This study introduces new measures of ethnicity‐related stress and a newly adapted measure of ethnic identity. Ethnicity‐related stressors assessed in this study were perceived discrimination, stereotype confirmation concern, and own‐group conformity pressure. Ethnic identity refers to the subjective sense of ethnic group membership and, following Luhtanen and Crocker (1992), was assessed as public regard, identity centrality, and private feelings. Data for 333 undergraduates from diverse ethnic groups indicated that the measures are psychometrically sound. Ethnic group differences for mean scores demonstrated the measures’ known‐groups validity. Cross‐sectional analyses indicated that ethnicity‐related stress and identity constructs captured by the instruments are related to measures of psychological and physical well‐being. The new measures may be useful in the investigation of psychological aspects of ethnicity and their adaptive consequences.

Objective: Cardiovascular recovery of prestress baseline blood pressure has been implicated as a possible additional determinant of sustained blood pressure elevation. We hypothesize that angry ruminations may slow the recovery process. Method: A within-subjects design was used in which resting baseline blood pressure and heart rate measurements were assessed on 60 subjects, who then took part in two anger-recall tasks. After each task, subjects sat quietly and alone during a 12-minute recovery period randomized to with or without distractions. During baseline, task, and recovery, blood pressure was continuously monitored; during recovery, subjects reported their thoughts at five fixed intervals. Results: Fewer angry thoughts were reported in the distraction condition (17%) compared with no distraction (31%; p ϭ .002); an interaction showed that this effect was largely the result of the two intervals immediately after the anger-recall task. Trait rumination interacted with distraction condition such that high ruminators in the no-distraction condition evidenced the poorest blood pressure recovery, assessed as area under the curve (p ϭ .044 [systolic blood pressure] and p ϭ .046 [diastolic pressure]). Conclusions: People who have a tendency to ruminate about past anger-provoking events may be at greater risk for target organ damage as a result of sustained blood pressure elevations; the effect is exacerbated when distractions are not available to interrupt the ruminative process.

This article reports a prospective study of religiousness and recovery from heart surgery. Religiousness and other psychosocial factors were assessed in 142 patients about a week prior to surgery. Those with stronger religious beliefs subsequently had fewer complications and shorter hospital stays, the former effect mediating the latter. Attendance at religious services was unrelated to complications but predicted longer hospitalizations. Prayer was not related to recovery. Depressive symptoms were associated with longer hospital stays. Dispositional optimism, trait hostility, and social support were unrelated to outcomes. Effects of religious beliefs and attendance were stronger among women than men and were independent of biomedical and other psychosocial predictors. These findings encourage further examination of differential health effects of the various elements of religiousness.

Early research on ethnicity focused on the stereotyped thinking, prejudiced attitudes, and discriminatory actions of Euro-Americans. Minoritygroup members were viewed largely as passive targets of these negative reactions, with low self-esteem studied as the main psychological outcome. By contrast, recent research has increasingly made explicit use of stress theory in emphasizing the perspectives and experiences of minority-group members. Several ethnicity-related stressors have been identified, and it has been found that individuals cope with these threats in an active, purposeful manner. In this article, we focus on ethnicity-related stress stemming from discrimination, from stereotypes, and from conformity pressure arising from one's own ethnic group. We discuss theory and review research in which examination of ethnicity-related outcomes has extended beyond self-esteem to include psychological and physical well-being.

Recent pre-trained abstractive summarization systems have started to achieve credible performance, but a major barrier to their use in practice is their propensity to output summaries that are not faithful to the input and that contain factual errors. While a number of annotated datasets and statistical models for assessing factuality have been explored, there is no clear picture of what errors are most important to target or where current techniques are succeeding and failing. We explore both synthetic and human-labeled data sources for training models to identify factual errors in summarization, and study factuality at the word-, dependency-, and sentence-level. Our observations are threefold. First, exhibited factual errors differ significantly across datasets, and commonly-used training sets of simple synthetic errors do not reflect errors made on abstractive datasets like XSUM. Second, human-labeled data with fine-grained annotations provides a more effective training signal than sentence-level annotations or synthetic data. Finally, we show that our best factuality detection model enables training of more factual XSUM summarization models by allowing us to identify non-factual tokens in the training data. 1 Reference Summary: An early-medieval gold pendant created from an imitation of a Byzantine coin that was found in a Norfolk field is a "rare find", a museum expert has said.Source Article Fragment: Discovered on land at North Elmham, near Dereham, the circa 600 AD coin was created by French rulers of the time to increase their available currency. […] The pendant was declared treasure by the Norfolk coroner on Wednesday.An 18th century coin believed to be worth more than #1m has been discovered.A gold pendant created from a necklace was found in a field Entitycentric (Ent-C)The pendant was declared a treasure by the Norfolk coroner on Wednesday.The pendant was declared a treasure by the Ohio coroner on March. Generationcentric (Gen-C) Label Human Annotation non-factual span non-factual arc (factual arcs not shown) Sentences Training Dataset

Paraphrasing natural language sentences is a multifaceted process: it might involve replacing individual words or short phrases, local rearrangement of content, or high-level restructuring like topicalization or passivization. Past approaches struggle to cover this space of paraphrase possibilities in an interpretable manner. Our work, inspired by pre-ordering literature in machine translation, uses syntactic transformations to softly "reorder" the source sentence and guide our neural paraphrasing model. First, given an input sentence, we derive a set of feasible syntactic rearrangements using an encoder-decoder model. This model operates over a partially lexical, partially syntactic view of the sentence and can reorder big chunks. Next, we use each proposed rearrangement to produce a sequence of position embeddings, which encourages our final encoder-decoder paraphrase model to attend to the source words in a particular order. Our evaluation, both automatic and human, shows that the proposed system retains the quality of the baseline approaches while giving a substantial increase in the diversity of the generated paraphrases. 1

Both preoperative depressive symptoms and postoperative increases in depressive symptoms seem associated with poorer QOL 6 months after cardiac surgery. Further examination of these associations and the mechanisms they reflect may provide a basis for guiding treatment decisions before and after coronary artery bypass graft surgery.

Despite significant progress in text generation models, a serious limitation is their tendency to produce text that is factually inconsistent with information in the input. Recent work has studied whether textual entailment systems can be used to identify factual errors; however, these sentence-level entailment models are trained to solve a different problem than generation filtering and they do not localize which part of a generation is non-factual. In this paper, we propose a new formulation of entailment that decomposes it at the level of dependency arcs. Rather than focusing on aggregate decisions, we instead ask whether the semantic relationship manifested by individual dependency arcs in the generated output is supported by the input. Human judgments on this task are difficult to obtain; we therefore propose a method to automatically create data based on existing entailment or paraphrase corpora. Experiments show that our dependency arc entailment model trained on this data can identify factual inconsistencies in paraphrasing and summarization better than sentencelevel methods or those based on question generation, while additionally localizing the erroneous parts of the generation. 1