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Journal of Behavioral and Cognitive... Mar 2021Eating disorders (EDs) are characterized by altered eating behaviors and valuation of self-image, as well as difficulty establishing supportive social relationships....
Eating disorders (EDs) are characterized by altered eating behaviors and valuation of self-image, as well as difficulty establishing supportive social relationships. This pilot study evaluated feasibility, acceptability, and clinical responses to a novel and brief group-therapy intervention for EDs, the Self-Blame and Perspective-Taking Intervention (SBPI). The SBPI consisted of four sessions of experiential art therapy activities in conjunction with psychoeducation targeting interpersonal attributions and mentalization. Twenty-four outpatient, treatment-seeking women with EDs participated in the SBPI, with 87.5% completing the intervention and 94% rating their participation positively. ED symptoms, depression, anxiety, self-attribution bias, and self-esteem were assessed before (T1) and after participation (N = 20 at T2; N = 18 at T3). Separate repeated measures MANOVAs were performed to assess these clinical and self-concept variables. Relative to baseline, participants demonstrated significant improvements in two all self-concept measures: self-attribution bias, trait self-esteem and state self-esteem at T2. ED, depression, and anxiety symptoms were significantly decreased at both T2 (1-4 weeks post) and T3 (3-5 months post). The SBPI altered self-concept targets acutely and led to sustained clinical improvements. Future work is needed to evaluate how self-concept and social constructs are related to clinical symptom expression in EDs.
PubMed: 34124699
DOI: 10.1016/j.jbct.2020.11.002 -
Environmental Research Letters : ERL... Mar 2022Understanding motivation to adopt personal household adaptation behaviors in the face of climate change-related hazards is essential for developing and implementing...
Understanding motivation to adopt personal household adaptation behaviors in the face of climate change-related hazards is essential for developing and implementing behaviorally realistic interventions that promote well-being and health. Escalating extreme weather events increase the number of those directly exposed and adversely impacted by climate change. But do people attribute these negative events to climate change? Such subjective attribution may be one cognitive process whereby the experience of negative climate change-related events may increase risk perceptions and motivate people to act. Here we surveyed a representative sample of 1,846 residents of Florida and Texas, many who had been repeatedly exposed to hurricanes on the Gulf Coast, facing the 2020 Atlantic hurricane season. We assessed prior hurricane negative personal experience, climate change-related subjective attribution (for hurricanes), risk appraisal (perceived probability and severity of a hurricane threat), hurricane adaptation appraisal (perceived efficacy of adaptation measures and self-efficacy to address the threat of hurricanes), and self-reported hurricane personal household adaptation. Our findings suggest that prior hurricane negative personal experiences and subjective attribution are associated with greater hurricane risk appraisal. Hurricane subjective attribution moderated the relationship between hurricane negative personal experiences and risk appraisal; in turn, negative hurricane personal experience, hurricane risk appraisal, and adaptation appraisal were positively associated with self-reported hurricane personal adaptation behaviors. Subjective attribution may be associated with elevated perceived risk for specific climate hazards. Communications that help people understand the link between their negative personal experiences (e.g., hurricanes) and climate change may help guide risk perceptions and motivate protective actions, particularly in areas with repeated exposure to threats.
PubMed: 36506931
DOI: 10.1088/1748-9326/ac4858 -
Comprehensive Psychiatry Aug 2021Previous studies report that income inequality is an important risk factor for depression and suicide, and an increasing income gap appears inevitable. However, little...
The influence of poverty attribution on attitudes toward suicide and suicidal thought: A cross-national comparison between South Korean, Japanese, and American populations.
BACKGROUND
Previous studies report that income inequality is an important risk factor for depression and suicide, and an increasing income gap appears inevitable. However, little study to date has investigated associations between the attribution of poverty and suicide. Though we previously reported associations between socio-cultural factors, including income, and suicide, we tried to explore more focused associations between income, attribution of poverty (individualistic, societal), permissive attitude toward suicide, and suicidal thought using a structural equation model.
