registered experimental approach.

Researchers and policy-makers have widely hypothesized that psychological distance to climate change acts as a barrier to climate action, and that reducing this psychological distance is a promising strategy to promote it. However, over the last decade, correlational and experimental studies have highlighted complex and inconsistent results, raising doubts about the effects of reduced psychological distance to climate change on policy support and intention to perform climate-related behaviours. To address this issue, this research adopts a meta-analytical approach (i) to evaluate the size of the effect of psychological distance to climate change on climate action, and (ii) to investigate some factors that might explain the heterogeneity of these effects.

Many behavioural scientists do not agree on core constructs and how they should be measured. Different literatures measure related constructs, but the connections are not always obvious to readers and meta-analysts. Many measures in behavioural science are based on agreement with survey items. Because these items are sentences, computerised language models can make connections between disparate measures and constructs and help researchers regain an overview over the rapidly growing, fragmented literature. Our fine-tuned language model, the SurveyBot3000, accurately predicts the correlations between survey items, the reliability of aggregated measurement scales, and intercorrelations between scales from item positions in semantic vector space. In our pilot study, the out-of-sample accuracy for item correlations was .71, .89 for reliabilities, and .89 for scale correlations. In our preregistered validation study using novel items, the out-of-sample accuracy was slightly reduced to .59 for item correlations, .84 for reliabilities, and .84 for scale correlations. The synthetic item correlations showed an average prediction error of .17, with larger errors for middling correlations. Predictions exhibited generalizability beyond the training data and across various domains, with some variability in accuracy. Our work shows language models can reliably predict psychometric relationships between survey items, enabling researchers to evaluate new measures against existing scales, reduce redundancy in measurement, and work towards a more unified behavioural science taxonomy.

Presented by OSF

Presented by OSF

This project contains the manuscript and raw preliminary data for the stage 1 registered report "Cognitive, affective and behavioural effects of temporal comparison with prior aversive experiences in individuals with social anxiety" Hosted on the Open Science Framework

Abstract. While there is extensive literature exploring the possible negative effects of video games, and many such studies using college student samples, there is little research on how video game...

Task decisions and confidence ratings are fundamental measures in metacognition research, but using these reports requires collecting them in some order. Only three orders exist and are used in an ad hoc manner across studies. Evidence suggests that when task decisions precede confidence, this report order can enhance metacognition. If verified, this effect pervades studies of metacognition and will lead the synthesis of this literature to invalid conclusions. In this Registered Report, we tested the effect of report order across popular domains of metacognition and probed two factors that may underlie why order effects have been observed in past studies: report time and motor preparation. We examined these effects in a perception experiment (n = 75) and memory experiment (n = 50), controlling task accuracy and learning. Our registered analyses found little effect of report order on metacognitive efficiency, even when timing and motor preparation were experimentally controlled. Our findings suggest the order of task decisions and confidence ratings has little effect on metacognition, and need not constrain secondary analysis or experimental design.

Displaying high effort at work is rewarded with more positive moral judgments (effort moralization effect) and increased cooperation partner attractiveness. This holds, even if higher effort is unrelated to better performance. Yet, current evidence is exclusively based on males, mostly situated in the work context. This prohibits generalization to the full population and neglects critical aspects of our lives, such as the care context (e.g., unpaid care for elders). To address this gap, we conducted two Studies (Study 1: Nwork = 859, Study 2: Ncare = 701) testing the effect between genders and contexts—work and care. Study materials featured two actors performing the same task, requiring different levels of effort (high/low). Participants rated the actor’s morality, suggested hourly salary, and reported their satisfaction to cooperate with them as assigned partners. The results confirmed the effort moralization effect in work contexts but were mixed for the care context, potentially due to the inherently moral nature of the behavior. There were no gender differences, supporting the demographic generalizability of the effect. Effort did not influence suggested pay, and participants expressed greater satisfaction with low-effort actors as assigned cooperation partners. While further research is needed to explore the boundary conditions of effort moralization in different contexts, the findings support its role as a robust heuristic for moral judgment in the work context.

