General Domain Research Articles

Oral health knowledge, attitudes and practices of dietitians in Australia: A national survey.

Dietitians are a well-placed profession to be providing pre-emptive oral health promotion. Despite recommendations that oral health promotion should be routinely part of dietetic practice, there is limited data informing the current practices of clinical dietitians in this area across Australia. Hence, the aim of this study was to investigate the knowledge and practices of Australian dietitians and oral health promotion. A cross-sectional survey was undertaken involving registered clinical dietitians in Australia using purposive and snowballing sampling (social media/dietetic organisations/public databases). Data were analysed using descriptive and inferential statistics. A total of 149 dietitians participated in the national survey. Overall, dietitians were knowledgeable about oral health risk factors and preventative measures across general health domains. Majority of dietitians agreed that oral health can affect nutrition interventions (95.5%) and dietitians should be discussing oral health (88.0%). However, nearly half were not confident in providing counselling or education and felt that undergraduate training for oral health promotion was inadequate (78.2%). A small proportion (6.0%) of dietitians were already providing oral health promotion regularly. Key barriers included a lack of clear guidelines for practice, limited training opportunities and indistinct referral pathways. Dietitians have acknowledged that oral health promotion should be incorporated into their practice. However, they are challenged by a lack of resources and training to support this in clinical practice. SO WHAT?: Capacity building dietitians to promote oral health allows opportunity for improvement in the oral health, nutritional status and quality of life of priority population groups.

Relationship Between General Health Domains and Pedestrian Safety Behavior: A Cross-Sectional Study

Background: Traffic injuries are one of the top ten leading causes of mortality worldwide that impose a substantial burden on the health and economy of nations. Among all traffic users, pedestrians are the most vulnerable, accounting for a significant share of injuries and fatalities resulting from traffic accidents. Numerous factors contribute to pedestrian traffic accidents, with unsafe pedestrian behavior being one of the most critical factors. Objectives: Various factors can influence the traffic behavior of pedestrians. Therefore, this study aimed to examine the relationship between demographic characteristics, the general health status of pedestrians, and their traffic behavior. Methods: In this cross-sectional study, after obtaining ethical approval and participants’ consent, data were collected from eligible individuals. The data included participants’ demographic information and their responses to the 28-question General Health Questionnaire (GHQ-28) and the Pedestrian Behavior Questionnaire (PBQ). Linear regression analysis was performed to assess the relationships between demographic variables, general health domains, and the total score obtained from the PBQ. Results: Among the 1 421 participants in the study, 720 (50.7%) were female and 1 310 (92.2%) were under the age of 60. In terms of marital status, 979 individuals were married (69.5%), and 368 (26.2%) were single. The overall score of the general health of the participants was high in 623 individuals (43.8%), moderate in 666 individuals (46.9%), low in 122 individuals (8.6%), and very low in 10 individuals (0.7%). The mean (standard deviation) score of pedestrian behavior was also 57.89 (±11.9). There was a significant relationship between female gender, age of 15-29 years, being single, and domains of general health (somatic symptoms, anxiety assessment, social dysfunction assessment, and depression assessment) with the overall score of pedestrian behavior. Additionally, a significant relationship was found between the overall score of the participants’ general health and the overall score of their pedestrian behavior. Conclusions: There is a significant relationship between demographic characteristics and various domains of the general health of pedestrians and their behavior. Paying attention to this issue in policymaking and conducting further studies to identify other factors affecting pedestrian traffic behavior to prevent traffic injuries seems necessary.

Identifying China's distinctive academic fields among the top 2% of influential scientists: A bibliometric analysis using Rasch KIDMAP.

Leading scientists worldwide are recognized by their placement in the top 2% based on their career-spanning contributions, as categorized by the Science-Metrix classification. However, there has been little focus on the unique scientific fields and subfields that separate countries. Although the KIDMAP in the Rasch model has been utilized to depict student performance, its application in identifying distinctive academic areas remains unexplored. Our study uses this model to pinpoint unique research domains specific to countries based on the top 2% author data. We sourced our data from Elsevier career-long author database updated until the end of 2022. This encompassed 168 countries, 22 scientific domains, and 174 subdomains in 2021 and 2022 (with a total of 194,983 and 204,643 researchers, respectively). Our approach was threefold: identifying unique fields, subfields, and researchers. Visualizations included scatter plots, KIDMAP, and the Impact Bam Plot (IBP). China distinctive research areas were identified using the Rasch KIDMAP. Key insights include the following: The US prevailing dominance in scientific domains in both 2021 and 2022. China distinct contribution in the "Enabling & Strategic Technologies" domain. China notable emphasis on the "Complementary & Alternative Medicine" subfield in 2022. Dr Phillip Low from the Mayo Clinic (US) emerged as a leading figure in the General & Internal Medicine research domain. Despite trailing the US in global research achievements, China showcased pronounced expertise in specific scientific areas, such as the "Complementary & Alternative Medicine" subfield in 2022, when compared to China other subfields based on the level of academic performance (-3.09 logits). Future research could benefit from incorporating KIDMAP visuals to gauge other countries' strengths in various research sectors, expanding beyond the China-centric focus in this study.

