Sequential analysis of the phasing of the medical interview

2003 ◽  
Vol 12 (2) ◽  
pp. 124-129 ◽  
Author(s):  
Ludwien Meeuwesen
Keyword(s):  
Sequential Analysis ◽  
Time Span ◽  
Data Driven ◽  
Medical Interview ◽  
Key Concepts ◽  
Topic Shift ◽  
Medical Interviews ◽  
Verbatim Transcript ◽  
Research Questions ◽  
Reliable Analytical Method

SUMMARYAims – To study an analytical approach towards sequential analysis of the medical interview. An interview phase constitutes the analytical unit. Generally accepted phases of the medical interview are 1) medical history, 2) physical examination and 3) the conclusion segment. While descriptive and prescriptive studies claim that the sequence of the phasing is standard, it is hypothesised that in natural medical conversation the sequencing of the interview is more complex. For the doctor, the sequencing pattern is a powerful device to structure the interview and to manage the interactions within a limited time span. For the patient, especially the conclusion segment will contain more self-selection than other segments. Key concepts are turn taking, topic shift, topic flow, conversational coherence and responsiveness. Methods – The data consisted of 800 verbatim transcript pages of 85 medical interviews obtained from general practice. Interplay of theoretical notions and data-driven observations produced a reliable analytical method described in the article. The method enables to study processes of (a)symmetry in medical communication and the ways to deal with problems of misunderstanding. Results – Results confirmed 1) the asymmetrical character of the interview, 2) the complexity of the phasing, and 3) the existence of several types of interviews. Conclusions – The method can be applied for a broad range of research questions.Declaration of Interest: none.

scholarly journals Language change and language evolution: Cousins, siblings, twins?

2020 ◽  
Author(s):  
Stefan Hartmann
Keyword(s):  
Adaptive Systems ◽  
Language Change ◽  
Historical Linguistics ◽  
Language Evolution ◽  
Integrated Approach ◽  
Data Driven ◽  
Historical Time ◽  
Research Questions ◽  
The Relationship ◽  
Cross Fertilization

The relationship between “language change” and “language evolution” has recently become subject to some debate regarding the scope of both concepts. It has been claimed that while the latter used to refer to language origins in the first place, both terms can now, to a certain extent, be used synonymously. In this paper, I argue that this can partly be explained by parallel develop-ments both in historical linguistics and in the field of language evolution research that have led to a considerable amount of convergence between both fields. Both have adopted usage-based approaches and data-driven methods, which entails similar research questions and similar perspectives on the phenomena under investigation. This has ramifications for current models and theories of language change (or evolution). Two approaches in particular – the concept of com-plex adaptive systems and construction grammar – have been combined in integrated approaches that seek to explain both language emergence and language change over historical time. I discuss the potential and limitations of this integrated approach, and I argue that there is still some unex-plored potential for cross-fertilization.

scholarly journals Assessing the Relevance of Undergraduate Auditing Education: A Scoping Review

2019 ◽  
Vol 4 (1) ◽  
pp. 11
Author(s):  
Diana Tien Irafahmi
Keyword(s):  
Scoping Review ◽  
Time Span ◽  
Study Selection ◽  
Research Questions

The objective of this scoping review is to map the existing literature that asses the relevance of undergraduate auditing education to meet contemporary audit practice. The scoping review followed the protocol outlined by Arksey and O'Malley (2005) that has five main stages: (1) identifying the research questions; (2) identifying relevant studies; (3) study selection; (4) charting the data, and (5) collating, summarizing and reporting the results. The studies reviewed were selected from three electronic databases: Proquest, Taylor & Francis, and Science Direct within 25 years time span (1995-2019). The results of the scoping review indicated that the majority of the studies recognize a shift both in terms of content and pedagogy to keep its relevance to professional demand. The shift needs to more emphasize on content as a gap still exists between auditing educators and practitioners.

scholarly journals An In-Class Demonstration of Bayesian Inference

2019 ◽  
Author(s):  
Johnny van Doorn ◽  
Dora Matzke ◽  
Eric-Jan Wagenmakers
Keyword(s):  
Bayesian Inference ◽  
Posterior Distribution ◽  
Prior Distribution ◽  
Sequential Analysis ◽  
Bayes Factor ◽  
Classroom Setting ◽  
Key Concepts

Sir Ronald Fisher's venerable experiment "The Lady Tasting Tea'' is revisited from a Bayesian perspective. We demonstrate how a similar tasting experiment, conducted in a classroom setting, can familiarize students with several key concepts of Bayesian inference, such as the prior distribution, the posterior distribution, the Bayes factor, and sequential analysis.