Presented by OSF

Objective: The study's principal aim was to explore the usefulness of the Harmful Dysfunction Analysis (HDA) in identifying individuals with pathological social media use (PSMU) and potentially other behavioral addictions as an alternative to using DSM-5-TR-based diagnostic criteria for substance use disorders (SUD) or research criteria for internet gaming disorder (GD). Method: Using Swiss data (N = 7,510) from the Health Behaviour in School-aged Children Study 2018, we tested weaker (HDA1) and stronger (HDA2) HDA versions. We examined differences between HDA cases and non-cases, convergence between different scoring methods, and between groups differences distinct to each scoring method (i.e., non-overlapping cases) on measures of physical health (physical activity and body mass index) and mental health (psychosomatic health, life satisfaction, school well-being), with models adjusted for age, gender, migration status, and family affluence. Data from Hungary (N = 3,789) was selected to repeat the analysis as a sensitivity investigation. Results: SUD-based scoring yielded the highest PSMU prevalence of 33.2% while GD-based prevalence was 9%. HDA1 and HDA2 scorings yielded PSMU prevalence of 22.2% and 4.2%, respectively. HDA1-based PSMU cases showed poorer physical and mental health than non-cases with differences of small-to-medium effect size, whereas HDA2 differences were of large effect size. SUD-based cases that overlapped with HDA scoring showed worse physical and mental health than non-overlapping SUD-based cases. Non-overlapping GD and HDA cases showed no significant differences on validators that survived the sensitivity evaluation. Conclusions: HDA may reduce PSMU prevalence while preserving conceptual and clinical validity. The more demanding HDA2 approach exhibited more convincing validator results than HDA1. From the HDA perspective, substantial SUD-based cases were false positives whereas GD-based scoring produced both false positives and negatives. Our findings suggest that further examination of HDA for advancing the conceptualization of addictive disorders is warranted.

In hundreds of articles published over the past two decades, nanoparticles have been described as probes for sensing and imaging of a variety of intracellular cytosolic targets. However, nanoparticles generally enter cells by endocytosis with only a small fraction reaching the cytosol. Most of those articles do not describe the mechanisms of nanoparticles entry into the cell and therefore the paradox of sensing a target in the cytosol when most particles do not access that compartment remains. To address this paradox, we are initiating a replication project of some of the most influential articles in this field with the aim of confirming their ability to detect their targets and getting additional insights into their intracellular localisation. This article is a pre-registered report for the second replication of this project, namely the replication of Seferos et al ‘Nano-Flares: Probes for Transfection and mRNA Detection in Living Cells’. To achieve our aim, we will carry out both a direct replication of the experiments in the original article, with the addition of some experiments and controls (in particular optical and electron microscopy). We hope that this study will contribute to a better understanding of the intracellular fate of nanoparticles and, with the help of the scientific community, help set standards in the design and reporting of studies in this field.

Presented by OSF

Presented by OSF

Presented by OSF

Presented by OSF

Presented by OSF

Presented by OSF

One of the key areas in the scientific programs of addictive behaviors is the research on measurement. In the present study, we focus on the survey measurement of gaming disorder. In particular, we tested the gambling hypotheses: whether survey studies on gaming disorder are potentially contaminated by gambling behavior data and thus do not reflect actual gaming behaviors. A systematic review of survey materials among the most cited studies (N=500) provided confirmatory evidence for a systematic error in the literature. The majority of research in the field is contaminated by a measurement error stemming from conceptual confusion. This problem is linguistic and does not concern all languages to the same degree. Additionally, we surveyed English (n=312) and Slovak (n=319) people to explore further reasons for the error. Exploration revealed various terminological confusions in both languages. For instance, half of the participants (EN=48.7%/SK=59.1%) interpreted “gambling” to also refer to “gaming”, including offline gambling like poker (EN=68.6%/SK=62.5%). Meanwhile, the majority interpreted offline activities like board games (EN=50.3%/SK=62%) to be gaming too. The findings suggest that the systematic measurement error is unlikely to be easily correctable through self-report instructions, as people interpret gaming and gambling in many inconsistent ways. We recommend gaming disorder research to seek alternatives to survey data.

Presented by OSF

Functional connectivity is frequently used to assess dynamic brain functioning and predict individual differences in behavioral outcomes, such as psychopathology. Inferences from functional connectivity analyses typically rely on group-averaged model statistics. However, heterogeneity between individuals may lead to group-level models that poorly reflect each individual. Poor individual-level precision may limit the ability to make individual-level predictions, which is necessary for key goals such as clinical translation. This registered report examined between-person heterogeneity in resting-state functional connectivity strength patterns by assessing similarity between group- and individual-level connectivity models in the Adolescent Brain Cognitive Development study. Using intraclass correlation coefficients, we found that a group-averaged region-of-interest-based connectivity model was a poor reflection of every individual. In contrast, a group-averaged model of between- and within-network connectivity was a good representation of most individuals. We then examined how individual-level distinctness from the group moderated predictive performance of several clinical and neurocognitive scales. Hypotheses that group-to-individual dissimilarity would worsen behavioral prediction were not supported with primary clinical outcomes. The little psychopathology reported in this sample was a notable limitation. In contrast, lower similarity to the group worsened prediction of performance on the pattern comparison test, providing minor support for hypotheses. Overall, results suggest that region-of-interest-based functional connectivity networks are highly heterogeneous and group-based models are inappropriate for individual-level inferences, but that network-based connectivity is largely similar across individuals. Additionally, we provide minor evidence of the impacts of heterogeneity on prediction that future studies should build on.