Developing ChatGPT for biology and medicine: a complete review of biomedical question answering.

ChatGPT explores a strategic blueprint of question answering (QA) to deliver medical diagnoses, treatment recommendations, and other healthcare support. This is achieved through the increasing incorporation of medical domain data via natural language processing (NLP) and multimodal paradigms. By transitioning the distribution of text, images, videos, and other modalities from the general domain to the medical domain, these techniques have accelerated the progress of medical domain question answering (MDQA). They bridge the gap between human natural language and sophisticated medical domain knowledge or expert-provided manual annotations, handling large-scale, diverse, unbalanced, or even unlabeled data analysis scenarios in medical contexts. Central to our focus is the utilization of language models and multimodal paradigms for medical question answering, aiming to guide the research community in selecting appropriate mechanisms for their specific medical research requirements. Specialized tasks such as unimodal-related question answering, reading comprehension, reasoning, diagnosis, relation extraction, probability modeling, and others, as well as multimodal-related tasks like vision question answering, image captioning, cross-modal retrieval, report summarization, and generation, are discussed in detail. Each section delves into the intricate specifics of the respective method under consideration. This paper highlights the structures and advancements of medical domain explorations against general domain methods, emphasizing their applications across different tasks and datasets. It also outlines current challenges and opportunities for future medical domain research, paving the way for continued innovation and application in this rapidly evolving field. This comprehensive review serves not only as an academic resource but also delineates the course for future probes and utilization in the field of medical question answering.

Access to High Quality Surgical Repair Services is a Fundamental Right of Patients with Obstetric Fistulas: A Study on Quality of Life Data in the Democratic Republic of the Congo.

The aim of this study was to assess quality of life (QoL) using the WHOQOL-BREF questionnaire among obstetric fistula (OF) patients before and after surgical repair of OF (SROF). A longitudinal cohort study was conducted between November 2022 and October 2023 in the Democratic Republic of the Congo (DRC) among OF patients to assess their QoL before and after SROF. A systematic sampling technique was used to recruit a total of 158 OF patients. The WHOQOL-BREF questionnaire assessed general health, life experience, as well as physical, social, psychological, and environmental domains. The mean age among the 158 respondents was 33.51 ± 9.63 years, and 77.85% of them lived in rural areas. In terms of surgical outcomes, 80.38% had closure of the OF with regained continence, 5.7% had closure of the OF with persistent incontinence, and 13.9% had a failed surgical repair. Overall mean QoL scores were higher after OF surgical repair (3.83, standard deviation [SD]=0.89) in comparison to pre-operative (1.58, SD=0.63) (p<0.001). These QoL improvements included physical (mean score 66.32 post-surgery versus 28.37 before, p<0.001), social (mean score 64.92 post-surgery versus 27.90 before, p<0.001), psychological (mean score 68.09 post-surgery versus 21.28 before, p<0.001), environmental (mean score 48.41 post-surgery versus 16.91 before p<0.001), and general domains. Patients with a successful OF repair had a better QoL score than those with a closed fistula but ongoing incontinence or those for whom surgery failed to close the fistula. The present study showed that among OF patients, all QoL domains were impaired before surgical repair and significantly improved after surgery. Successful OF closure alleviates the consequences of OF and helps to restore patients' wellbeing. Our findings call for improved access to high-quality surgical repair services as a fundamental right for OF patients.

