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Prediction of life expectancy in terminally ill patients is an important end-of-life care issue for patients, families and mental health workers during the last days of life. This study was conducted to examine the importance/usefulness for patients/families to have an accurate prognosis and its impact on planning their activities prior to death. All patients admitted during a period of one year were included. Patients' and families' viewpoints on the usefulness of an accurate prognosis was documented at admission. There were 285 patients in the cohort. The median time to death was 8 days. Most families (83%) rated the importance of an accurate prognosis as moderately (13%) to very much useful (70%). A total of 42% of patients were able to complete e the questionnaire. Among these, 58% found it moderately to very much useful. For families, having an accurate prognosis influenced the planning of visits (69%), communication/closure (42%) and spiritual needs/funeral arrangements (31%). Patients identified planning of visits (10%), communication/closure (12%), and goals/accomplishments (9%) as very important. Discussing the prognosis and its impact is very helpful for the mental health professionals to have open and honest conversations with patients/families to identify, prioritize and adapt treatment to achieve goals prior to death. [ABSTRACT FROM AUTHOR]

Brand communities are of great interest to marketers, and motivating participation in these communities has been explored from many different perspectives. The current study examined how men and women differ in the motives (i.e., entertainment, receiving and sharing information, social integration, self-discovery, and status enhancement) behind their behavior rather than the more often discussed difference between men and women regarding actual behavior and attitudes. A survey sample (N = 195) was collected to explore this, and the data were analyzed with PLS-SEM. The model of participation motives for women was compared to the model for men, and differences (and similarities) were analyzed. The findings indicate that the genders have different motivations to participate in these brand communities. Specifically, the self-discovery and sharing information motives were significantly more important for males, while the social integration motive was significantly more important for females. However, the genders had no differences regarding entertainment, receiving information, and status enhancement motives. [ABSTRACT FROM AUTHOR]

To identify profiles of aging by combining psychological distress, cognition and functional disability, and their associated factors. Data were drawn from the Étude sur la Santé des Aînés-Services study and included 1585 older adults. Sociodemographic, psychosocial, lifestyle and health factors were informed from structured interviews. Group-based multi-trajectory modeling and multinomial logistic regression were used to identify aging profiles and correlates. Sampling weights were applied to account for the sampling plan. The weighted sample size was 1591. Three trajectories were identified: a favorable (79.0%), intermediate (14.5%), and severe scenario (6.5%). Factors associated with the severe scenario were older age, male gender, lower education, the presence of anxiety disorders, low physical activity, and smoking. Membership in the intermediate scenario was associated with daily hassles, physical disorders, anxiety and depression, antidepressant/psychotherapy use, low physical activity, and no alcohol use. High social support was protective against less favorable profiles. Symptoms of anxiety and depression and high burden of physical disorders were associated with less favorable trajectories. Modifiable lifestyle factors have a significant effect on healthy aging. Assessment and management of anxio-depressive symptoms are important in older adults. Clinical interventions including access to psychotherapy and promotion of healthier lifestyles should be considered. [ABSTRACT FROM AUTHOR]

Approximately 3.5% of deliveries in Canada result in potentially preventable neonatal readmission, often times due to preventable morbidities. With complexities in hospital discharge planning, health care providers may benefit in identifying infants at risk of readmission for additional monitoring. To develop and validate models for predicting 7-day neonatal readmission following vaginal or cesarean births. All liveborn term singleton infants without congenital anomalies in the province of Alberta who were not admitted to the NICU were identified using perinatal and hospitalization databases. A temporal split-sample was used for model development (2012–2014, vaginal n = 63,378; cesarean n = 21,225) and external validation (2014–2015, vaginal n = 21,583, cesarean n = 7,477). Multivariable logistic regression models using backward stepwise selection were used to identify predictors of 7-day readmission. We evaluated predictors of maternal age, Apgar score, length-of-stay, birthweight, gestational age, parity, residence, and sex. Hosmer-Lemeshow test and c-statistics were used to estimate calibration and discrimination. The rate of readmission was 3.3% (95% CI 3.1%, 3.4%) and 2.1% (95% CI 1.9%, 2.3%) following vaginal and cesarean births in the development dataset. Prediction model following vaginal birth, excluding predictors of length-of-stay and birthweight, had sub-optimal performance in development (c-statistics 0.69) and validation data (c-statistics 0.68). Prediction model following cesarean birth, excluding predictors of maternal age, birthweight, and residence, had sub-optimal performance in development (c-statistics 0.62) and validation data (c-statistics 0.64). Readmission was observed in 7.9% (95% CI 7.1%, 8.8%) and 4.9% (95% CI 3.9%, 6.1%) of infants of vaginal and cesarean births, respectively, in the top quintile for the risk of 7-day readmission. Using routinely collected administrative data, we developed and validated prediction models for neonatal readmission following vaginal and cesarean births. Presently the model is sub-optimal for use in risk assessment and planning at discharge, however, additional information may improve the predictive performance. [ABSTRACT FROM AUTHOR]

This paper investigates how to best compare algorithms for predicting chronic homelessness for the purpose of identifying good candidates for housing programs. Predictive methods can rapidly refer potentially chronic shelter users to housing but also sometimes incorrectly identify individuals who will not become chronic (false positives). We use shelter access histories to demonstrate that these false positives are often still good candidates for housing. Using this approach, we compare a simple threshold method for predicting chronic homelessness to the more complex logistic regression and neural network algorithms. While traditional binary classification performance metrics show that the machine learning algorithms perform better than the threshold technique, an examination of the shelter access histories of the cohorts identified by the three algorithms show that they select groups with very similar characteristics. This has important implications for resource constrained not-for-profit organizations since the threshold technique can be implemented using much simpler information technology infrastructure than the machine learning algorithms. [ABSTRACT FROM AUTHOR]