Markersvenstrup5175

psychiatric disorders with cannabinoids.

This study examined psychosocial and mental health characteristics associated with COVID-19 infection.

An online survey that asked about COVID-19 status, social support, and mental health was used to recruit a national sample of 6,607 low- and middle-income adults; 354 reported a positive COVID-19 test, 1,819 reported a negative test, and 4,434 reported not being tested in May or June 2020.

Psychiatric history and current social support and mental health were not statistically significantly associated with testing positive for COVID-19 after analyses controlled for other characteristics. In order of magnitude, having any friends or family who had COVID-19, being a veteran, having a greater number of close friends or relatives, having any history of homelessness, having an advanced degree, or being a student was significantly associated with testing positive for COVID-19.

Clinical risk for COVID-19 infection and the medical needs of veterans and of unstably housed populations should be considered.

Clinical risk for COVID-19 infection and the medical needs of veterans and of unstably housed populations should be considered.

Little is known about clients' preferences for family involvement and subsequent family contact in naturalistic, community-based coordinated specialty care (CSC) settings. The study's primary goal was to characterize clients' preferences and longitudinal patterns of family contact with providers across the OnTrackNY network in New York.

Clinical administrative data collected at 3-month intervals and spanning 21 OnTrackNY CSC sites were used to analyze the preferences of 761 clients at baseline (unconditional involvement, conditional involvement, or no involvement) and patterns of family contact with program staff (always, sometimes, never, or early discharge) and their correlates during the initial 12-month service period. Data from clients discharged before 12 months were included for comparison.

At baseline, most clients requested some form of family involvement (unconditional, 59%; conditional, 35%; and none, 6%). see more Within each 3-month assessment period, rates of family contact ranged from 73% to 84%. t levels of family contact.

To identify geographic variation in mental health service use in the Department of Veterans Affairs (VA), the authors constructed utilization-based VA mental health service areas (MHSAs) for outpatient treatment and mental health referral regions (MHRRs) for residential and acute inpatient treatment.

MHSAs are empirically derived geographic groupings of one or more counties containing one or more VA outpatient mental health clinics. For each county within an MHSA, patients received most of their VA-provided outpatient mental health care within that MHSA. MHSAs were aggregated into MHRRs according to where VA users in each MHSA received most of their residential and acute inpatient mental health care. Attribution loyalty was evaluated with the localization index-the fraction of VA users living in each geographic area who used their designated MHSA and MHRR facility. Variation in outpatient mental health visits and in acute inpatient and residential mental health stays was determined for the 2008-2018 period.

A total of 441 MHSAs were aggregated to 115 MHRRs (representing 3,909,080 patients with 52,372,303 outpatient mental health visits). The mean±SD localization index was 59.3%±16.4% for MHSAs and 67.8%±12.7% for MHRRs. Adjusted outpatient mental health visits varied from a mean of 0.88 per year in the lowest quintile of MHSAs to 3.14 in the highest. Combined residential and acute inpatient days varied from 0.29 to 1.79 between the lowest and highest quintiles.

MHSAs and MHRRs validly represented mental health utilization patterns in the VA and displayed considerable variation in mental health service provision across different locations.

MHSAs and MHRRs validly represented mental health utilization patterns in the VA and displayed considerable variation in mental health service provision across different locations.

Critical care trials are limited by problems with participant recruitment, and little is known about the most effective ways to enhance trial participation. Despite clinical research improving in the past decades within intensive care, participant recruitment remains a challenge. Not all eligible patients are identified, and opportunities for enrolment into clinical trials are often missed. Interventions to facilitate recruitment need to be identified to improve trial conduct in the critical care environment. Therefore, we aimed to establish the effectiveness of recruitment strategies in critical care trials in order to inform future research practice.

Databases including MEDLINE, Embase, CINAHL and PsycINFO were searched for English language papers from inception to February 2020. The objectives were to (1) establish the effectiveness of recruitment strategies and (2) recommend how effective recruitment strategies can inform research practice. Two reviewers independently assessed papers for inclusion andre required to enhance recruitment and the representativeness of the patient sample obtained in critical care trials, in order to expand the evidence base for treatments in this field. Greater focus is needed on assessing the performance of different recruitment strategies within different types of studies and critical care research environments. Future research should explore key stakeholders' experiences of, and attitudes towards, recruitment and establish the most important and feasible modifiable barriers to recruitment.

To predict post-operative depth of focus (DoF) using machine learning techniques after cataract surgery with Tecnis Symfony implantation and determine associated impact factors.

This was a retrospective cohort study among patients receiving Tecnis Symfony implantation, an extended-range-of-vision intraocular lens, during October 2016-January 2020 at Daqing Oilfield General Hospital, China. Four different predictive models were used to predict good post-operative DoF (⩾2.5 D) Extreme Gradient Boost (XGBoost), random forest (RF), LASSO penalized regression, and multivariable logistic regression (MLR). Apriori algorithm was employed to further explore the association between patient attributes and DoF.

A total of 182 unique cases (143 patients) were included. The XGBoost model produced the best predictive accuracy compared to RF, LASSO, and MLR models. Overall performance of the best fitting XGBoost model was as follows accuracy = 70.3%, AUC = 80.2%, sensitivity = 65.5%, and specificity = 87.5%. The Apriori algorithm identified six preoparative attributes with substantial effects on good post-operative DoF low anterior chamber depth (ACD) (1.