Rad-Phi2: Instruction Tuning Phi2 for Radiology

Small Language Models (SLMs) have shown remarkable performance in general domain language understanding, reasoning and coding tasks, but their capabilities in the medical domain, particularly concerning radiology text, is less explored. In this study, we investigate the application of SLMs for general radiology knowledge specifically question answering related to understanding of symptoms, radiological appearances of findings, differential diagnosis, assessing prognosis, and suggesting treatments w.r.t diseases pertaining to different organ systems. Additionally, we explore the utility of SLMs in handling text-related tasks with respect to radiology reports within AI-driven radiology workflows.We fine-tune Phi2, a SLM with 2.7 billion parameters using high-quality educational content from Radiopaedia, a collaborative online radiology resource. The resulting language model, RadPhi2-Base, exhibits the ability to address general radiology queries across various systems (e.g., chest, cardiac). Furthermore, we investigate Phi2 for instruction tuning, enabling it to perform specific tasks. By fine-tuning Phi2 on general domain tasks followed by radiology-specific tasks related to chest X-ray reports, we create RadPhi2. Our empirical results reveal that Rad-Phi2-Base and Rad-Phi2 perform comparably or even outperform larger models such as Mistral-7B-Instruct-v0.2 and GPT-4 providing concise and precise answers.

Sexuality and sexual dysfunction in patients with psoriatic arthritis: A cross-sectional study.

Psoriatic arthritis (PsA) is a chronic inflammatory disorder affecting the joints, skin and entheses. Despite the importance of the topic, few studies have investigated the association between PsA and sexual function. The purpose of this study was to assess sexuality and the prevalence of sexual dysfunction (SD) in patients with PsA. This was an observational, cross-sectional single-center study on 23 PsA patients (male=12; female=11) evaluated with 2 male questionnaires (MSQ= Male Sexual Quotient, and IIEF=International Index of Erectile Function) and 2 female questionnaires (FSQ= Female Sexual Quotient, and FSFI=Female Sexual Function Index) validated for Brazilian Portuguese, in order to determine changes in sexual function. Clinical parameters, musculoskeletal activity and skin activity were also analyzed to identify factors associated with SD. The mean age was 52.1±9.7 years (males) and 49.1±9.6 years (females). Clinically, the patients had low skin and peripheral joint disease activity or were in remission. The mean time of PsA was 10±6.2 years, and 65.2% had a steady sexual partner. The mean MSQ score was 75.8±16.8. The prevalence of SD was 91.7% in men (IIEF), with a predominance of mild SD. The mean FSQ score was 64.9±24.1. The prevalence of SD was 72.7% in women (FSFI), with low domain scores. Also, a significant association was found between female age and total and domain-specific FSFI scores. PASI (Psoriasis Area and Severity Index) and the general satisfaction domain (IIEF) were significantly correlated. This study found a high prevalence of SD in PsA patients. Age had a negative impact on female sexual function. Physicians need to be more aware of SD in this population to provide early multidisciplinary treatment and minimize the impact of the disease on the quality of life of patients and their partners.

Multi-Modality Multi-Attribute Contrastive Pre-Training for Image Aesthetics Computing.

In the Image Aesthetics Computing (IAC) field, most prior methods leveraged the off-the-shelf backbones pre-trained on the large-scale ImageNet database. While these pre-trained backbones have achieved notable success, they often overemphasize object-level semantics and fail to capture the high-level concepts of image aesthetics, which may only achieve suboptimal performances. To tackle this long-neglected problem, we propose a multi-modality multi-attribute contrastive pre-training framework, targeting at constructing an alternative to ImageNet-based pre-training for IAC. Specifically, the proposed framework consists of two main aspects. (1) We build a multi-attribute image description database with human feedback, leveraging the competent image understanding capability of the multi-modality large language model to generate rich aesthetic descriptions. (2) To better adapt models to aesthetic computing tasks, we integrate the image-based visual features with the attribute-based text features, and map the integrated features into different embedding spaces, based on which the multi-attribute contrastive learning is proposed for obtaining more comprehensive aesthetic representation. To alleviate the distribution shift encountered when transitioning from the general visual domain to the aesthetic domain, we further propose a semantic affinity loss to restrain the content information and enhance model generalization. Extensive experiments demonstrate that the proposed framework sets new state-of-the-arts for IAC tasks. The code, database and pre-trained weights will be available at https://github.com/yipoh/AesNet.

Distantly Supervised Biomedical Relation Extraction Via Negative Learning and Noisy Student Self-Training.