Determination of Cd and Cr in tannin samples by high-resolution continuum source graphite furnace atomic absorption spectrometry and direct solid sample sequential analysis (HR-CS SS-GF AAS)

2015 ◽  
Vol 7 (9) ◽  
pp. 3735-3741 ◽  
Author(s):  
Ariane V. Zmozinski ◽  
Tatiane Pretto ◽  
Aline R. Borges ◽  
Maria Goreti R. Vale
Keyword(s):  
Sequential Analysis ◽  
Graphite Furnace ◽  
Absorption Spectrometry ◽  
Continuum Source ◽  
Sequential Determination ◽  
Graphite Furnace Atomic Absorption ◽  
Gf Aas ◽  
Reliable Analytical Method ◽  
Direct Solid

A fast and reliable analytical method for the sequential determination of Cd and Cr in tannin samples by HR-CS SS-GF AAS from the same sample aliquot is proposed.

Patient cues and medical interviewing in general practice: examples of the application of sequential analysis

2003 ◽  
Vol 12 (2) ◽  
pp. 115-123 ◽  
Author(s):  
Christa Zimmermann ◽  
Lidia Del Piccolo ◽  
Maria Angela Mazzi
Keyword(s):  
Primary Care ◽  
Pattern Recognition ◽  
General Practice ◽  
Communication Skills ◽  
Sequential Analysis ◽  
Formal Training ◽  
Medical Interview ◽  
Medical Interviewing ◽  
Lag Sequential Analysis ◽  
Pattern Recognition Analysis

SUMMARYAims – To illustrate how sequence analysis may be applied to the medical interview to: 1. explore how physicians without formal training in communication skills elicit and respond to patient cues and expression of expectations and opinions; and 2. test the hypothesis that physicians' closed ended questions determine the use of subsequent closed ended questions. Methods – 238 consultations in primary care, coded with the Verona Medical Interview Classification System, were analysed. Lag 1 analysis was applied to study which physician behaviour precedes and follows patient cues. Pattern recognition analysis for five lag sequences was performed to test the occurrence of predefined specific code chains, where a closed and an open ended question were followed either by two closed-ended questions or by two patient facilitating interventions Results – Patients' cue offers were most likely after facilitative interventions, but not after open-ended questions; physicians were most likely to respond to these expressions with facilitation. Physicians' tendency to use closed ended questions increased after previous closed questions and decreased after an open-ended question. Conclusions – Lag sequential analysis and pattern recognition analysis are useful methods to study exploratory and theory driven hypotheses and allow an initial approach to validate the supposed appropriateness of specific physician interventions.Declaration of Interest: none.

scholarly journals How Do Simulated Interviews Increase the Self-efficacy of Medical Students? - Quasi-experimental Research-

2021 ◽  
Author(s):  
Shuntaro Aoki ◽  
Yayoi Shikama ◽  
Kiyotaka Yasui ◽  
Yoko Moroi ◽  
Nobuo Sakamoto ◽  
...  
Keyword(s):  
Medical Students ◽  
Self Efficacy ◽  
Simulated Patient ◽  
The Self ◽  
Medical Interview ◽  
Feedback Session ◽  
Medical Interviewing ◽  
Before And After ◽  
Medical Interviews ◽  
Quasi Experimental

Abstract Background Self-efficacy is crucial in improving medical student communication skills. This study aims to clarify whether the self-efficacy of medical students conducting medical interviews increased after simulated interviews or after feedback discussions. Methods A total of 162 medical students (109 men, 53 women) in their fourth or fifth year at a university in Japan participated in this study. The degree of self-efficacy in medical interviewing was measured before and after a medical interview with a simulated patient, and after the subsequent feedback session. Results ANOVA analysis revealed that self-efficacy for medical interviews was higher after both the interview and the feedback session than before the interview. Self-efficacy was highest after the feedback session among all three time points. Conclusions Feedback following a simulated interview with a simulated patient is important to improve the self-efficacy of medical students learning medical interviewing skills.

scholarly journals Contextualizing SEGUE: Evaluating Residents' Communication Skills Within the Framework of a Structured Medical Interview

