scholarly journals OSCE rater cognition – an international multi-centre qualitative study

2022 ◽  
Vol 22 (1) ◽  
Author(s):  
Sarah Hyde ◽  
Christine Fessey ◽  
Katharine Boursicot ◽  
Rhoda MacKenzie ◽  
Deirdre McGrath
Keyword(s):  
Decision Making ◽  
Data Analysis ◽  
Student Performance ◽  
Student Perception ◽  
Thinking Aloud ◽  
Objective Structured Clinical Examinations ◽  
Decision Making Processes ◽  
Method Of Assessment ◽  
Recruiting Strategy ◽  
Tacit Assumptions

Abstract Introduction This study aimed to explore the decision-making processes of raters during objective structured clinical examinations (OSCEs), in particular to explore the tacit assumptions and beliefs of raters as well as rater idiosyncrasies. Methods Thinking aloud protocol interviews were used to gather data on the thoughts of examiners during their decision-making, while watching trigger OSCE videos and rating candidates. A purposeful recruiting strategy was taken, with a view to interviewing both examiners with many years of experience (greater than six years) and those with less experience examining at final medical examination level. Results Thirty-one interviews were conducted in three centres in three different countries. Three themes were identified during data analysis, entitled ‘OSCEs are inauthentic’, ‘looking for glimpses of truth’ and ‘evolution with experience’. Conclusion Raters perceive that the shortcomings of OSCEs can have unwanted effects on student behaviour. Some examiners, more likely the more experienced group, may deviate from an organisations directions due to perceived shortcomings of the assessment. No method of assessment is without flaw, and it is important to be aware of the limitations and shortcomings of assessment methods on student performance and examiner perception. Further study of assessor and student perception of OSCE performance would be helpful.

scholarly journals OSCE Rater Cognition – An International Multi-centre Qualitative Study.

2021 ◽  
Author(s):  
Sarah Hyde ◽  
Christine Fessey ◽  
Katherine Boursicot ◽  
Rhoda McKenzie ◽  
Deirdre McGrath
Keyword(s):  
Decision Making ◽  
Data Analysis ◽  
Qualitative Study ◽  
Medical Examination ◽  
Thinking Aloud ◽  
Objective Structured Clinical Examinations ◽  
Decision Making Processes ◽  
Recruiting Strategy ◽  
Tacit Assumptions ◽  
Insight Into

Abstract Introduction This study aimed to explore the decision-making processes of raters during objective structured clinical examinations (OSCEs), in particular to explore the tacit assumptions and beliefs of raters as well as rater idiosyncrasies.MethodsThinking aloud protocol interviews were used to gather data on the thoughts of examiners during their decision-making, while watching trigger OSCE videos and rating candidates. A purposeful recruiting strategy was taken, with a view to interviewing both examiners with many years of experience and those with less experience examining at final medical examination level.ResultsThirty-one interviews were conducted in three centres in three different countries. Three themes were identified during data analysis, entitled ‘OSCEs are inauthentic’, ‘looking for glimpses of truth’ and ‘evolution with experience’. ConclusionThis study gives an insight into how raters approach OSCEs, and how the perceived shortcomings of OSCEs affect how examiners consider candidate behaviours. Some examiners, more likely the more experienced group, may deviate from an organisation’s instructions due to perceived shortcomings of the assessment.

scholarly journals State-of-the-Art Prescriptive Criteria Weight Elicitation

2012 ◽  
Vol 2012 ◽  
pp. 1-24 ◽  
Author(s):  
Mona Riabacke ◽  
Mats Danielson ◽  
Love Ekenberg
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Decision Theory ◽  
Decision Analysis ◽  
Analytical Methods ◽  
State Of The Art ◽  
Criteria Weights ◽  
Decision Making Processes ◽  
Method Of Assessment ◽  
Descriptive Theories

Comparatively few of the vast amounts of decision analytical methods suggested have been widely spread in actual practice. Some approaches have nevertheless been more successful in this respect than others. Quantitative decision making has moved from the study of decision theory founded on a single criterion towards decision support for more realistic decision-making situations with multiple, often conflicting, criteria. Furthermore, the identified gap between normative and descriptive theories seems to suggest a shift to more prescriptive approaches. However, when decision analysis applications are used to aid prescriptive decision-making processes, additional demands are put on these applications to adapt to the users and the context. In particular, the issue of weight elicitation is crucial. There are several techniques for deriving criteria weights from preference statements. This is a cognitively demanding task, subject to different biases, and the elicited values can be heavily dependent on the method of assessment. There have been a number of methods suggested for assessing criteria weights, but these methods have properties which impact their applicability in practice. This paper provides a survey of state-of-the-art weight elicitation methods in a prescriptive setting.