Biomedical relation extraction aims to identify underlying relationships among entities, such as gene associations and drug interactions, within biomedical texts. Despite advancements in relation extraction in general knowledge domains, the scarcity of labeled training data remains a significant challenge in the biomedical field. This paper provides a novel approach for biomedical relation extraction that leverages a noisy student self-training strategy combined with negative learning. This method addresses the challenge of data insufficiency by utilizing distantly supervised data to generate high-quality labeled samples. Negative learning, as opposed to traditional positive learning, offers a more robust mechanism to discern and relabel noisy samples, preventing model overfitting. The integration of these techniques ensures enhanced noise reduction and relabeling capabilities, leading to improved performance even with noisy datasets. Experimental results demonstrate the effectiveness of the proposed framework in mitigating the impact of noisy data and outperforming existing benchmarks.

Test of the Latent Dimension of a Spatial Blind Source Separation Model

We assume a spatial blind source separation model in which the observed multivariate spatial data is a linear mixture of latent spatially uncorrelated Gaussian random fields containing a number of pure white noise components. We propose a test on the number of white noise components and obtain the asymptotic distribution of its statistic for a general domain. We also demonstrate how computations can be facilitated in the case of gridded observation locations. Based on this test, we obtain a consistent estimator of the true dimension. Simulation studies and an environmental application demonstrate that our test is at least comparable to and often outperforms bootstrap-based techniques, which are also introduced in this paper.

Learning to Discover Knowledge: A Weakly-Supervised Partial Domain Adaptation Approach.

Domain adaptation has shown appealing performance by leveraging knowledge from a source domain with rich annotations. However, for a specific target task, it is cumbersome to collect related and high-quality source domains. In real-world scenarios, large-scale datasets corrupted with noisy labels are easy to collect, stimulating a great demand for automatic recognition in a generalized setting, i.e., weakly-supervised partial domain adaptation (WS-PDA), which transfers a classifier from a large source domain with noises in labels to a small unlabeled target domain. As such, the key issues of WS-PDA are: 1) how to sufficiently discover the knowledge from the noisy labeled source domain and the unlabeled target domain, and 2) how to successfully adapt the knowledge across domains. In this paper, we propose a simple yet effective domain adaptation approach, termed as self-paced transfer classifier learning (SP-TCL), to address the above issues, which could be regarded as a well-performing baseline for several generalized domain adaptation tasks. The proposed model is established upon the self-paced learning scheme, seeking a preferable classifier for the target domain. Specifically, SP-TCL learns to discover faithful knowledge via a carefully designed prudent loss function and simultaneously adapts the learned knowledge to the target domain by iteratively excluding source examples from training under the self-paced fashion. Extensive evaluations on several benchmark datasets demonstrate that SP-TCL significantly outperforms state-of-the-art approaches on several generalized domain adaptation tasks. Code is available at https://github.com/mc-lan/SP-TCL.

Satisfaction of basic needs mediates relationships between incremental mindsets and well-being.

Research on the extent to which people believe that people can change (incremental beliefs) suggests that incrementalist beliefs are positively related to well-being, whereas entity beliefs (people cannot change) are not. One explanation for this relationship is that incremental beliefs are associated with a mastery orientation, whereas entity beliefs are not. If this is the case, then autonomous and competence motives should mediate relationships between incrementalism and well-being because these motives reflect different aspects of mastery. The present study examined the possibility that autonomous and competence motives mediate relationships between self-theories and well-being. Participants were adult community members (n = 428) who completed the Life Engagement Test (eudaimonic well-being), the Satisfaction with life Scale (hedonic well-being), the Mental Health Continuum Scale (eudaimonic, subjective, and psychological well-being), the Basic Needs Satisfaction scale (autonomy, competence, relatedness), and a measure of implicit theories of the self (incremental and entity beliefs). Regression analyses found that incremental beliefs were significantly related (positively) to all three measures of well-being, whereas entity beliefs were not significantly related to well-being. Regression analyses also found that incremental beliefs were positively related to satisfaction of autonomy and competence needs but were not related to satisfaction of relatedness needs. Entity beliefs were not related to the satisfaction of any of the three basic needs. A series of mediational analyses found that competence and autonomy motives mediated relationships between incremental beliefs and all three measures of well-being. In all but one case, satisfaction with life, the direct effects of incremental beliefs on well-being were rendered non-significant when satisfaction of autonomy and competence needs were included as mediators. The present results confirm and extend to the general domain the supposition that a mastery orientation is responsible for relationships between well-being and incremental theories of the self. They also conform the importance of the tenants of Self-Determination Theory in understanding self-theories.

