Salient Information Research Articles

Codesigning a Digital Type 2 Diabetes Risk Communication Tool in Singapore: Qualitative Participatory Action Research Approach.

Diabetes is a serious public health concern worldwide. Despite public health efforts encouraging early screening and improving knowledge of effective interventions for those at increased risk of type 2 diabetes (T2D), the incorporation of preventative behaviors into an individual's daily life remains suboptimal. Successfully and accurately increasing risk perception has been demonstrated to increase behavioral intention. The study aims to codesign a T2D risk communication tool by engaging public participants to (1) identify key characteristics that contribute to an effective risk communication tool and (2) test and iterate to develop a culturally sensitive and meaningful risk communication tool that can motivate T2D preventative behaviors. We adopted a novel methodology, "Patient and Public Involvement (PPI) Hawkers," where we approached patrons at hawker centers and public eateries frequented by all local residents to evaluate and test 3 prototypes for the tool. The three prototypes were (1) "Diabetes Onset"-estimated age of diabetes onset of T2D based on one's risk factors, (2) "Relative Risk"-the relative risk of T2D is presented in a 1-10 scale indicating where one's risk score lie in relation to others, and (3) "Metabolic Age"-the median age of the risk category based on one's risk factors, presented to be compared against their chronological age. We gathered reactions and feedback through rapid testing and iteration to understand which risk result presentation would be received the best. All the collected data were revisited and analyzed using an inductive thematic analysis to identify the key characteristics contributing to an effective risk communication tool. We engaged with 112 participants (female: n=59, 56%) across 6 hawker centers. The key characteristics that were important to participants emerged in four main themes: (1) appeal and user experience, in terms of format and readability; (2) trust and validity of the institution providing the tool and the accuracy of the risk result; (3) threat appraisal: salience of risk information, which influenced their risk perception; and (4) coping appraisal: facilitators for behavior change, which impacted their intention for implementing T2D preventative behaviors. The predictive nature of the prototype entitled "Diabetes Onset" was poorly received and removed after the first iteration. The Relative Risk prototype was valued for being straightforward but feared to be boring. The Metabolic Age prototype was anticipated to be more motivating for behavior change, but there were some concerns that the terminology may not be understood by everyone. Participants were divided on which of the 2 prototypes, "Metabolic Age" or "Relative Risk," they would favor adopting. Further testing is now required to determine which prototype will be more effective in motivating behavior change. This study's insights on the design process and valued characteristics of a risk communication tool will inform future development of such interventions.

Enhancing Accessibility in Online Shopping: A Dataset and Summarization Method for Visually Impaired Individuals

A visually impaired individual (VI) encounters numerous challenges in their daily activities, particularly in tasks reliant on visual systems such as navigation, educational pursuits, and shopping. Online shopping poses a heightened difficulty due to its reliance on visual representations of products in digital formats. The impact of visual impairment on product selection based on reviews remains inadequately investigated. This study endeavors to address two primary objectives. Firstly, we propose the creation of a dataset comprising product review videos (referred to as PVS10) tailored for visually impaired individuals. Secondly, we present a foundational summarization methodology designed to facilitate access to pertinent and informative content within extensive video collections for visually impaired individuals. Our dataset, gathered from YouTube, encompasses 10 distinct products, each associated with the top 10 review videos, totaling 100 videos of varying lengths. Utilizing the search term "review videos of PRODUCT NAME", we assembled the dataset to facilitate automated summarization processes aimed at maximizing salient information, minimizing redundant content, and preserving the overarching sentiment conveyed in the reviews. This research focuses on the challenges faced by visually impaired people in online shopping, particularly when selecting products based on customer reviews. Our study demonstrates that people with visual impairments may actively explore product reviews and only acquire the information they require.

Investigating the hemiretinal asymmetry in emotion processing as a function of spatial frequency.