Editors' Summary: This Replication Study has reproduced some parts of the original paper but it also contains results that are not consistent with other parts of the original paper.

Background: Providing informal care to persons with dementia (PWD) living at home can be challenging and lead to experience of caregiver stress and depression. Interventions delivered through mobile applications (apps) provide innovative solutions for community-based social care professionals to address the increasing support needs of family caregivers (FCs) to PWD. Objective: This study aimed to examine, among FCs to PWD living at home, a) the potential association between professional support provided through a mobile app and caregiver stress and depressive symptoms; b) types of support provided through chat interaction between FCs and social care professionals; c) how provision of support provided through a mobile app relates to changes in caregiver stress and depressive symptoms. Methods: A mixed-methods pilot study was conducted integrating quantitative pre- and post-intervention data and qualitative logged chat data. FCs to PWD living at home (n=35) were recruited to test a tailor-made mobile app over eight weeks. The outcome measures were caregiver stress (primary) and depressive symptoms (secondary). Descriptive statistics were used to describe socio-demographic factors. Inferential statistics were used to analyse mean differences in the outcome measures pre- and post-intervention. FCs were divided into three groups based on changes in caregiver stress scores between pre- and post-intervention. Generalized linear models (GLM) analyses were used to determine the association between participation in the intervention on caregiver stress and depressive symptoms, controlling for age, gender and relationship to the PWD. Logged chat data was analysed using summative content analysis to identify types of support provided and received. Changes in caregiver stress were integrated with the chat data to determine patterns in the types of support received. Results: The mean age of the FCs was (69.4), with most being women (80%), partners (68.6%) and living together with the PWD (74%). The mean score of FCs' caregiver stress was marginally higher post-intervention (24.1, SD 9.3) compared to pre-intervention (23.9, SD 9.2), while the mean score of their depressive symptoms declined between pre- (6.5, SD 5.1) and post-intervention (6.2, SD 5.2). These differences were not statistically significant. Regression analyses showed that participation in the intervention was not statistically significantly associated with caregiver stress (β=0.171, α=.05, P=.862) or depressive symptoms (β= -0.293, α=.05, P=.745) after adjusting for age, gender and relationship to the PWD. However, mixed-methods analysis at the sub-group level, suggests that frequently provided tailored support by social care professionals delivered through a mobile app potentially can reduce caregiver stress among FCs to PWD living at home. Conclusions: The study highlights the importance of providing frequent and tailor-made support to meet the individual needs of FCs to PWD. The findings from this study can help community-based social care providers plan and organize the content of digital support provided for FCs to PWD living at home.

Presented by OSF

We present a programmatic research line to test whether a longitudinal intervention aiming to increase key psychological correlates of pro-environmental behavior motivates young adults to take climate action. In five longitudinal studies, we will test and optimize a six-week, in-person intervention, exposing participants to interactive lessons and activities that were developed from existing psychological theories and interventions. The goal is to increase pro-climate behavior by leveraging 12 psychological correlates such as beliefs, attitudes and affect. Before and after the intervention, participants will complete surveys measuring the psychological correlates, and also report individual and collective pro-climate behaviors via Ecological Momentary Assessment (EMA). Each study will include a control group. The psychological correlates and behavior will be measured again after three months to assess the medium-term effectiveness of the intervention. The results and participant feedback from each study will be used to update the intervention for the following studies. Each study will target young adults at different life stages (studying or working) across two countries (Italy and the Netherlands). The longitudinal design, the intensive manipulations and EMA’s ecological validity are uniquely capable of revealing causal evidence that changes in beliefs, attitudes and affect lead to climate action across age groups and cultures. Hosted on the Open Science Framework

Schnall, Benton, and Harvey (2008) hypothesized that physical cleanliness reduces the severity of moral judgments. In support of this idea, they found that individuals make less severe judgments wh...

