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An Artistic Active-Learning Approach to Teaching a Substance Use Disorder Elective Course.

ABSTRACT: Objective. To incorporate an artistic, multimedia approach to teaching within a substance use disorder (SUD) elective course to intellectually, visually, physically, and emotionally engage Doctor of Pharmacy (PharmD) students in learning and applying clinical and legal SUD topics. Methods. Faculty members created a two credit-hour SUD elective course that required students to engage in visual, linguistic, and performing art forms, including acting, screenwriting, choreography, dancing, artwork, writing movie reviews, writing book reports, writing journal reflections, create-your-own-adventure storytelling, speech writing, examination writing, policy writing, and creative thinking in an escape room gaming environment to learn about SUD and related topics. Results. Student learning and perception of the activities was evaluated using faculty-created analytic rubrics, pre- and post-intervention tests, student feedback, and student responses on standard course evaluations. Students performed well on the graded assignments. Pre- and post-intervention tests administered for the escape room activity demonstrated an increase in scores from 56.7% to 94.9%. Student feedback and course evaluations revealed student engagement with subject material and enthusiasm for creative applications, critical thinking, and collaborative aspects of the activities. Conclusion. The PharmD students consistently rated the interactive class format highly on course evaluations and reported having the perception of simultaneously learning and having fun. Pharmacy instructors are encouraged to incorporate creative projects and activities in courses to enhance student learning experiences and increase student motivation to engage with the material, their classmates, and other professionals.

SUBMITTER: Steinhardt SJ

PROVIDER: S-EPMC7223939 | biostudies-literature | 2020 Apr

REPOSITORIES: biostudies-literature

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An Artistic Active-Learning Approach to Teaching a Substance Use Disorder Elective Course.

Steinhardt Sarah J SJ Kelly William N WN Clark John E JE Hill Angela M AM

American journal of pharmaceutical education 20200401 4

<b>Objective.</b> To incorporate an artistic, multimedia approach to teaching within a substance use disorder (SUD) elective course to intellectually, visually, physically, and emotionally engage Doctor of Pharmacy (PharmD) students in learning and applying clinical and legal SUD topics. <b>Methods.</b> Faculty members created a two credit-hour SUD elective course that required students to engage in visual, linguistic, and performing art forms, including acting, screenwriting, choreography, danc ...[more]

PMID: 32431315

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Project description:BackgroundSmartphone-based apps are increasingly used to prevent relapse among those with substance use disorders (SUDs). These systems collect a wealth of data from participants, including the content of messages exchanged in peer-to-peer support forums. How individuals self-disclose and exchange social support in these forums may provide insight into their recovery course, but a manual review of a large corpus of text by human coders is inefficient.ObjectiveThe study sought to evaluate the feasibility of applying supervised machine learning (ML) to perform large-scale content analysis of an online peer-to-peer discussion forum. Machine-coded data were also used to understand how communication styles relate to writers' substance use and well-being outcomes.MethodsData were collected from a smartphone app that connects patients with SUDs to online peer support via a discussion forum. Overall, 268 adult patients with SUD diagnoses were recruited from 3 federally qualified health centers in the United States beginning in 2014. Two waves of survey data were collected to measure demographic characteristics and study outcomes: at baseline (before accessing the app) and after 6 months of using the app. Messages were downloaded from the peer-to-peer forum and subjected to manual content analysis. These data were used to train supervised ML algorithms using features extracted from the Linguistic Inquiry and Word Count (LIWC) system to automatically identify the types of expression relevant to peer-to-peer support. Regression analyses examined how each expression type was associated with recovery outcomes.ResultsOur manual content analysis identified 7 expression types relevant to the recovery process (emotional support, informational support, negative affect, change talk, insightful disclosure, gratitude, and universality disclosure). Over 6 months of app use, 86.2% (231/268) of participants posted on the app's support forum. Of these participants, 93.5% (216/231) posted at least 1 message in the content categories of interest, generating 10,503 messages. Supervised ML algorithms were trained on the hand-coded data, achieving F1-scores ranging from 0.57 to 0.85. Regression analyses revealed that a greater proportion of the messages giving emotional support to peers was related to reduced substance use. For self-disclosure, a greater proportion of the messages expressing universality was related to improved quality of life, whereas a greater proportion of the negative affect expressions was negatively related to quality of life and mood.ConclusionsThis study highlights a method of natural language processing with potential to provide real-time insights into peer-to-peer communication dynamics. First, we found that our ML approach allowed for large-scale content coding while retaining moderate-to-high levels of accuracy. Second, individuals' expression styles were associated with recovery outcomes. The expression types of emotional support, universality disclosure, and negative affect were significantly related to recovery outcomes, and attending to these dynamics may be important for appropriate intervention.

Dataset Information

An Artistic Active-Learning Approach to Teaching a Substance Use Disorder Elective Course.

Publications

An Artistic Active-Learning Approach to Teaching a Substance Use Disorder Elective Course.

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