The subcortical visual pathway to the amygdala has long been considered a rapid and crude stream for processing emotionally salient information that is reliant on low spatial frequency (LSF) information. Recently, research has called this LSF dependency into question. To resolve this debate, we take advantage of an anatomical hemiretinal asymmetry, whereby the nasal hemiretina sends a higher proportion of information through the subcortical pathway than the temporal hemiretina. We recorded brain activity using electroencephalography (EEG) in human participants (N = 40) while they completed a monocular viewing paradigm. Pairs of faces (one fearful and one neutral, or both neutral) were projected simultaneously to the nasal and temporal hemiretina in three contrast-equated blocks; faces filtered to display only (i) LSF, (ii) high spatial frequency (HSF), or (iii) unfiltered information (broadband spatial frequency; BSF). BSF fearful faces were found to produce a greater naso-temporal asymmetry, with greater N170 amplitudes evoked by BSF faces in the nasal field, compared to HSF faces. Conversely, the naso-temporal asymmetry for LSF fearful faces did not differ between BSF and HSF. Collectively, these findings provide crucial evidence that the subcortical pathway carries combined spatial frequency visual signals, with a potential bias against HSF content.

Key Frame Selection for Temporal Graph Optimization of Skeleton-Based Action Recognition

Graph neural networks (GNNs) are extensively utilized to capture the spatial–temporal relationships among human body parts for skeleton-based action recognition. However, due to the inefficient information propagation caused by redundant sampling of video frames in the temporal domain, we focus on refining temporal graphs through key frame selection. To this end, we propose a multi-stage key frame selection (MSKFS) method, aiming to find the most representative frames as the graph nodes to learn compact temporal graph representations of human skeletons for action recognition. The MSKFS progressively selects key frames in two stages: (1) salient posture frame selection based on the global dynamics of body parts and (2) key frame refinement and alignment according to intra-frame correlations. The first stage captures the most salient information and aligns the corresponding information of skeleton sequences within the same category. The second stage enriches the subtle information for the integrity of the information derived by the salient frames. Moreover, variational inference is applied to differentiate the key frame refinement and alignment procedure, allowing the end-to-end optimization of arbitrary graph-based models to represent the obtained compact graph for skeleton-based action recognition. Our MSKFS method achieves state-of-the-art performances on two challenging action recognition datasets.

Auditory competition and stimulus selection across spatial locations from midbrain to forebrain in barn owls

Barn owls enable investigation of neural mechanisms underlying stimulus selection of concurrent stimuli. The audio-visual space map in the optic tectum (OT), avian homologue of the superior colliculus, encodes relative strength of concurrent auditory stimuli through spike response rate and interneuronal spike train synchrony (STS). Open questions remain regarding stimulus selection in downstream forebrain regions lacking topographic coding of auditory space, including the functional consequences of interneuronal STS on interregional signaling. To this end, we presented concurrent stimuli at different locations and manipulated relative strength while simultaneously recording neural responses from OT and its downstream thalamic target, nucleus rotundus (nRt), in awake barn owls of both sexes. Results demonstrated that broadly spatially tuned nRt units exhibit different spike response patterns to competition depending on spatial tuning preferences. Modeling suggests nRt units integrate convergent inputs from distant locations across midbrain map regions. Additionally, STS within nRt reflects the temporal properties of the strongest stimulus. Furthermore, interregional STS between OT and nRt was strongest when spatial tuning overlap between units across regions was large and when the strongest stimulus location during competition was favorable for units in both regions. Additionally, though gamma oscillations synthesized within OT are weakly propagated within nRt, average gamma power across regions correlates with strength of interregional STS. Overall, we demonstrate that nRt integrates inputs across distant areas of OT, retains spatial information through differences in strength of inputs from various locations of the midbrain map across neurons, and prioritizes coding of identity features to the strongest sound.Significance StatementThe brain strategically selects and preferentially processes salient stimuli. A critical function to this process involves transferring salient information across regions that may exhibit drastic transformations in coding schemes. Our study in barn owls investigates bottom-up signaling between the midbrain space map and its downstream thalamic target, which lacks spatial topography as also observed in mammalian auditory forebrain regions to elucidate general mechanisms underlying how spatial location information and other properties of the strongest sound are relayed between regions. Results show that the thalamus integrates neural responses widely across the midbrain map, retains coding of spatial location through varying strength of inputs of the map across neurons, and prioritizes further coding of identity features only to the strongest sound.