METHODS
A total of 2213 participants from each of three nations (South Korea, Japan, and the United States) completed an online survey. Participants without a history of psychological disorders or suicide attempts completed scales measuring attributions of poverty, attitudes toward suicide, and severity of suicidal thoughts.
RESULTS
We established a structural equation model, which exhibited a good fit for all nations, and compared significant path coefficients by country. South Korea had the highest severity of suicidal thought and societal attribution of poverty, followed by Japan and America. In all nations, a permissive attitude was positively related to the severity of suicidal thought and individualistic attribution of poverty was positively related to a permissive attitude toward suicide. Societal attribution of poverty was positively associated with a permissive attitude in Japan and the United States. Income was negatively associated with the severity of suicide in South Korea and the United States.
CONCLUSION
Through an established structural equation model, we found the influence of poverty on suicide and identify the common and distinctive factors associated with suicide in each country.
Topics: Attitude; Humans; Japan; Poverty; Republic of Korea; Suicidal Ideation; United States
PubMed: 34273607
DOI: 10.1016/j.comppsych.2021.152259 -
Journal of Epidemiology Aug 2023Identifying which exposures cause disease and quantifying their impacts is essential in promoting and monitoring public health. When multiple exposures are involved,...
BACKGROUND
Identifying which exposures cause disease and quantifying their impacts is essential in promoting and monitoring public health. When multiple exposures are involved, measuring individual contributions becomes challenging.
METHODS
The authors propose a disease attribution method based on aggregate data or summary statistics of individual-level data, possibly from multiple data sources.
RESULTS
Using the proposed method, the burden of disease is apportioned to the independent and interaction effects of each of its major risk factors and all the other factors as a whole. This scheme guarantees that 100% is the total share of the burden.
CONCLUSION
The calculation is simple and straightforward; therefore, it is recommended for use in studies on disease burden.
Topics: Humans; Disease Attributes; Cost of Illness; Public Health; Japan; Causality
PubMed: 35283399
DOI: 10.2188/jea.JE20210084 -
Journal of Cheminformatics Jan 2023Explainable artificial intelligence (XAI) methods have shown increasing applicability in chemistry. In this context, visualization techniques can highlight regions of a...
BACKGROUND
Explainable artificial intelligence (XAI) methods have shown increasing applicability in chemistry. In this context, visualization techniques can highlight regions of a molecule to reveal their influence over a predicted property. For this purpose, some XAI techniques calculate attribution scores associated with tokens of SMILES strings or with atoms of a molecule. While an association of a score with an atom can be directly visually represented on a molecule diagram, scores computed for SMILES non-atom tokens cannot. For instance, a substring [N+] contains 3 non-atom tokens, i.e., [, [Formula: see text], and ], and their attributions, depending on the model, are not necessarily revealing an influence of the nitrogen atom over the predicted property; for that reason, it is not possible to represent the scores on a molecule diagram. Moreover, SMILES's notation is complex, foregrounding the need for techniques to facilitate the analysis of explanations associated with their tokens.
RESULTS
We propose XSMILES, an interactive visualization technique, to explore explainable artificial intelligence attributions scores and support the interpretation of SMILES. Users can input any type of score attributed to atom and non-atom tokens and visualize them on top of a 2D molecule diagram coordinated with a bar chart that represents a SMILES string. We demonstrate how attributions calculated for SMILES strings can be evaluated and better interpreted through interactivity with two use cases.
CONCLUSIONS
Data scientists can use XSMILES to understand their models' behavior and compare multiple modeling approaches. The tool provides a set of parameters to adapt the visualization to users' needs and it can be integrated into different platforms. We believe XSMILES can support data scientists to develop, improve, and communicate their models by making it easier to identify patterns and compare attributions through interactive exploratory visualization.
PubMed: 36609340
DOI: 10.1186/s13321-022-00673-w -
Frontiers in Psychology 2023Attributing mental states to others, such as feelings, beliefs, goals, desires, and attitudes, is an important interpersonal ability, necessary for adaptive...
