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      Medical graduate views on statistical learning needs for clinical practice: a comprehensive survey

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          Abstract

          Background

          This paper seeks to contribute to a reputable evidence base for required competencies across different topics in statistics and probability (statistical topics) in preparing medical graduates for clinical practice. This is in order to inform the prioritization of statistical topics within future undergraduate medical curricula, while exploring the need for preparing tomorrow’s doctors to be producers, and not merely consumers, of statistics.

          Methods

          We conducted a comprehensive online survey from July 2013 to August 2014 for a target group of 462 medical graduates with current or prior experience of teaching undergraduate medical students of the University of Edinburgh of whom 278 (60.2%) responded. Statistical topics were ranked by proportion of respondents who identified the practice of statistics, performing statistical procedures or calculations using appropriate data, as a required competency for medical schools to provide in preparing undergraduate medical students for clinical practice. Mixed effects analyses were used to identify potential predictors for selection of the above competency and to compare the likelihood of this selection for a range of statistical topics versus critical appraisal.

          Results

          Evidence was gleaned from medical graduates’ experiences of clinical practice for the need for, not only a theoretical understanding of statistics and probability but also, the ability to practice statistics. Nature of employment and statistical topic were highly significant predictors of choice of the practice of statistics as a required competency ((F = 3.777, p < 0.0005) and (F = 45.834, p < 0.0005), respectively). The most popular topic for this competency was graphical presentation of data (84.3% of respondents) in contrast to cross-over trials for the competency understanding the theory only (70.5% of respondents). Several topics were found to be more popular than critical appraisal for competency in the practice of statistics.

          Conclusions

          The model of medical graduates as mere consumers of statistics is oversimplified. Contrary to what has been suggested elsewhere, statistical learning opportunities in undergraduate medicine should not be restricted to development of critical appraisal skills. Indeed, our findings support development of learning opportunities for undergraduate medical students as producers of statistics across a wide range of statistical topics.

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          Most cited references23

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          Medicine residents' understanding of the biostatistics and results in the medical literature.

          Physicians depend on the medical literature to keep current with clinical information. Little is known about residents' ability to understand statistical methods or how to appropriately interpret research outcomes. To evaluate residents' understanding of biostatistics and interpretation of research results. Multiprogram cross-sectional survey of internal medicine residents. Percentage of questions correct on a biostatistics/study design multiple-choice knowledge test. The survey was completed by 277 of 367 residents (75.5%) in 11 residency programs. The overall mean percentage correct on statistical knowledge and interpretation of results was 41.4% (95% confidence interval [CI], 39.7%-43.3%) vs 71.5% (95% CI, 57.5%-85.5%) for fellows and general medicine faculty with research training (P < .001). Higher scores in residents were associated with additional advanced degrees (50.0% [95% CI, 44.5%-55.5%] vs 40.1% [95% CI, 38.3%-42.0%]; P < .001); prior biostatistics training (45.2% [95% CI, 42.7%-47.8%] vs 37.9% [95% CI, 35.4%-40.3%]; P = .001); enrollment in a university-based training program (43.0% [95% CI, 41.0%-45.1%] vs 36.3% [95% CI, 32.6%-40.0%]; P = .002); and male sex (44.0% [95% CI, 41.4%-46.7%] vs 38.8% [95% CI, 36.4%-41.1%]; P = .004). On individual knowledge questions, 81.6% correctly interpreted a relative risk. Residents were less likely to know how to interpret an adjusted odds ratio from a multivariate regression analysis (37.4%) or the results of a Kaplan-Meier analysis (10.5%). Seventy-five percent indicated they did not understand all of the statistics they encountered in journal articles, but 95% felt it was important to understand these concepts to be an intelligent reader of the literature. Most residents in this study lacked the knowledge in biostatistics needed to interpret many of the results in published clinical research. Residency programs should include more effective biostatistics training in their curricula to successfully prepare residents for this important lifelong learning skill.
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            Statistical reviewing for medical journals.

            This paper reviews the difficulties associated with being a statistical reviewer for a medical journal. As background, I consider first the use of statistical reviewers by medical journals, medical journals' policies on statistical peer review, and the limited evidence of its effectiveness. The assessment of a manuscript is considered under the headings of design, methods of analysis, presentation and interpretation, with many illustrative examples of the difficulties to be overcome. I emphasize the judgemental nature of many aspects. I suggest how to present and structure the reviewer's report to the editor. Finally, I consider wider issues, including the various other ways in which statisticians can interact with medical journals.
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              Statistical methods in the journal.

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                Author and article information

                Contributors
                Margaret.MacDougall@ed.ac.uk
                Helen.Cameron@aston.ac.uk
                S.Maxwell@ed.ac.uk
                Journal
                BMC Med Educ
                BMC Med Educ
                BMC Medical Education
                BioMed Central (London )
                1472-6920
                31 December 2019
                31 December 2019
                2020
                : 20
                : 1
                Affiliations
                [1 ]ISNI 0000 0004 1936 7988, GRID grid.4305.2, Centre for Population Health Sciences, , Usher Institute, Edinburgh Medical School, University of Edinburgh, ; Teviot Place, Edinburgh, EH8 9AG UK
                [2 ]ISNI 0000 0004 0376 4727, GRID grid.7273.1, Aston Medical School, Aston University, ; Birmingham, B4 7ET UK
                [3 ]Internal Medicine Office, Medical Education Centre, University of Edinburgh, Western General Hospital, Edinburgh, EH4 2XU UK
                Author information
                http://orcid.org/0000-0002-5389-6838
                Article
                1842
                10.1186/s12909-019-1842-1
                6937818
                31892326
                0f576af7-df2b-4ef6-a111-d6a951be0a02
                © The Author(s). 2019

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

                History
                : 14 September 2018
                : 15 October 2019
                Funding
                Funded by: University of Edinburgh Principal's Teaching Award Scheme
                Award ID: not applicable
                Categories
                Research Article
                Custom metadata
                © The Author(s) 2020

                Education
                clinical practice,critical appraisal,curriculum design,statistical learning,statistics education research,undergraduate medicine

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