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- All Subjects: Depression
- Creators: Davis, Mary
- Creators: Guthrey, Ann
- Resource Type: Text
We attempted to apply a novel approach to stock market predictions. The Logistic Regression machine learning algorithm (Joseph Berkson) was applied to analyze news article headlines as represented by a bag-of-words (tri-gram and single-gram) representation in an attempt to predict the trends of stock prices based on the Dow Jones Industrial Average. The results showed that a tri-gram bag led to a 49% trend accuracy, a 1% increase when compared to the single-gram representation’s accuracy of 48%.
Background and Purpose:
Depression in older adults is a significant problem that often goes undetected and untreated in primary care. The U.S. Preventive Services Task Force recommends screening adults for depression in primary care to increase detection, so it can be adequately managed. Despite this recommendation, screening rates in primary care are low. The purpose of this project was to implement a screening intervention and examine the effect of screening on the treatment of depression in older adults.
Methods:
The screening intervention was implemented as an evidence-based project in a small primary care practice. Consenting adults ≥ 65 years of age were screened with the Patient Health Questionnaire-9 (PHQ-9). Research indicates the PHQ-9 is valid and reliable for older adults. A post-screening chart audit was conducted to collect data and analyze the outcome of screening related to treatment.
Conclusions:
A total of 38 participants were screened. Five (13.2%) participants had a positive screening, two received treatment during the follow up period. The number of participants who were treated after a positive screening was significant (p= .040).
Implications for Practice:
Screening can increase detection and treatment of depression and reduce the associated illness burden in the older adult population.
The field of veterinary medicine can be rewarding, but also very demanding. Research has shown that many practicing veterinarians struggle with mental illness, and the profession has one of the highest suicide rates in the United States. Research has also shown that many veterinary students struggle with mental illness. It is important to further research the mental health of veterinary students and how that can correlate with one's mental health as a practicing veterinarian. The purpose of this project is to summarize findings of the literature concerning the mental health of veterinary students and to present a new resource, the Wisdom Vet app, that can potentially support the well-being of veterinary students.