Use of chatgpt-4 to explain kawasaki disease to parents and paediatric trainees

Abstract Background Paediatric trainees and parents of Kawasaki disease (KD) patients may face challenges when managing the disease due to insufficient accessibility to KD information. Chat Generative Pre-Trained Transformer (ChatGPT) based on large language models may help explain KD information to paediatric trainees and patient parents. Purpose This study aims to determine the usefulness of Chat Generative Pre- Trained Transformer -4 (ChatGPT-4) in explaining Kawasaki Disease (KD) and its management to parents and paediatric trainees managing KD patients. Methods We created 2 sets of clinical scenarios. In the first set, ChatGPT-4 was instructed to respond to 10 questions based on enquiries from parents of KD patients. Responses were scored in terms of "factual accuracy", "coherence", "comprehensiveness", and "humaneness" by 3 paediatric cardiologists using Likert scale of 0-10. Readability were calculated using Flesch reading-ease test. In the second set, ChatGPT-4 was instructed to respond to 8 KD-related questions based on enquiries from paediatric trainees. Responses are graded based on "relevance", "reliability" and "comprehensiveness" using Likert scale of 0-10 by 3 paediatric cardiologists independently. Reviewers would determine whether major advice from chatGPT-4 would be adopted in clinical judgement. Results For parent-targeted responses, ChatGPT-4 achieved the highest scores in ‘humaneness’ (median 9.00, IQR 8.00 to 9.00) and ‘coherence’ (median 8.00, IQR 7.00 to 8.00). Inaccurate information regarding disease prognosis, actions as well as prescription of medications and surgery is found in 80% of scenarios. Missing information regarding long-term coronary complications, antiplatelet management and cardiac assessments is found in all 10 scenarios. Mean readability of parent-targeted responses is 71.70 ± 6.26, a readability level easily understood by 12-year-olds. For paediatrician-targeted responses, ChatGPT-4 achieved the highest scores in ‘relevance’ (median 9.50, IQR 7.25 to 9.0). Inaccurate information regarding coronary interventions, patient education and immunization recommendations is spotted in 37.5% of the scenarios. Missing information regarding patient education and stress imaging is found in 25% of the scenarios. All reviewers would adopt ChatGPT-4’s advice in 87.5% of the scenarios. Conclusions ChatGPT-4 has significant limitations in accuracy and lacks salient information when providing KD recommendations for parents and paediatric trainees.Performance in parent-targeted questionsPerformance in trainee-targeted question

Driver Distraction Detection Based on Fusion Enhancement and Global Saliency Optimization

Driver distraction detection not only effectively prevents traffic accidents but also promotes the development of intelligent transportation systems. In recent years, thanks to the powerful feature learning capabilities of deep learning algorithms, driver distraction detection methods based on deep learning have increased significantly. However, for resource-constrained onboard devices, real-time lightweight models are crucial. Most existing methods tend to focus solely on lightweight model design, neglecting the loss in detection performance for small targets. To achieve a balance between detection accuracy and network lightweighting, this paper proposes a driver distraction detection method that combines enhancement and global saliency optimization. The method mainly consists of three modules: context fusion enhancement module (CFEM), channel optimization feedback module (COFM), and channel saliency distillation module (CSDM). In the CFEM module, one-dimensional convolution is used to capture information between distant pixels, and an injection mechanism is adopted to further integrate high-level semantic information with low-level detail information, enhancing feature fusion capabilities. The COFM module incorporates a feedback mechanism to consider the impact of inter-layer and intra-layer channel relationships on model compression performance, achieving joint pruning of global channels. The CSDM module guides the student network to learn the salient feature information from the teacher network, effectively balancing the model’s real-time performance and accuracy. Experimental results show that this method outperforms the state-of-the-art methods in driver distraction detection tasks, demonstrating good performance and potential application prospects.

