Hispanic Journal of Behavioral Sciences最新文献

Background: Percutaneous electrical nerve field stimulation (PENFS) improves symptoms in adolescents with functional abdominal pain disorders (FAPDs). However, little is known about its impact on sleep and psychological functioning. We evaluated the effects of PENFS on resting and evoked pain and nausea, sleep and psychological functioning, and long-term outcomes.

Methods: Patient ages 11-19 years with FAPD requiring PENFS as standard care were recruited. Evoked pain was elicited by a Water Load Symptom Provocation Task (WL-SPT) before and after four weeks of treatment. Pain, gastrointestinal symptoms, sleep, somatic symptoms, and physical and psychological functioning were assessed. Actigraphy was used to measure daily sleep-wake patterns.

Key results: Twenty patients (14.3 ± 2.2 years old) with FAPD were enrolled. Most patients were females (70%) and white (95%). During pain evoked by WL-SPT, visual analog scale (VAS) pain intensity and nausea were lower following PENFS compared with baseline (p = 0.004 and p = 0.02, respectively). After PENFS, resting VAS pain unpleasantness (p = 0.03), abdominal pain (p < 0.0001), pain catastrophizing (p = 0.0004), somatic complaints (0.01), functional disability (p = 0.04), and anxiety (p = 0.02) exhibited significant improvements, and some were sustained long-term. Self-reported sleep improved after PENFS (p's < 0.05) as well as actigraphy-derived sleep onset latency (p = 0.03).

Conclusions and inferences: We demonstrated improvements in resting and evoked pain and nausea, sleep, disability, pain catastrophizing, somatic complaints, and anxiety after four weeks of PENFS therapy. Some effects were sustained at 6-12 months post-treatment. This suggests that PENFS is a suitable alternative to pharmacologic therapy.

Clickbait is the use of an enticing title as bait to deceive users to click. However, the corresponding content is often disappointing, infuriating or even deceitful. This practice has brought serious damage to our social trust, especially to online media, which is one of the most important channels for information acquisition in our daily life. Currently, clickbait is spreading on the internet and causing serious damage to society. However, research on clickbait detection has not yet been well performed. Almost all existing research treats clickbait detection as a binary classification task and only uses the title as the input. This shallow usage of information and detection technology not only suffers from low performance in real detection (e.g., it is easy to bypass) but is also difficult to use in further research (e.g., potential empirical studies). In this work, we proposed a novel clickbait detection model that incorporated a knowledge graph, a graph convolutional network and a graph attention network to conduct fine-grained-level clickbait detection. According to experiments using a real dataset, our novel proposed model outperformed classical and state-of-the-art baselines. In addition, certain explainability can also be achieved in our model through the graph attention network. Our fine-grained-level results can provide a measurement foundation for future empirical study. To the best of our knowledge, this is the first attempt to incorporate a knowledge graph and deep learning technique to detect clickbait and achieve explainability.