Maciasshepherd1278

Sudden unexpected infant death (SUID), which includes the diagnosis of sudden infant death syndrome (SIDS), is a leading cause of infant mortality in the United States. Despite prevention efforts, many parents continue to create unsafe infant sleep environments and use potentially dangerous infant sleep and monitoring devices, ultimately leading to sleep-related infant deaths. Analyzing Facebook conversations regarding SIDS may offer a unique maternal perspective to guide future research and prevention efforts.

This study aims to describe and analyze conversations among mothers engaged in discussions about SIDS on a Facebook mother's group. We were interested in understanding maternal knowledge of SIDS, identifying information sources for SIDS, describing actual infant sleep practices, exploring opinions regarding infant sleep products and monitoring devices, and discovering evidence of provider communication regarding SIDS.

We extracted and analyzed 20 posts and 912 comments from 512 mothers who particovider and public health agency communication on the topic of SUID and safe sleep should be simple and clear, address infant sleep products and monitoring devices, address maternal anxiety regarding SIDS, and address the common practice of unsafe sleep.

Although sex toys representing human body parts are widely accepted and normalized, human-like full-body sex dolls and sex robots have elicited highly controversial debates.

This systematic scoping review of the academic literature on sex dolls and sex robots, the first of its kind, aimed to examine the extent and type of existing academic knowledge and to identify research gaps against this backdrop.

A comprehensive multidisciplinary, multidatabase search strategy was used. All steps of literature search and selection, data charting, and synthesis followed the leading methodological guideline, the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) checklist. A total of 29 (17 peer reviewed) and 98 publications (32 peer reviewed) for sex dolls and sex robots, respectively, from 1993 to 2019 were included.

According to the topics and methodologies, the sex doll and sex robot publications were divided into 5 and 6 groups, respectively. The majority of publications were theoretical papers. Thus far, no observational or experimental research exists that uses actual sex dolls or sex robots as stimulus material.

There is a need to improve the theoretical elaboration and the scope and depth of empirical research examining the sexual uses of human-like full-body material artifacts, particularly concerning not only risks but also opportunities for sexual and social well-being.

There is a need to improve the theoretical elaboration and the scope and depth of empirical research examining the sexual uses of human-like full-body material artifacts, particularly concerning not only risks but also opportunities for sexual and social well-being.

Suicide is an important public health concern in the United States and around the world. read more There has been significant work examining machine learning approaches to identify and predict intentional self-harm and suicide using existing data sets. With recent advances in computing, deep learning applications in health care are gaining momentum.

This study aimed to leverage the information in clinical notes using deep neural networks (DNNs) to (1) improve the identification of patients treated for intentional self-harm and (2) predict future self-harm events.

We extracted clinical text notes from electronic health records (EHRs) of 835 patients with International Classification of Diseases (ICD) codes for intentional self-harm and 1670 matched controls who never had any intentional self-harm ICD codes. The data were divided into training and holdout test sets. We tested a number of algorithms on clinical notes associated with the intentional self-harm codes using the training set, including several traditionaR. The modest performance on the predictive task notwithstanding, the results using DNN models on clinical text alone are competitive with other reports in the literature using risk factors from structured EHR data.

The strong performance on the first task, namely, phenotyping based on clinical notes, suggests that such models could be used effectively for surveillance of intentional self-harm in clinical text in an EHR. The modest performance on the predictive task notwithstanding, the results using DNN models on clinical text alone are competitive with other reports in the literature using risk factors from structured EHR data.

The ubiquity of smartphones and health apps make them a potential self-management tool for patients that could be prescribed by medical professionals. However, little is known about how Australian general practitioners and their patients view the possibility of prescribing mobile health (mHealth) apps as a nondrug intervention.

This study aimed to determine barriers and facilitators to prescribing mHealth apps in Australian general practice from the perspective of general practitioners and their patients.

We conducted semistructured interviews in Australian general practice settings with purposively sampled general practitioners and patients. The audio-recorded interviews were transcribed, coded, and thematically analyzed by two researchers.

Interview participants included 20 general practitioners and 15 adult patients. General practitioners' perceived barriers to prescribing apps included a generational difference in the digital propensity for providers and patients; lack of knowledge of prescribablef health professionals and patients is vital for the successful integration of effective, evidence-based mHealth apps with clinical practice.

mHealth app prescription appears to be feasible in general practice. The barriers and facilitators identified by the providers and patients overlapped, though privacy was of less concern to patients. The involvement of health professionals and patients is vital for the successful integration of effective, evidence-based mHealth apps with clinical practice.