Brain Tumor Detection through Image Fusion Using Cross Guided Filter and Convolutional Neural Network

This Data fusion has become a significant issue in diagnostic imaging, particularly in medical applications like radiation and guided image surgery. Medical image fusion aims to enhance the precision of tumor diagnosis, by preserving the salient information and characteristics of the original images in the fused image. It has been shown that guided filters are capable of maintaining edges well. In this paper, we propose a novel cross-guided filter-based fusion approach for multimodal medical images utilizing convolutional neural networks. The cross-guided filter is used in the proposed algorithm to extract the detailed features from the source images. Convolutional neural networks are used to generate the feature weights of source images derived from the detail layers. The weighted average rule is used to merge the source images based on these weights. We used thirty distinct types of medical images from diverse sources to compare the effectiveness of the proposed strategy to that of existing methods, both numerically and visually. The experimental findings demonstrated that, in terms of both objective evaluation and qualitative image quality, the suggested system performs better than other standard methods already in use. The quantitative results show that compared to existing methods under consideration for comparison, the proposed algorithm improves mutual information by 25%, image entropy by 9.5%, spatial frequency by 21%, standard deviation by 18.1%, structural similarity index by 30%, and edge strength of the fused image by 39%.

How corruption prevails: A laboratory experiment

Previous research has found the contagion effect in corruption, nevertheless the mechanisms underlying the contamination of corruption remain unexplored. In this paper, we varied different kinds of information relevant to corruption, which are the number(breadth) /the size(depth) /the maximum amount of corruption, exposed to participants in a laboratory experiment to testify how the salience of different information affects participants' corruption decisions. Our results show that participants are more likely to engage in corruption and provide a higher amount after they are informed of the information of peers' corrupt behaviors. We confirm that decreasing social norms cost related to corrupt behavior, belief of increased likelihood of finding a partner to reach corruption, and conforming to peers' behaviors are the main channels.

Connector hubs in semantic network contribute to creative thinking.

Semantic memory offers a rich repository of raw materials (e.g., various concepts and connections between concepts) for creative thinking, represented as a semantic network. Similar to other networks, the semantic network exhibits a modular structure characterized by modules with dense internal connections and sparse connections between them. This organizational principle facilitates the routine storage and retrieval of information but may impede creativity. The present study investigated the effect of hub concepts with varying connection patterns on creative thinking from the perspective of a modular structured semantic network. By analyzing a large-scale semantic network, connector hubs (C-hubs) and provincial hubs (P-hubs) were identified based on their intra- and intermodule connections. These hubs were used as cue words in the alternative uses task, a widely used measure of creative thinking. Across four experiments, behavioral and neural evidence indicated that C-hubs facilitate the generation of more novel and remote ideas compared to P-hubs. However, this effect is predominantly observed in the early stage of the creative thinking process, involving changes in brain activation and functional connectivity in core regions of the default mode network and the frontoparietal network, including the dorsolateral prefrontal cortex, angular gyrus, and precuneus. Neural findings suggest that the superior performance of C-hubs relies on stronger interactions between automatic spreading activation, controlled semantic retrieval, and attentional regulation of salient information. These results provide insight into how concepts with varying semantic connection patterns facilitate and constrain different stages of the creative thinking process through the modular structure of semantic network. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Transfer-learning enabled micro-expression recognition using dense connections and mixed attention