Conquery: an Open Source Application to analyze High Content Healthcare Data (Preprint)

2021 ◽  
Author(s):  
Fabian Kovacs ◽  
Max Thonagel ◽  
Marion Ludwig ◽  
Alexander Albrecht ◽  
Manuel Hegner ◽  
...  
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Data Analysis ◽  
Open Source ◽  
Open Source Software ◽  
Medical Records ◽  
Healthcare Sector ◽  
Study Cohort ◽  
Decision Making Processes ◽  
Analytical Approaches

BACKGROUND Big data in healthcare must be exploited to achieve a substantial increase in efficiency and competitiveness. Especially the analysis of patient-related data possesses huge potential to improve decision-making processes. However, most analytical approaches used today are highly time- and resource-consuming. OBJECTIVE The presented software solution Conquery is an open-source software tool providing advanced, but intuitive data analysis without the need for specialized statistical training. Conquery aims to simplify big data analysis for novice database users in the medical sector. METHODS Conquery is a document-oriented distributed timeseries database and analysis platform. Its main application is the analysis of per-person medical records by non-technical medical professionals. Complex analyses are realized in the Conquery frontend by dragging tree nodes into the query editor. Queries are evaluated by a bespoke distributed query-engine for medical records in a column-oriented fashion. We present a custom compression scheme to facilitate low response times that uses online calculated as well as precomputed metadata and data statistics. RESULTS Conquery allows for easy navigation through the hierarchy and enables complex study cohort construction whilst reducing the demand on time and resources. The UI of Conquery and a query output is exemplified by the construction of a relevant clinical cohort. CONCLUSIONS Conquery is an efficient and intuitive open-source software for performant and secure data analysis and aims at supporting decision-making processes in the healthcare sector.

scholarly journals Automated Decision-Making and Environmental Impact Assessments: Decisions, Data Analysis and Predictions

2021 ◽  
Vol 3 (1) ◽  
Author(s):  
Zoe Nay ◽  
Anna Huggins ◽  
Felicity Deane
Keyword(s):  
Decision Making ◽  
Data Analysis ◽  
Environmental Impact ◽  
Environmental Law ◽  
Critical Role ◽  
Data Driven ◽  
Data Driven Decision Making ◽  
Environmental Impact Assessments ◽  
Decision Making Processes ◽  
Automated Decision Making

This article critically examines the opportunities and challenges that automated decision-making (ADM) poses for environmental impact assessments (EIAs) as a crucial aspect of environmental law. It argues that while fully or partially automating discretionary EIA decisions is legally and technically problematic, there is significant potential for data-driven decision-making tools to provide superior analysis and predictions to better inform EIA processes. Discretionary decision-making is desirable for EIA decisions given the inherent complexity associated with environmental regulation and the prediction of future impacts. This article demonstrates that current ADM tools cannot adequately replicate human discretionary processes for EIAs—even if there is human oversight and review of automated outputs. Instead of fully or partially automating EIA decisions, data-driven decision-making can be more appropriately deployed to enhance data analysis and predictions to optimise EIA decision-making processes. This latter type of ADM can augment decision-making processes without displacing the critical role of human discretion in weighing the complex environmental, social and economic considerations inherent in EIA determinations.

An Investigation of BI Implementation Critical Success Factors in Iranian Context

2016 ◽  
pp. 1935-1951
Author(s):  
Ahad Zare Ravasan ◽  
Sogol Rabiee Savoji
Keyword(s):  
Decision Making ◽  
Factor Analysis ◽  
Data Analysis ◽  
Critical Success Factors ◽  
Success Factors ◽  
Successful Implementation ◽  
Domain Experts ◽  
Critical Success ◽  
Decision Making Processes ◽  
Iranian Context