Dialectics of Opportunities and Challenges in Disciplinary Shifting of ‘Rural Development’ into ‘Development Studies’

This paper aims to discuss the emerging trend and scope of Development Studies as an academic discipline. Methodologically, the paper is based on secondary sources of data. A comparative and argumentative method has been followed in a continuum approach to Rural Development and Development Studies. It argues that conventional particularistic approaches to development are being upgraded in the more general domain of Development Studies. Therefore, going with some national and international trends of studies in both disciplines, this paper offers a strong recommendation for the adoption of Development Studies with exploration and extended continuity of Rural Development. The paper does not, however, conclude with the immortality of the discipline of Development Studies but stands with its continuous evolution and contestation. Taking the reference of Nepal, the paper has highlighted different issues, opportunities, and challenges of such shifting in terms of quality of the program, courses and faculties, career and exposure, and research potential and policy dilemma. The paper is expected to contribute to the studies of different paradigms and discourses of development in their given contexts and conditions of socio-political realties.

Construções marcadoras discursivas formadas por olhar, no português, e guardare, no italiano: uma análise constrastivo-funcional

"Discourse markers formed by olhar in Portuguese and guardare in Italian: a contrastive-functional analysis. Discourse markers (DM) make up a hybrid category and little distinct from the others. As elements outside grammar, these constituents are difficult to delimit and, therefore, difficult to analyze. Based on this consideration, adopting a functionalist theoretical framework combined with the constructional approach to grammar, in the terms of Traugott and Trousdale (2013), Hilpert (2014) and Traugott (2021, 2022), among others, we proceeded to an analysis of a qualitative and contrastive nature of MD formed by the verbs of visual perception (Scheibman 2000) olhar, in Portuguese, and guardare, in Italian, in the detection of their correspondences and distinctions. We assume such MDs as specific pairs of form and meaning in contemporary uses of Portuguese and Italian, as procedural constructions, such as [olha só] and [guarda un po'], respectively. We found that these MDs act in calling attention, in monitoring interaction via manipulation of the virtually idealized attentional space, due to communicative purposes, as defended by Sambrana (2021, 2023), for Portuguese, and Oliveira and Lazzarotto (2022), for Italian. We found that they contribute to this functionality of the MDs researched cognitive processes of general domain, according to Bybee (2010), such as chunking and analogization, and also social cognition, according to Diessel (2017). Keywords: discourse markers; contrastive analysis; contexts of use; contemporary Portuguese; contemporary Italian."

Strategy-proof allocation of objects: A characterization result

This paper considers an allocation problem with a finite number of objects and unit-demand agents. The main result is a characterization of a class of strategy-proof price mechanisms on a general domain where preferences over pairs of objects and houses are rational, monotonic, and continuous. A mechanism belongs to this class if and only if the price space is restricted in a special way and, given this restriction, that the mechanism selects minimal equilibrium prices.

FinKENet: A Novel Financial Knowledge Enhanced Network for Financial Question Matching.

Question matching is the fundamental task in retrieval-based dialogue systems which assesses the similarity between Query and Question. Unfortunately, existing methods focus on improving the accuracy of text similarity in the general domain, without adaptation to the financial domain. Financial question matching has two critical issues: (1) How to accurately model the contextual representation of a financial sentence? (2) How to accurately represent financial key phrases in an utterance? To address these issues, this paper proposes a novel Financial Knowledge Enhanced Network (FinKENet) that significantly injects financial knowledge into contextual text. Specifically, we propose a multi-level encoder to extract both sentence-level features and financial phrase-level features, which can more accurately represent sentences and financial phrases. Furthermore, we propose a financial co-attention adapter to combine sentence features and financial keyword features. Finally, we design a multi-level similarity decoder to calculate the similarity between queries and questions. In addition, a cross-entropy-based loss function is presented for model optimization. Experimental results demonstrate the effectiveness of the proposed method on the Ant Financial question matching dataset. In particular, the Recall score improves from 73.21% to 74.90% (1.69% absolute).