Attributing mental states to others, such as feelings, beliefs, goals, desires, and attitudes, is an important interpersonal ability, necessary for adaptive relationships, which underlies the ability to mentalize. To evaluate the attribution of mental and sensory states, a new 23-item measure, the Attribution of Mental States Questionnaire (AMS-Q), has been developed. The present study aimed to investigate the dimensionality of the AMS-Q and its psychometric proprieties in two studies. Study 1 focused on the development of the questionnaire and its factorial structure in a sample of Italian adults ( = 378). Study 2 aimed to confirm the findings in a new sample ( = 271). Besides the AMS-Q, Study 2 included assessments of Theory of Mind (ToM), mentalization, and alexithymia. A Principal Components Analysis (PCA) and a Parallel Analysis (PA) of the data from Study 1 yielded three factors assessing mental states with positive or neutral valence (AMS-NP), mental states with negative valence (AMS-N), and sensory states (AMS-S). These showed satisfactory reliability indexes. AMS-Q's whole-scale internal consistency was excellent. Multigroup Confirmatory Factor Analysis (CFA) further confirmed the three-factor structure. The AMS-Q subscales also showed a consistent pattern of correlation with associated constructs in the theoretically predicted ways, relating positively to ToM and mentalization and negatively to alexithymia. Thus, the questionnaire is considered suitable to be easily administered and sensitive for assessing the attribution of mental and sensory states to humans. The AMS-Q can also be administered with stimuli of nonhuman agents (e.g., animals, inanimate things, and even God); this allows the level of mental anthropomorphization of other agents to be assessed using the human as a term of comparison, providing important hints in the perception of nonhuman entities as more or less mentalistic compared to human beings, and identifying what factors are required for the attribution of human mental traits to nonhuman agents, further helping to delineate the perception of others' minds.
PubMed: 36895742
DOI: 10.3389/fpsyg.2023.999921 -
One Health Outlook Sep 2021Bacterial Foodborne Pathogens (FBP) are the commonest cause of foodborne illness or foodborne diseases (FBD) worldwide. They contaminate food at any stages in the entire... (Review)
Review
Prevalence and epidemiological distribution of selected foodborne pathogens in human and different environmental samples in Ethiopia: a systematic review and meta-analysis.
Bacterial Foodborne Pathogens (FBP) are the commonest cause of foodborne illness or foodborne diseases (FBD) worldwide. They contaminate food at any stages in the entire food chain, from farm to dining-table. Among these, the Diarrheagenic Escherichia coli (DEC), Non typhoidal Salmonella (NTS), Shigella spp. and Campylobacter spp. are responsible for a large proportion of illnesses, deaths; and, particularly, as causes of acute diarrheal diseases. Though existing studies indicate the problem may be severe in developing countries like Ethiopia, the evidence is commonly based on fragmented data from individual studies. A review of published and unpublished manuscripts was conducted to obtain information on major FBP and identify the gaps in tracking their source attributions at the human, animal and environmental interface. A total of 1753 articles were initially retrieved after restricting the study period to between January 2000 and July 2020. After the second screening, only 51 articles on the humans and 43 on the environmental sample based studies were included in this review. In the absence of subgroups, overall as well as human stool and environmental sample based pooled prevalence estimate of FBP were analyzed. Since, substantial heterogeneity is expected, we also performed a subgroup analyses for principal study variables to estimate pooled prevalence of FBP at different epidemiological settings in both sample sources. The overall random pooled prevalence estimate of FBP (Salmonella, pathogenic Escherichia coli (E. coli), Shigella and Campylobacter spp.) was 8%; 95% CI: 6.5-8.7, with statistically higher (P < 0.01) estimates in environmental samples (11%) than in human stool (6%). The subgroup analysis depicted that Salmonella and pathogenic E. coli contributed to 5.7% (95% CI: 4.7-6.8) and 11.6% (95% CI: 8.8-15.1) respectively, of the overall pooled prevalence estimates of FBD in Ethiopia. The result of meta-regression showed, administrative regional state, geographic area of the study, source of sample and categorized sample size all significantly contributed to the heterogeneity of Salmonella and pathogenic E. coli estimates. Besides, the multivariate meta- regression indicated the actual study year between 2011 and 2015 was significantly associated with the environmental sample-based prevalence estimates of these FBP. This systematic review and meta-analysis depicted FBP are important in Ethiopia though majority of the studies were conducted separately either in human, animal or environmental samples employing routine culture based diagnostic method. Thus, further FBD study at the human, animal and environmental interface employing advanced diagnostic methods is needed to investigate source attributions of FBD in one health approach.