Background: Concerns have been expressed about the abundance of new clinical prediction models (CPMs) proposed in the literature. The extent of this proliferation in prediction research, however, remains unclear. Objective: To estimate the total and annual number of CPM development publications available for all medical fields. Methods: Using a validated search strategy, we conducted a systematic search for prediction model studies published in Pubmed and Embase between 1995 and the end of 2020. By taking random samples for each year, we identified eligible studies that developed a multivariable model (diagnostic or prognostic) for individual-level prediction of a health outcome across all medical fields. Exclusion criteria included the development of models with a single predictor, studies not concerning humans, methodological studies, conference abstracts, articles with unavailable full text, and studies not available in English. Next, we estimated the total and annual number of published regression-based multivariable CPM development articles, based on the total number of publications, proportion of included articles and search sensitivity. Furthermore, we used an adjusted Poisson regression to extrapolate our results to years 1950-2024. Additionally, we estimated the number of articles developing non-regression-based CPMs. Results: From a random sample of 10,660 articles published between 1995 and 2020, 109 regression-based CPM development articles were included. We estimated that 82,772 (95%CI: 65,313-100,231) CPM development articles using regression were published, with an acceleration in model development from 2010 onward. With the addition of articles that used other techniques than regression (e.g., machine learning), the number rose to 147,714 (125,201-170,226). After extrapolation to 1950-2024, numbers increased to 156,673 and 248,431 for regression-based models and all models, respectively. Conclusions: Based on a representative sample of publications from the literature, we estimate that close to 250,000 articles reporting the development of clinical prediction models for all medical fields have been published until 2024. CPM development publication is ever increasing. To prevent research waste and to close the gap between research and clinical practice, focus should shift away from developing new CPMs to facilitating model validation and impact assessment of the plethora of available CPMs. Limitations of this study include restriction of search to articles available in English and development of the validated search strategy prior to the popularity of artificial intelligence and machine learning models.

Background: Falls are a prevalent and serious health condition among older people in residential aged care facilities, causing significant health and economic burdens. However, the likelihood of future falls can be predicted, and thus, falls can be prevented if appropriate prevention programs are implemented. Current fall prevention programs in residential aged care facilities rely on risk screening tools with suboptimal predictive performance, leading to significant concerns regarding resident safety. Objective: This study aimed to develop a predictive, dynamic dashboard to identify residents at risk of falls with associated decision support. This paper provides an overview of the technical process, including the challenges faced and the strategies used to overcome them during the development of the dashboard. Methods: A predictive dashboard was co-designed with a major residential aged care partner in New South Wales, Australia. Data from resident profiles, daily medications, fall incidents, and fall risk assessments were used. A dynamic fall risk prediction model and personalized rule-based fall prevention recommendations were embedded in the dashboard. The data ingestion process into the dashboard was designed to mitigate the impact of underlying data system changes. This approach aims to ensure resilience against alterations in the data systems. Results: The dashboard was developed using Microsoft Power BI and advanced R programming by linking data silos. It includes dashboard views for those managing facilities and for those caring for residents. Data drill-through functionality was used to navigate through different dashboard views. Resident-level change in daily risk of falling and risk factors and timely evidence-based recommendations were output to prevent falls and enhance prescriptive decision support. Conclusions: This study emphasizes the significance of a sustainable dashboard architecture and how to overcome the challenges faced when developing a dashboard amid underlying data system changes. The development process used an iterative dashboard co-design process, ensuring the successful implementation of knowledge into practice. Future research will focus on the implementation and evaluation of the dashboard’s impact on health processes and economic outcomes.

There are a myriad of obstacles faced by individuals who take medication for opioid use disorder, including high out-of-pocket costs, stigma, logistical challenges associated with traveling to treatment facilities, and negative experiences with treatment providers. The proliferation of illicit fentanyl use and associated overdose risk underscores the urgent need to broaden access to lifesaving pharmacotherapy for opioid use disorder. This case report describes the safe and effective induction of a 32-year-old male onto sublingual buprenorphine in a digital care setting, with integrated, counselor-facilitated psychosocial treatment delivered via a smartphone app targeting relapse prevention. The digital care modality enabled this patient to overcome 2 major treatment barriers: limited access to evidence-based treatment given his residence in a rural area, and heightened overdose risk given his use of fentanyl, a potent synthetic opioid. In addition, we describe tobacco cessation treatment delivered digitally concurrently with opioid use disorder care, combining pharmacotherapy and behavioral skills training. Given the disproportionately high rate of co-occurring opioid use disorder and tobacco use and associated mortality rates, the effective treatment of these conditions simultaneously in this case advances our knowledge of potentially efficacious treatment approaches for underserved individuals with opioid use disorder who present with polysubstance use.

Presented by OSF

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