2010 ◽  
Vol 2 (1) ◽  
pp. 102-107 ◽  
Author(s):  
Jared Lyon Skillings ◽  
John H. Porcerelli ◽  
Tsveti Markova
Keyword(s):  
Communication Skills ◽  
Rating Scale ◽  
Substantial Improvement ◽  
Resident Training ◽  
New Method ◽  
Medical Interview ◽  
Case Examples ◽  
New Instrument ◽  
Medical Interviews ◽  
Global Ratings

Abstract Background The SEGUE (Set the stage, Elicit information, Give information, Understand the patient's perspective, and End the encounter) Framework is a checklist-style rating scale to facilitate the teaching and assessment of communication skills in medical learners. It has been used for over 15 years, and it is recommended in the Accreditation Council for Graduate Medical Education toolbox of assessment methods for resident training. When it was developed, its ability to provide objective scoring was a substantial improvement over global ratings. Methods In this article we describe the strengths and weaknesses of the SEGUE Framework. We highlight one residency program's experience with using the SEGUE Framework to evaluate residents' communication skills. Specifically, we cite previous studies and describe our own analysis of resident interviewing performance that demonstrates how the SEGUE Framework did not distinguish between different levels of interviewing skill level in our sample. Results Two case examples illustrate how the SEGUE Framework is not an ideal instrument to measure either the quality or the process of medical interviews. Conclusion Therefore, we propose a new method of contextualized assessment that builds on the SEGUE Framework. Our system evaluates discrete interviewing behaviors within the context of an ambulatory medical interview. We describe our interview structure, as well as a new instrument (the Wy-Mii, pronounced “why me”), to assess both communication and interpersonal skills. We expect that our new method of contextualized assessment will better differentiate between beginning and advanced levels of medical interviewing skills for residents.

Data Mining Storm Attributes from Spatial Grids

2009 ◽  
Vol 26 (11) ◽  
pp. 2353-2365 ◽  
Author(s):  
Valliappa Lakshmanan ◽  
Travis Smith
Keyword(s):  
Data Driven ◽  
Objective Data ◽  
Temporal Features ◽  
Identification Technique ◽  
Self Similar ◽  
Research Questions ◽  
Grid Points ◽  
Spatial Grids ◽  
Traditional Understanding ◽  
Temporal Extent

Abstract A technique to identify storms and capture scalar features within the geographic and temporal extent of the identified storms is described. The identification technique relies on clustering grid points in an observation field to find self-similar and spatially coherent clusters that meet the traditional understanding of what storms are. From these storms, geometric, spatial, and temporal features can be extracted. These scalar features can then be data mined to answer many types of research questions in an objective, data-driven manner. This is illustrated by using the technique to answer questions of forecaster skill and lightning predictability.

JD.com: Transaction-Level Data for the 2020 MSOM Data Driven Research Challenge

2020 ◽  
Author(s):  
Max Shen ◽  
Christopher S. Tang ◽  
Di Wu ◽  
Rong Yuan ◽  
Wei Zhou
Keyword(s):  
Econometric Models ◽  
Data Driven ◽  
Level Data ◽  
Research Questions ◽  
Transaction Level ◽  
Research Challenge ◽  
Transactional Data

To support the 2020 MSOM Data Driven Research Challenge, JD.com, China’s largest retailer, offers transaction-level data to MSOM members for conducting data-driven research. This article describes the transactional data associated with over 2.5 million customers (457,298 made purchases) and 31,868 stock keeping units (SKUs) over the month of March in 2018. We also present potential research questions suggested by JD.com. Researchers are welcome to develop econometric models or data-driven models using this database to address some of the suggested questions or examine their own research questions.

scholarly journals The limits and potentials of deep learning for robotics

2018 ◽  
Vol 37 (4-5) ◽  
pp. 405-420 ◽  
Author(s):  
Niko Sünderhauf ◽  
Oliver Brock ◽  
Walter Scheirer ◽  
Raia Hadsell ◽  
Dieter Fox ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Learning Communities ◽  
Data Driven ◽  
Important Research ◽  
Research Directions ◽  
Model Driven ◽  
Research Questions ◽  
Robotic Learning ◽  
Specific Learning

The application of deep learning in robotics leads to very specific problems and research questions that are typically not addressed by the computer vision and machine learning communities. In this paper we discuss a number of robotics-specific learning, reasoning, and embodiment challenges for deep learning. We explain the need for better evaluation metrics, highlight the importance and unique challenges for deep robotic learning in simulation, and explore the spectrum between purely data-driven and model-driven approaches. We hope this paper provides a motivating overview of important research directions to overcome the current limitations, and helps to fulfill the promising potentials of deep learning in robotics.

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