PubMed: 34474688
DOI: 10.1186/s42522-021-00048-5 -
Frontiers in Artificial Intelligence 2021Literary narratives regularly contain passages that different readers attribute to different speakers: a character, the narrator, or the author. Since literary...
Literary narratives regularly contain passages that different readers attribute to different speakers: a character, the narrator, or the author. Since literary narratives are highly ambiguous constructs, it is often impossible to decide between diverging attributions of a specific passage by hermeneutic means. Instead, we hypothesise that attribution decisions are often influenced by annotator bias, in particular an annotator's literary preferences and beliefs. We present first results on the correlation between the literary attitudes of an annotator and their attribution choices. In a second set of experiments, we present a neural classifier that is capable of imitating individual annotators as well as a common-sense annotator, and reaches accuracies of up to 88% (which improves the majority baseline by 23%).
PubMed: 35187471
DOI: 10.3389/frai.2021.725321 -
Mathematical Biosciences and... Feb 2022Scientific documents contain a large number of mathematical expressions and texts containing mathematical semantics. Simply using mathematical expressions or text to...
Scientific documents contain a large number of mathematical expressions and texts containing mathematical semantics. Simply using mathematical expressions or text to retrieve scientific documents can hardly meet retrieval needs. The real difficulty in retrieving scientific documents is to effectively integrate mathematical expressions and related textual features. Therefore, this study proposes a multi-attribute scientific documents retrieval and ranking model based on GBDT (gradient boosting decision tree) and LR (logistic regression) by integrating the expressions and text contained in scientific documents. First, the similarities of the five attributes are calculated, including mathematical expression symbols, mathematical expression sub-forms, mathematical expression context, scientific document keywords and the frequency of mathematical expressions. Next, the GBDT model is used to discretize and reorganize the five attributes. Finally, the reorganized features are input into the LR model, and the final retrieval and ranking results of scientific documents are obtained. The experiment in this study was carried out on the NTCIR dataset. The average value of the final MAP@20 of the scientific document recall was 81.92%. The average value of the scientific document ranking nDCG@20 was 86.05%.
Topics: Logistic Models; Semantics
PubMed: 35341272
DOI: 10.3934/mbe.2022172 -
Nature Communications Apr 2024Intertemporal choices - decisions that play out over time - pervade our life. Thus, how people make intertemporal choices is a fundamental question. Here, we investigate...
Intertemporal choices - decisions that play out over time - pervade our life. Thus, how people make intertemporal choices is a fundamental question. Here, we investigate the role of attribute latency (the time between when people start to process different attributes) in shaping intertemporal preferences using five experiments with choices between smaller-sooner and larger-later rewards. In the first experiment, we identify attribute latencies using mouse-trajectories and find that they predict individual differences in choices, response times, and changes across time constraints. In the other four experiments we test the causal link from attribute latencies to choice, staggering the display of the attributes. This changes attribute latencies and intertemporal preferences. Displaying the amount information first makes people more patient, while displaying time information first does the opposite. These findings highlight the importance of intra-choice dynamics in shaping intertemporal choices and suggest that manipulating attribute latency may be a useful technique for nudging.
Topics: Humans; Animals; Mice; Time Factors; Delay Discounting; Reward; Reaction Time; Choice Behavior
PubMed: 38580626
DOI: 10.1038/s41467-024-46657-2