Micro-expression recognition (MER) is a challenging computer vision problem, where the limited amount of available training data and insufficient intensity of the facial expressions are among the main issues adversely affecting the performance of existing recognition models. To address these challenges, this paper explores a transfer–learning enabled MER model using a densely connected feature extraction module with mixed attention. Unlike previous works that utilize transfer learning to facilitate MER and extract local facial-expression information, our model relies on pretraining with three diverse macro-expression datasets and, as a result, can: (i) overcome the problem of insufficient sample size and limited training data availability, (ii) leverage (related) domain-specific information from multiple datasets with diverse characteristics, and (iii) improve the model adaptability to complex scenes. Furthermore, to enhance the intensity of the micro-expressions and improve the discriminability of the extracted features, the Euler video magnification (EVM) method is adopted in the preprocessing stage and then used jointly with a densely connected feature extraction module and a mixed attention mechanism to derive expressive feature representations for the classification procedure. The proposed feature extraction mechanism not only guarantees the integrity of the extracted features but also efficiently captures local texture cues by aggregating the most salient information from the generated feature maps, which is key for the MER task. The experimental results on multiple datasets demonstrate the robustness and effectiveness of our model compared to the state-of-the-art.

Multi-Prior Driven Network for RGB-D Salient Object Detection

Most existing RGB-D salient object detection (SOD) methods rely on high-quality depth images. However, their performance is limited when processing low-quality depth maps. This paper exploits more complementary image priors to guide the model to learn on variable depth maps, and a novel multi-prior driven network called MPDNet is proposed for RGB-D SOD. MPDNet utilizes four processing pipelines to process RGB images and other priors, which include an RGB image processing pipeline, a depth map processing pipeline, a fine-grained and gradient prior processing pipeline, and an edge learning pipeline. Specifically, fine-grained and gradient priors are input to the same processing pipeline. For the depth maps, fine-grained and gradient priors, a prior channel attention module utilizes the channel attention mechanism to filter noises and highlights the salient cues. The RGB image processing pipeline uses a multi-feature progressive enhancement module to fuse and enhance features from depth maps. And a multi-feature prediction decoder decodes initial salient masks. In the edge learning pipeline, edge prior serves as an edge label and is captured by an edge capture module. Finally, the clear salient masks are obtained by fusing the salient information from the four pipelines. The experimental results on six benchmarks indicate that the proposed method outperforms thirteen state-of-the-art methods in six evaluation metrics.

Integrating user- and data-driven weather forecasts to develop legitimate, credible, and salient information services for smallholders in the Global South

Climate-related risks and variability pose significant challenges to the livelihoods and food security of smallholder farmers practicing rainfed agriculture. Many smallholders have limited access to weather information from climate services, and this information is often not tailored to their specific context and needs. Therefore, they rely on local ecological knowledge. This study utilizes the second generation of climate services, which provide demand-driven forecast information systems through mobile apps. We present three cases from agricultural communities in Guatemala, Bangladesh, and Ghana where we collaborated with farmers to develop local weather forecasts (LF) and combined them with scientific weather forecasts (SF) to create hybrid weather forecasts (HF). The integration of user-driven forecasts (LF) and data-driven forecasts (SF) enhances the legitimacy of the service, thereby increasing farmers’ trust and credibility by providing skilful forecasts. Furthermore, our results demonstrate that the hybrid weather forecast approach facilitates climate-smart, adaptive agricultural decision-making, enhancing the resilience and capacity of smallholder farmers in the Global South to adapt to a changing climate.

A Novel Framework for Multimodal Brain Tumor Detection with Scarce Labels.

Brain tumor detection has advanced significantly with the development of deep learning technology. Although multimodal data, such as Magnetic Resonance Imaging (MRI) and Computed Tomography (CT), has potential advantages in diagnostics, most existing studies rely solely on a single modality. This is because common fusion methods may lead to the loss of critical information when attempting multimodal fusion. Therefore, effectively integrating multimodal data has become a significant challenge. Additionally, medical image analysis requires large amounts of annotated data, and labeling images is a resourceintensive task that demands experienced professionals to spend a considerable amount of time. To address these challenges, this paper introduces a new unsupervised learning framework named Double-SimCLR. This framework builds on the foundation of contrastive learning and features a dual-branch structure, enabling direct and simultaneous processing of MRI and CT images for multimodal feature fusion. Given the "weak feature" characteristics of CT images (e.g., low soft tissue contrast and low resolution), we incorporated adaptive weight masking technology to enhance CT feature extraction. Moreover, we introduced a multimodal attention mechanism, which ensures that the model focuses on salient information, thereby elevating the precision and robustness of brain tumor detection. Even without substantial labeled data, experimental results demonstrate that Double-SimCLR achieves 93.458% accuracy, 92.463% precision, and a 93.058% F1-score, outperforming state-of-the-art (SOTA) models by 2.871%, 2.643%, and 3.098%, respectively.

SDAM: A dual attention mechanism for high-quality fusion of infrared and visible images.

Image fusion of infrared and visible images to obtain high-quality fusion images with prominent infrared targets has important applications in various engineering fields. However, current fusion processes encounter problems such as unclear texture details and imbalanced infrared targets and texture detailed information, which lead to information loss. To address these issues, this paper proposes a method for infrared and visible image fusion based on a specific dual-attention mechanism (SDAM). This method employs an end-to-end network structure, which includes the design of channel attention and spatial attention mechanisms. Through these mechanisms, the method can fully exploit the texture details in the visible images while preserving the salient information in the infrared images. Additionally, an optimized loss function is designed to combine content loss, edge loss, and structure loss to achieve better fusion effects. This approach can fully utilize the texture detailed information of visible images and prominent information in infrared images, while maintaining better brightness and contrast, which improves the visual effect of fusion images. Through conducted ablation experiments and comparative evaluations on public datasets, our research findings demonstrate that the SDAM method exhibits superior performance in both subjective and objective assessments compared to the current state-of-the-art fusion methods.

High‐order multilayer attention fusion network for 3D object detection

AbstractThree‐dimensional object detection based on the fusion of 2D image data and 3D point clouds has become a research hotspot in the field of 3D scene understanding. However, different sensor data have discrepancies in spatial position, scale, and alignment, which severely impact detection performance. Inappropriate fusion methods can lead to the loss and interference of valuable information. Therefore, we propose the High‐Order Multi‐Level Attention Fusion Network (HMAF‐Net), which takes camera images and voxelized point clouds as inputs for 3D object detection. To enhance the expressive power between different modality features, we introduce a high‐order feature fusion module that performs multi‐level convolution operations on the element‐wise summed features. By incorporating filtering and non‐linear activation, we extract deep semantic information from the fused multi‐modal features. To maximize the effectiveness of the fused salient feature information, we introduce an attention mechanism that dynamically evaluates the importance of pooled features at each level, enabling adaptive weighted fusion of significant and secondary features. To validate the effectiveness of HMAF‐Net, we conduct experiments on the KITTI dataset. In the “Car,” “Pedestrian,” and “Cyclist” categories, HMAF‐Net achieves mAP performances of 81.78%, 60.09%, and 63.91%, respectively, demonstrating more stable performance compared to other multi‐modal methods. Furthermore, we further evaluate the framework's effectiveness and generalization capability through the KITTI benchmark test, and compare its performance with other published detection methods on the 3D detection benchmark and BEV detection benchmark for the “Car” category, showing excellent results. The code and model will be made available on https://github.com/baowenzhang/High‐order‐Multilayer‐Attention‐Fusion‐Network‐for‐3D‐Object‐Detection.

Secondary capture: Salience information persistently drives attentional selection.

