Dataset Information

Learning to prescribe intravenous fluids: A scoping review.

ABSTRACT: Prescribing intravenous (IV) fluid therapy is a core skill expected of qualified doctors at the point of graduation, but medical graduates often feel ill-equipped to perform this task. This lack of preparedness contributes to treatment-related patient harm. This scoping review maps the current state of published evidence about how junior doctors prescribe IV fluid therapy and learn how to do it.We searched five electronic databases and grey literature from 1994 until June 2016 for articles describing any aspect of IV fluid prescribing practice or its education. A total of 63 articles were selected for analysis. Using the WHO Guide to Good Prescribing to categorize the extracted findings, our review focuses on prescribing IV fluids in adult generalist settings.Most articles studied IV fluid prescribing from the perspective of the doctor. Junior clinicians struggled to conceptualize IV fluid prescribing as a 'whole task' in authentic work settings and lacked support. Educational interventions to improve IV fluid prescribing often focused on enhancing prescriber knowledge about fluid and electrolyte balance rather than execution of the prescribing task.Our understanding of IV fluid prescribing as a holistic integrated skill is patchy, as is its performance. Current IV fluid prescribing education appears insufficient to foster safe and effective practice. For education to achieve the ultimate goal of safer prescribing in workplaces, we need a clearer understanding of how healthcare professionals prescribe IV fluids in real world practice.

SUBMITTER: McCrory RFR

PROVIDER: S-EPMC5732109 | biostudies-literature | 2017 Dec

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Learning to prescribe intravenous fluids: A scoping review.

McCrory Richard F R RFR Gormley Gerard Joseph GJ Maxwell Alexander Peter AP Dornan Tim T

Perspectives on medical education 20171201 6

<h4>Introduction</h4>Prescribing intravenous (IV) fluid therapy is a core skill expected of qualified doctors at the point of graduation, but medical graduates often feel ill-equipped to perform this task. This lack of preparedness contributes to treatment-related patient harm. This scoping review maps the current state of published evidence about how junior doctors prescribe IV fluid therapy and learn how to do it.<h4>Methods</h4>We searched five electronic databases and grey literature from 19 ...[more]

PMID: 29119469

Similar Datasets

Project description:BackgroundUnderstanding students' learning styles, and modifying teaching styles and material accordingly, is an essential to delivering quality education. Knowing more about the learning styles of physiotherapy learners will assist educators' planning and delivering of learning activities. The purpose of this scoping review was to explore what is published about physiotherapy learning styles.MethodsAn adapted Arksey and O'Malley framework was applied to undertake this systematic scoping review. Nine electronic databases (CINAHL, BIOMED CENTRAL, Cochrane, Web of Science, PROQUEST, PubMed, OTseeker, Scopus, ERIC) were searched using the keywords: 'learning styles' and 'physiotherapy'. English-language, primary research articles that investigated physiotherapy learners' learning styles were sought.ResultsOf 396 potentially-relevant articles, 15 were included in this review. The studies mostly reflected undergraduate students (910 undergraduates, 361 postgraduates, 23 professionals), in developed countries. Nine articles used the Kolb's experiential learning theory (ELT); one study applied Honey and Mumford's approach; two studies used the Gregorc model of cognition and three studies did not specify an underlying theory. Outcome measures included different versions of Kolb's Learning Style Inventory, the visual-aural-read/write-kinesthetic questionnaire, Gregorc style delineator, Felder Silverman's Index of Learning Survey, and Honey and Mumford's Learning Style Questionnaire. The preferred physiotherapy learning styles, according to the ELT, seem to be Converger (learns "hands-on" and applying previously attained knowledge) and Assimilator (gathers and organises information to make the most sense).ConclusionsBoth physiotherapy learners and physiotherapists have specific learning styles of active participation, underpinned with practical examples of theoretical concepts. More research is needed in developing countries, and on postgraduate and professional physiotherapy learners' learning styles. Also, further research should focus on defining and describing physiotherapy learning styles in a way to be used as an industry standard; and developing valid and reliable learning style outcome measures applicable across physiotherapy learners and settings.

Project description:OBJECTIVE:To determine how machine learning has been applied to prediction applications in population health contexts. Specifically, to describe which outcomes have been studied, the data sources most widely used and whether reporting of machine learning predictive models aligns with established reporting guidelines. DESIGN:A scoping review. DATA SOURCES:MEDLINE, EMBASE, CINAHL, ProQuest, Scopus, Web of Science, Cochrane Library, INSPEC and ACM Digital Library were searched on 18 July 2018. ELIGIBILITY CRITERIA:We included English articles published between 1980 and 2018 that used machine learning to predict population-health-related outcomes. We excluded studies that only used logistic regression or were restricted to a clinical context. DATA EXTRACTION AND SYNTHESIS:We summarised findings extracted from published reports, which included general study characteristics, aspects of model development, reporting of results and model discussion items. RESULTS:Of 22 618 articles found by our search, 231 were included in the review. The USA (n=71, 30.74%) and China (n=40, 17.32%) produced the most studies. Cardiovascular disease (n=22, 9.52%) was the most studied outcome. The median number of observations was 5414 (IQR=16?543.5) and the median number of features was 17 (IQR=31). Health records (n=126, 54.5%) and investigator-generated data (n=86, 37.2%) were the most common data sources. Many studies did not incorporate recommended guidelines on machine learning and predictive modelling. Predictive discrimination was commonly assessed using area under the receiver operator curve (n=98, 42.42%) and calibration was rarely assessed (n=22, 9.52%). CONCLUSIONS:Machine learning applications in population health have concentrated on regions and diseases well represented in traditional data sources, infrequently using big data. Important aspects of model development were under-reported. Greater use of big data and reporting guidelines for predictive modelling could improve machine learning applications in population health. REGISTRATION NUMBER:Registered on the Open Science Framework on 17 July 2018 (available at https://osf.io/rnqe6/).

Dataset Information

Learning to prescribe intravenous fluids: A scoping review.

Publications

Learning to prescribe intravenous fluids: A scoping review.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets