Promotoras as Mental Health Practitioners in Primary Care: A Multi-Method Study of an Intervention to Address Contextual Sources of Depression

We assessed the role of promotoras—briefly trained community health workers—in depression care at community health centers. The intervention focused on four contextual sources of depression in underserved, low-income communities: underemployment, inadequate housing, food insecurity, and violence. A multi-method design included quantitative and ethnographic techniques to study predictors of depression and the intervention’s impact. After a structured training program, primary care practitioners (PCPs) and promotoras collaboratively followed a clinical algorithm in which PCPs prescribed medications and/or arranged consultations by mental health professionals and promotoras addressed the contextual sources of depression. Based on an intake interview with 464 randomly recruited patients, 120 patients with depression were randomized to enhanced care plus the promotora contextual intervention, or to enhanced care alone. All four contextual problems emerged as strong predictors of depression (chi square, p < .05); logistic regression revealed housing and food insecurity as the most important predictors (odds ratios both 2.40, p < .05). Unexpected challenges arose in the intervention’s implementation, involving infrastructure at the health centers, boundaries of the promotoras’ roles, and “turf” issues with medical assistants. In the quantitative assessment, the intervention did not lead to statistically significant improvements in depression (odds ratio 4.33, confidence interval overlapping 1). Ethnographic research demonstrated a predominantly positive response to the intervention among stakeholders, including patients, promotoras, PCPs, non-professional staff workers, administrators, and community advisory board members. Due to continuing unmet mental health needs, we favor further assessment of innovative roles for community health workers

Accuracy comparison of machine learning algorithms for predictive analytics in higher education

In this research, we compared the accuracy of machine learning algorithms that could be used for predictive analytics in higher education. The proposed experiment is based on a combination of classic machine learning algorithms such as Naive Bayes and Random Forest with various ensemble methods such as Stochastic, Linear Discriminant Analysis (LDA), Tree model (C5.0), Bagged CART (treebag) and K Nearest Neighbors (KNN). We applied traditional classification methods to classify the students’ performance and to determine the independent variables that offer the highest accuracy. Our results depict that the data with the 11 features using random forest generated the best accuracy value of 0.7333. However, we revised the experiment with ensemble algorithms to reduce the variance (bagging), bias (boosting) and to improve the prediction accuracy (stacking). Consequently, the bagging random forest outperformed other methods with the accuracy value of 0.7959

Predicting Student Dropouts in Higher Education Using Supervised Classification Algorithms

The aim of this paper is to predict, on a purely algorithmic basis, students who are at risk of dropping out of university. Data used in this study originated from the University of Bari Aldo Moro, during 2013–16, and were provided by the Osservatorio Studenti-Didattica of Miur-Cineca. Data analysis is based solely on the information set available, for each student, inside the university information system. Predictions of individual dropouts have been carried out by means of suitable Machine Learning techniques, known as supervised classification algorithms

Sexual orientation differences in treatment expectation, alliance, and outcome among patients at risk for suicide in a public psychiatric hospital.

Sexual minority (SM) individuals (gay, lesbian, bisexual, or otherwise nonheterosexual) are at increased risk for mental disorders and suicide and adequate mental healthcare may be life-saving. However, SM patients experience barriers in mental healthcare that have been attributed to the lack of SM-specific competencies and heterosexist attitudes and behaviors on the part of mental health professionals. Such barriers could have a negative impact on common treatment factors such as treatment expectancy or therapeutic alliance, culminating in poorer treatment outcomes for SM versus heterosexual patients. Actual empirical data from general psychiatric settings is lacking, however. Thus, comparing the treatment outcome of heterosexual and SM patients at risk for suicide was the primary aim of this study. The secondary aim was to compare treatment expectation and working alliance as two common factors. We report on 633 patients from a suicide prevention inpatient department within a public psychiatric hospital. Most patients were at risk for suicide due to a recent suicide attempt or warning signs for suicide, usually in the context of a severe psychiatric disorder. At least one indicator of SM status was reported by 21% of patients. We assessed the treatment outcome by calculating the quantitative change in suicide ideation, hopelessness, and depression. We also ran related treatment responder analyses. Treatment expectation and working alliance were the assessed common factors. Contrary to the primary hypothesis, SM and heterosexual patients were comparable in their improvement in suicide ideation, hopelessness, or depression, both quantitatively and in treatment responder analysis. Contrary to the secondary hypothesis, there were no significant sexual orientation differences in treatment expectation and working alliance. When adjusting for sociodemographics, diagnosis, and length of stay, some sexual orientation differences became significant, indicating that SM patients have better outcomes. These unexpected but positive findings may be due to common factors of therapy compensating for SM-specific competencies. It may also be due to actual presence of SM competencies - though unmeasured - in the department. Replication in other treatment settings and assessment of SM-specific competencies are needed, especially in the field of suicide prevention, before these findings can be generalized

An effective way of designing blended learning: A three phase design-based research approach

Online learning is common in higher education, but has its drawbacks. As a result, blended learning (BL) has emerged as an alternative to alleviate the challenges of online learning. The purpose of this design-based research study was to determine what elements were needed to assist a higher education instructor inexperienced in designing and teaching a BL course to successfully create and implement it, and to document the instructor’s perceptions about the first experience of teaching a BL course. The BL course was designed, implemented and redesigned to make the BL course an effective and efficient learning environment through the three phases of this design-based research. Qualitative and quantitative research methods including instructor interviews, learning environment observations and student surveys were employed to collect data. Results indicated that iterative analysis, design and evaluation of the created BL course provided an opportunity for the researchers to find applicable solutions to any real-world problems that the instructor faced in the course. Besides, the design and implementation of BL led the instructor to shift from a passive teaching approach to an active teaching approach and allowed the students to become active and interactive learners through the process of three iterative design cycles. Although challenges were identified, she had an overall positive perception toward teaching the BL course. © 2019, Springer Science+Business Media, LLC, part of Springer Nature