It is well known that attention is captured by salient objects or events. The notion that attention is attracted by salience information present in the visual field is also at the heart of many influential models of attention. These models typically posit a hierarchy of saliency, suggesting that attention progresses from the most to the least salient item in the visual field. However, despite the significance of this claim in various models, research on eye movements challenges the idea that search strictly follows this saliency hierarchy. Instead, eye-tracking studies have suggested that saliency information has a transient impact, only influencing the initial saccade toward the most salient object, and only if executed swiftly after display onset. While these findings on overt eye movements are important, they do not address covert attentional processes occurring before a saccade is initiated. In the current series of experiments, we explored whether there was evidence for secondary capture-whether attention could be captured by another salient item after the initial capture episode. To explore this, we utilized displays with multiple distractors of varying levels of saliency. Our primary question was whether two distractors with different saliency levels would disrupt search more than a single, highly salient distractor. Across three experiments, clear evidence emerged indicating that two distractors interfered more with search than a single salient distractor. This observation suggests that following initial capture, secondary capture by the next most salient distractor occurred. These findings collectively support the idea that covert attention traverses the saliency hierarchy. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Disinformation Spillover: Uncovering the Ripple Effect of Bot-Assisted Fake Social Engagement on Public Attention

Disinformation activities that aim to manipulate public opinion pose serious challenges to managing online platforms. One of the most widely used disinformation techniques is bot-assisted fake social engagement, which is used to falsely and quickly amplify the salience of information at scale. Based on agenda-setting theory, we hypothesize that bot-assisted fake social engagement boosts public attention in the manner intended by the manipulator. Leveraging a proven case of bot-assisted fake social engagement operation in a highly trafficked news portal, this study examines the impact of fake social engagement on the digital public’s news consumption, search activities, and political sentiment. For that purpose, we used ground-truth labels of the manipulator’s bot accounts, as well as real-time clickstream logs generated by ordinary public users. Results show that bot-assisted fake social engagement operations disproportionately increase the digital public’s attention to not only the topical domain of the manipulator’s interest (i.e., political news) but also to specific attributes of the topic (i.e., political keywords and sentiment) that align with the manipulator’s intention. We discuss managerial and policy implications for increasingly cluttered online platforms.

Nudging strategies to influence prescribers' behavior toward reducing opioid prescriptions: a systematic scoping review.

This systematic scoping review aimed to map the literature on the use of various nudging strategies to influence prescriber behavior toward reducing opioid prescriptions across diverse healthcare settings. A systematic database search was conducted using seven electronic databases. Only articles published in English were included. A total of 2234 articles were identified, 35 of which met the inclusion criteria. Two independent dimensions were used to describe nudging strategies according to user action and the timing of their implementation. Six nudging strategies were identified. The most common strategy was default choices, followed by increasing salience of information or incentives and providing feedback. Moreover, 32 studies used the electronic health record as an implementation method, and 29 reported significant results. Most of the effective interventions were multicomponent interventions (i.e., combining nudge strategies and non-nudge components). Most nudging strategies used a passive approach, such as defaulting prescriptions to generics and requiring no action from the prescriber. Although reported as effective, this approach often operates under the prescriber's radar. Future research should explore the ethical implications of nudging strategies.INPLASY registration number: 202420082.

Gender identity, salience of information, and tacit coordination: Gender differences in response to strategic uncertainty

We use a lab experiment to explore whether gender composition and gender identity salience influence team coordination. Identity in the experiment is induced using gender-specific and neutral avatars. In contrast with many previous studies, we do not find the presence of in-group favoritism, irrespective of whether gender identity or random avatars define the group. In addition, behavior remains unchanged when the gender of the counterpart is revealed. However, females are found to cooperate significantly more when gender information is disclosed, implying an elevation in the salience of gender identity induces the females to choose based on social expectations. This research adds to the discourse on gender dynamics in decision-making and suggests that gender identity plays a role in economic choices, innovating traditional views on diversity in teamwork. Our research sheds light on the intricate dynamics of gender composition in team settings, particularly under conditions of risk and uncertainty. These findings have the potential to inform both organizational practices and public policy, thereby contributing to a more equitable and efficient labor market.