71 research outputs found

    Wellness and Professional Quality of Life in Counselor-in-Training Interns: Assessment of Wellness and Non-Wellness-Infused Supervision

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    Introduction: Counselors-in-training (CITs) commonly encounter issues of burnout, compassion fatigue, and/or vicarious traumatization due to the nature of their jobs in the helping profession. Wellness infused supervision may help CITs foster personal wellness and mitigate deleterious effects of helping. This investigation examined connections related to counselor-in-training wellness and professional quality of life during an internship-level supervision course across a wellness and control section. Methods: A quasi-experimental design was piloted, comparing a wellness-focused supervision internship section with a non-wellness control group supervision internship section during one academic semester. Participants included 15 clinical mental health CITs (9 experimental; 6 control), who were randomly assigned into the wellness or control internship section. Internship classes consisted of two-hour meetings across a 16 week semester, with participants working towards 300 clock hours of experience. All participants who were offered inclusion into internship accepted, and were randomly assigned into the wellness-infused or control internship sections, which took place within a large, Council for Accreditation in Counseling and Related Educational Program (CACREP) accredited program. Results: Results indicated decreased wellness scores in both internship sections from pre-to-post assessment, no differences between wellness-based internship and the control group in wellness or professional quality of life, and an increase in compassion satisfaction in the wellness-based internship group. Conclusion: Although counselors are vulnerable to compromised wellness due to the nature of their work, training CITs to work from a wellness paradigm in their personal and professional lives may facilitate well-being, and mitigate the effects of burnout and fatigue. Results from this study shed light on how CITs are viewing their personal wellness and how supervisors can utilize assessments to facilitate reflective conversations with supervisees about their wellness and quality of life

    Traumatic brain injury: integrated approaches to improve prevention, clinical care, and research

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    Quality indicators for patients with traumatic brain injury in European intensive care units

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    Background: The aim of this study is to validate a previously published consensus-based quality indicator set for the management of patients with traumatic brain injury (TBI) at intensive care units (ICUs) in Europe and to study its potential for quality measur

    Machine learning algorithms performed no better than regression models for prognostication in traumatic brain injury

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    Objective: We aimed to explore the added value of common machine learning (ML) algorithms for prediction of outcome for moderate and severe traumatic brain injury. Study Design and Setting: We performed logistic regression (LR), lasso regression, and ridge regression with key baseline predictors in the IMPACT-II database (15 studies, n = 11,022). ML algorithms included support vector machines, random forests, gradient boosting machines, and artificial neural networks and were trained using the same predictors. To assess generalizability of predictions, we performed internal, internal-external, and external validation on the recent CENTER-TBI study (patients with Glasgow Coma Scale <13, n = 1,554). Both calibration (calibration slope/intercept) and discrimination (area under the curve) was quantified. Results: In the IMPACT-II database, 3,332/11,022 (30%) died and 5,233(48%) had unfavorable outcome (Glasgow Outcome Scale less than 4). In the CENTER-TBI study, 348/1,554(29%) died and 651(54%) had unfavorable outcome. Discrimination and calibration varied widely between the studies and less so between the studied algorithms. The mean area under the curve was 0.82 for mortality and 0.77 for unfavorable outcomes in the CENTER-TBI study. Conclusion: ML algorithms may not outperform traditional regression approaches in a low-dimensional setting for outcome prediction after moderate or severe traumatic brain injury. Similar to regression-based prediction models, ML algorithms should be rigorously validated to ensure applicability to new populations

    Variation in Structure and Process of Care in Traumatic Brain Injury: Provider Profiles of European Neurotrauma Centers Participating in the CENTER-TBI Study.

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    INTRODUCTION: The strength of evidence underpinning care and treatment recommendations in traumatic brain injury (TBI) is low. Comparative effectiveness research (CER) has been proposed as a framework to provide evidence for optimal care for TBI patients. The first step in CER is to map the existing variation. The aim of current study is to quantify variation in general structural and process characteristics among centers participating in the Collaborative European NeuroTrauma Effectiveness Research in Traumatic Brain Injury (CENTER-TBI) study. METHODS: We designed a set of 11 provider profiling questionnaires with 321 questions about various aspects of TBI care, chosen based on literature and expert opinion. After pilot testing, questionnaires were disseminated to 71 centers from 20 countries participating in the CENTER-TBI study. Reliability of questionnaires was estimated by calculating a concordance rate among 5% duplicate questions. RESULTS: All 71 centers completed the questionnaires. Median concordance rate among duplicate questions was 0.85. The majority of centers were academic hospitals (n = 65, 92%), designated as a level I trauma center (n = 48, 68%) and situated in an urban location (n = 70, 99%). The availability of facilities for neuro-trauma care varied across centers; e.g. 40 (57%) had a dedicated neuro-intensive care unit (ICU), 36 (51%) had an in-hospital rehabilitation unit and the organization of the ICU was closed in 64% (n = 45) of the centers. In addition, we found wide variation in processes of care, such as the ICU admission policy and intracranial pressure monitoring policy among centers. CONCLUSION: Even among high-volume, specialized neurotrauma centers there is substantial variation in structures and processes of TBI care. This variation provides an opportunity to study effectiveness of specific aspects of TBI care and to identify best practices with CER approaches
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