Gamification as an educational tool to address antimicrobial resistance: a systematic review

Nowbuth, Avis A; Asombang, Akwi W; Alaboud, Khuder; Souque, Célia; Dahu, Butros M; Pather, Kyrtania; Mwanza, Monica M; Lotfi, Sameen; Parmar, Vikram S

doi:10.1093/jacamr/dlad130

Cited by 3 publications

(1 citation statement)

References 47 publications

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“…A bacterium is considered resistant to a specific antimicrobial agent when a recommended dose neither kills [ 9 ] nor effectively inhibits its multiplication [ 10 , 11 ].…”

Section: Resultsmentioning

confidence: 99%

Post-Coronavirus Disease 2019 Pandemic Antimicrobial Resistance

Boccabella,

Palma,

Abenavoli

et al. 2024

Antibiotics

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Background and aim: Antimicrobial resistance (AMR) is a chronic issue of our Westernized society, mainly because of the uncontrolled and improper use of antimicrobials. The coronavirus disease 2019 (COVID-19) pandemic has triggered and expanded AMR diffusion all over the world, and its clinical and therapeutic features have changed. Thus, we aimed to review evidence from the literature on the definition and causative agents of AMR in the frame of the COVID-19 post-pandemic era. Methods: We conducted a search on PubMed and Medline for original articles, reviews, meta-analyses, and case series using the following keywords, their acronyms, and their associations: antibiotics, antimicrobial resistance, severe acute respiratory syndrome coronavirus 2 (SARS-CoV2), COVID-19 pandemic, personal protective equipment. Results: AMR had a significant rise in incidence both in in-hospital and outpatient populations (ranging from 5 up to 50%) worldwide, but with a variegated profile according to the germ and microorganism considered. Not only bacteria but also fungi have developed more frequent and diffuse AMR. These findings are explained by the increased use and misuse of antibiotics and preventive measures during the first waves of the SARS-CoV2 pandemic, especially in hospitalized patients. Subsequently, the reduction in and end of the lockdown and the use of personal protective equipment have allowed for the indiscriminate circulation of resistant microorganisms from low-income countries to the rest of the world with the emergence of new multi- and polyresistant organisms. However, there is not a clear association between COVID-19 and AMR changes in the post-pandemic period. Conclusions: AMR in some microorganisms has significantly increased and changed its characteristics during and after the end of the pandemic phase of COVID-19. An integrated supranational monitoring approach to this challenge is warranted in the years to come. In detail, a rational, personalized, and regulated use of antibiotics and antimicrobials is needed.

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“…A bacterium is considered resistant to a specific antimicrobial agent when a recommended dose neither kills [ 9 ] nor effectively inhibits its multiplication [ 10 , 11 ].…”

Section: Resultsmentioning

confidence: 99%

Post-Coronavirus Disease 2019 Pandemic Antimicrobial Resistance

Boccabella,

Palma,

Abenavoli

et al. 2024

Antibiotics

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A practice-based approach to teaching antimicrobial therapy using artificial intelligence and gamified learning

Driesnack,

Rücker,

Dietze-Jergus

et al. 2024

JAC-Antimicrobial Resistance

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Objectives Scalable teaching through apps and artificial intelligence (AI) is of rising interest in academic practice. We focused on how medical students could benefit from this trend in learning antibiotic stewardship (ABS). Our study evaluated the impact of gamified learning on factual knowledge and uncertainty in antibiotic prescription. We also assessed an opportunity for AI-empowered evaluation of freeform answers. Methods We offered four short courses focusing on ABS, with 46 participating medical students who self-selected themselves into the elective course. Course size was limited by the faculty. At the start of the course, students were given a questionnaire about microbiology, infectious diseases, pharmacy and qualitative questions regarding their proficiency of selecting antibiotics for therapy. Students were followed up with the same questionnaire for up to 12 months. We selected popular game mechanics with commonly known rules for teaching and an AI for evaluating freeform questions. Results The number of correctly answered questions improved significantly for three topics asked in the introductory examination, as did the self-assessed safety of prescribing antibiotics. The AI-based review of freeform answers was found to be capable of revealing students’ learning gaps and identifying topics in which students needed further teaching. Conclusions We showed how an interdisciplinary short course on ABS featuring gamified learning and AI could substantially improve learning. Even though large language models are a relatively new technology that sometimes fails to produce the anticipated results, they are a possible first step in scaling a tutor-based teaching approach in ABS.

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Application of Machine Learning and Deep Neural Visual Features for Predicting Adult Obesity Prevalence in Missouri

Dahu,

Martinez-Villar,

Toubal

et al. 2024

IJERPH

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This research study investigates and predicts the obesity prevalence in Missouri, utilizing deep neural visual features extracted from medium-resolution satellite imagery (Sentinel-2). By applying a deep convolutional neural network (DCNN), the study aims to predict the obesity rate of census tracts based on visual features in the satellite imagery that covers each tract. The study utilizes Sentinel-2 satellite images, processed using the ResNet-50 DCNN, to extract deep neural visual features (DNVF). Obesity prevalence data, sourced from the CDC’s 2022 estimates, is analyzed at the census tract level. The datasets were integrated to apply a machine learning model to predict the obesity rates in 1052 different census tracts in Missouri. The analysis reveals significant associations between DNVF and obesity prevalence. The predictive models show moderate success in estimating and predicting obesity rates in various census tracts within Missouri. The study emphasizes the potential of using satellite imagery and advanced machine learning in public health research. It points to environmental factors as significant determinants of obesity, suggesting the need for targeted health interventions. Employing DNVF to explore and predict obesity rates offers valuable insights for public health strategies and calls for expanded research in diverse geographical contexts.

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Gamification as an educational tool to address antimicrobial resistance: a systematic review

Cited by 3 publications

References 47 publications

Post-Coronavirus Disease 2019 Pandemic Antimicrobial Resistance

Post-Coronavirus Disease 2019 Pandemic Antimicrobial Resistance

A practice-based approach to teaching antimicrobial therapy using artificial intelligence and gamified learning

Application of Machine Learning and Deep Neural Visual Features for Predicting Adult Obesity Prevalence in Missouri

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