The impact of COVID-19 on the quality of life among University students with obsessive-compulsive disorder

Background:&#x0D; The coronavirus disease 2019 (COVID-19) pandemic has necessitated precautionary measures to prevent the transmission of the virus, affecting individuals with obsessive-compulsive disorder (OCD). We investigated the impact of the COVID-19 pandemic on the quality of life of university students with OCD.&#x0D; Methods:&#x0D; Students with validated confirmed diagnosis of OCD at King Abdulaziz University, Jeddah, received two online surveys: the Yale-Brown Obsessive-Compulsive Scale (Y-BOCS) to approve the diagnosis and the 36-Item Short-Form Health Survey (SF-36) to assess symptom severity and overall quality of life during the pandemic. Students with psychotic disorders were excluded from this study.&#x0D; Results:&#x0D; The majority were females (62.5%), with an average age of 23.13 years. The participants had mild to moderate of OCD symptoms. The SF-36 role limitation due to emotional problems domain was significantly associated with the level of OCD symptoms (p=0.023), with higher scores reported for those with mild symptoms. Age was positively correlated with social functioning and pain domains but negatively correlated with Y-BOCS scores. The general health domain was significantly associated with current OCD symptoms and family history of OCD diagnosis.&#x0D; Conclusions:&#x0D; The self-reported decline in the severity of OCD symptoms in adult students indicates that the participants in our study with diagnosable OCD perceive an improvement in their symptoms from the previous year.

A hybrid method of combination probability and machine learning for Chinese geological text segmentation

To address the issues surrounding incomplete coverage of core dictionaries, limited training corpora, and low precision in Chinese geological text segmentation, a knowledge- and data-driven word segmentation method by combining combination probability and machine learning was proposed in this paper. We extracted mathematical feature information from terms in Chinese geological text to construct a Term Combination Probability Model (TCPM) for Chinese word combinations by integrating the combination features of geological terms and the Chinese writing styles under zero-sample conditions. The TCPM was used to extract geological terms with high combination characteristics as a user-defined dictionary, and then a geological corpus was constructed by using a general domain word segmentation method based on this dictionary. After a small amount of manual review and optimization, the geological corpus was trained with a BiLSTM-CRF model to segment Chinese geological text. The proposed method in this paper was tested using a regional geological survey report set in Henan Province, and the precision, recall, and F1-score of the method are 92.65%, 92.53%, and 92.59%, respectively. The experimental results demonstrated that this method, combined with the inherent knowledge features of geological text and the machine learning method, can assist in expanding the core dictionary for Chinese geological text segmentation based on zero-sample, and can improve the segmentation precision of Chinese geological text compared to simple general word segmentation methods or machine learning methods.

Relationship between physical and psychological functioning and health-related quality of life in congenital Aniridia.

Congenital aniridia is a serious eye disease characterized by absence of iris to various degrees. The aims of this study were to investigate health-related quality of life (HRQoL) in adults with aniridia and assess the relationships between HRQoL, psychological status, ocular health and obesity. Twenty-nine adults with congenital aniridia (48% male, aged 18-79 years) participated. HRQoL was measured with SF-36 and the EQ visual analogue scale (VAS). The physical (PCS) and mental (MCS) component summaries of the SF-36 were calculated with higher scores indicating better HRQoL. Symptoms of anxiety and depression were measured using the Hospital Anxiety and Depression Scale (HADS). Obesity was assessed with the Patient-Reported Outcomes in Obesity (PROS). Sociodemographic characteristics, genetic variants and ocular and medical health variables were also analysed. The participants scored significantly lower in the general health domain of the SF-36 than the general population (65.2 vs. 75.3, p = 0.017). The EQ VAS score was also lower in the aniridia group (64.9 vs. 77.9, p = 0.021). Low PCS score was correlated with presence of ocular pain (p = 0.019), high HADS score (p = 0.017) and high PROS score (p = 0.009). Low MCS score was related to higher educational level (p = 0.038) and high HADS score (p < 0.001). High HADS and PROS scores were both related to low EQ VAS scores. Adults with congenital aniridia scored worse on certain measures of HRQoL than the general population. Poorer HRQoL was associated with increased symptoms of anxiety, depression and obesity and with presence of ocular pain.

Anchored belief updating from recommendations

We study a belief updating behavior in a framework where information is presented as a recommendation from a menu of actions. We introduce a property on belief updating called order independence of recommendations, which is analogous to the Bayes’ Rule property of path independence of signals. We show that order independence and the properties that characterize the contraction rule of Ke et al. (2021) lead to an impossibility result on the general domain of recommendations. We then show that such a rule exists if and only if the domain is substantially restricted. Lastly, we propose the anchored contraction rule, which satisfies order independence on the general domain. The anchored contraction rule gives a theoretical ground for the asymmetric confirmation bias in the general domain of recommendations that a belief is updated if and only if information supports an ideal point.