The process of skills training: A content analysis of evidence-based addiction therapies.
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ABSTRACT: Overview
This work builds on previous efforts to delineate cross-cutting factors of evidence-based therapies. In this report, we target a single therapeutic factor-skills training for addictive behavior change-and we operationalize this factor in a manner that will aid clinical training and quality control. Specifically, we identify principles, which we defined as broader understandings on the part of the therapist that must be kept in mind when implementing a specific therapeutic practice. We define a practice as discrete action step or specific type of intervention that the therapist uses when addressing skills training content with clients.Method
We conducted a literature review and qualitative content analysis of 30 source documents (i.e., therapy manuals, literature reviews, and government issued practice guidelines) and videos (i.e., therapy demonstration videos). We performed analysis of source materials in NVIVO.Results
We identified 10 principles and 30 therapeutic practices. Together, the principles suggest that skills training in evidence-based addiction therapies can be characterized as a client-centered approach to teaching and behavioral practice. The identified practices fell into four function themes: 1) client-centered goal-setting, 2) building client self-efficacy, 3) engaging in teaching, and 4) engaging in practice.Conclusions
When the identified principles and practices are combined, they can inform a fidelity-based approach to behavioral skills training that is applicable to a wide range of alcohol or other drug (AOD) content topics, therapeutic modalities, and implementation settings. We discuss future implications regarding standardized training and fidelity assessment.
SUBMITTER: Magill M
PROVIDER: S-EPMC7399208 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundFood addiction has a long history; however, there has been a substantial increase in published literature and public media focus in the past decade. Food addiction has previously demonstrated an overlap with overweight and obesity, a risk for cardiovascular disease. This increased focus has led to the establishment of numerous support options for addictive eating behaviors, yet evidence-based support options are lacking.ObjectiveThis study aimed to evaluate the availability and content of support options, accessible online, for food addiction.MethodsA standardized Web search was conducted using 4 search engines to identify current support availability for food addiction. Through use of a comprehensive data extraction sheet, 2 reviewers independently extracted data related to the program or intervention characteristics, and support fidelity including fundamentals, support modality, social support offered, program or intervention origins, member numbers, and program or intervention evaluation.ResultsOf the 800 records retrieved, 13 (1.6%, 13/800) websites met the inclusion criteria. All 13 websites reported originating in the United States, and 1 website reported member numbers. The use of credentialed health professionals was reported by only 3 websites, and 5 websites charged a fee-for-service. The use of the 12 steps or traditions was evident in 11 websites, and 9 websites described the use of food plans. In total, 6 websites stated obligatory peer support, and 11 websites featured spirituality as a main theme of delivery. Moreover, 12 websites described phone meetings as the main program delivery modality, with 7 websites stating face-to-face delivery and 4 opting for online meetings. Newsletters (n=5), closed social media groups (n=5), and retreat programs (n=5) were the most popular forms of social support.ConclusionsThis is the first review to analyze online support options for food addiction. Very few online support options include health professionals, and a strengthening argument is forming for an increase in support options for food addiction. This review forms part of this argument by showing a lack of evidence-based options. By reviewing current support availability, it can provide a guide toward the future development of evidence-based support for food addiction.
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Project description:BackgroundOur aim was to build a skill assessment system, providing objective feedback to trainees based on the motion metrics of laparoscopic surgical instruments.MethodsParticipants performed tissue dissection around the aorta (tissue dissection task) and renal parenchymal closure (parenchymal-suturing task), using swine organs in a box trainer under a motion capture (Mocap) system. Two experts assessed the recorded movies, according to the formula of global operative assessment of laparoscopic skills (GOALS: score range, 5-25), and the mean scores were utilized as objective variables in the regression analyses. The correlations between mean GOALS scores and Mocap metrics were evaluated, and potential Mocap metrics with a Spearman's rank correlation coefficient value exceeding 0.4 were selected for each GOALS item estimation. Four regression algorithms, support vector regression (SVR), principal component analysis (PCA)-SVR, ridge regression, and partial least squares regression, were utilized for automatic GOALS estimation. Model validation was conducted by nested and repeated k-fold cross validation, and the mean absolute error (MAE) was calculated to evaluate the accuracy of each regression model.ResultsForty-five urologic, 9 gastroenterological, and 3 gynecologic surgeons, 4 junior residents, and 9 medical students participated in the training. In both tasks, a positive correlation was observed between the speed-related parameters (e.g., velocity, velocity range, acceleration, jerk) and mean GOALS scores, with a negative correlation between the efficiency-related parameters (e.g., task time, path length, number of opening/closing operations) and mean GOALS scores. Among the 4 algorithms, SVR showed the highest accuracy in the tissue dissection task ([Formula: see text]), and PCA-SVR in the parenchymal-suturing task ([Formula: see text]), based on 100 iterations of the validation process of automatic GOALS estimation.ConclusionWe developed a machine learning-based GOALS scoring system in wet lab training, with an error of approximately 1-2 points for the total score, and motion metrics that were explainable to trainees. Our future challenges are the further improvement of onsite GOALS feedback, exploring the educational benefit of our model and building an efficient training program.
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