Nowadays, many organizations take Business Intelligence (BI) systems to improve their decision-making processes. Although many organizations have adopted BI systems, not all of these implementations have been successful. This paper seeks to identify critical success factors (CSFs) that impact on successful implementation of BI systems in organizations. So, at first, through literature review, 26 CSFs were identified. Following that, a questionnaire was developed and then filled out by domain experts who had at least three years of experience in BI implementation projects in Iran. Robust Exploratory Factor Analysis (EFA) was run for data analysis, which finally classified 26 CSFs into four distinct groups termed as “organizational”, “human”, “project management”, and “technical”. The results of this study provide a very useful reference for scholars and managers to identify the relevant issues of BI projects in Iran.

scholarly journals Implementasi Big Data Analytical Untuk Perguruan Tinggi Menggunakan Machine Learning

2021 ◽  
Vol 2 (1) ◽  
pp. 77-88
Author(s):  
Rakhmat Purnomo ◽  
Wowon Priatna ◽  
Tri Dharma Putra
Keyword(s):  
Higher Education ◽  
Machine Learning ◽  
Decision Making ◽  
Big Data ◽  
Data Analysis ◽  
Student Performance ◽  
Big Data Analytics ◽  
Big Data Analysis ◽  
Process Data ◽  
Grouping Students

The dynamics of higher education are changing and emphasize the need to adapt quickly. Higher education is under the supervision of accreditation agencies, governments and other stakeholders to seek new ways to improve and monitor student success and other institutional policies. Many agencies fail to make efficient use of the large amounts of available data. With the use of big data analytics in higher education, it can be obtained more insight into students, academics, and the process in higher education so that it supports predictive analysis and improves decision making. The purpose of this research is to implement big data analytical to increase the decision making of the competent party. This research begins with the identification of process data based on analytical learning, academic and process in the campus environment. The data used in this study is a public dataset from UCI machine learning, from the 33 available varibales, 4 varibales are used to measure student performance. Big data analysis in this study uses spark apace as a library to operate pyspark so that python can process big data analysis. The data already in the master slave is grouped using k-mean clustering to get the best performing student group. The results of this study succeeded in grouping students into 5 clusters, cluster 1 including the best student performance and cluster 5 including the lowest student performance

scholarly journals Characteristics of decision-making process during prescribing in general practice

2006 ◽  
Vol 63 (3) ◽  
pp. 279-285 ◽  
Author(s):  
Natasa Djordjevic ◽  
Slobodan Jankovic
Keyword(s):  
Decision Making ◽  
General Practitioners ◽  
Good Clinical Practice ◽  
Educational Background ◽  
Decision Making Process ◽  
Optimal Method ◽  
Thinking Aloud ◽  
Inappropriate Treatment ◽  
Decision Making Processes ◽  
Tract Infections

Background/Aim. The process of prescribing decision-making by general practitioners requires numerous consultations in order to obtain maximal effects, minimal risks, and cost-effectiveness with the full appreciation of a patient's right to choose. The aim of our study was to describe the process of decision-making by general practitioners who decide on the treatment for an individual patient, and to relate the scope and nature of this process to the quality of the outcome of the decision. Methods. The study involved 53 general practitioners who worked in the Health Center, Kragujevac at the time of investigation (September-December 2002.). General practitioners made prescribing decisions, thinking aloud, for five patients with urinary tract infections (n = 2), or stomach complaints (n = 3). The resulting 265 transcripts were analyzed to determine the scope and nature of the decision-making processes. Differences in prescribing were related to the case or the practitioners? working experience, and to their educational background. Results. Our results showed that the more years of practice the practitioners had the less treatments they prescribed, and the less additional aspects before prescribing they considered. The doctors with less experience, in most of the cases, considered the core aspects, while those with more experience more often considered the contextual and habitual aspects. Educational background of the general practitioners, and the type of a considered disease, had an influence on the decision-making process. The most optimal method for decision-making (marked as type F) was mostly used by the practitioners with the least experience, while the those with more experience mainly made their decisions in the ways considered the least acceptable. The optimal method for decision-making process does not necessarily provide the optimal therapy, so the least acceptable decision-making might not result in an inappropriate treatment. Conclusions. The observed prescribing decisions were mostly in disagreement with the Good Clinical Practice. Our study pointed out the need for the obligatory continuation of medical education of general practitioners in decision-making process during prescribing.

Business Intelligence Implementation Critical Success Factors

2019 ◽  
pp. 112-129
Author(s):  
Ahad Zare Ravasan ◽  
Sogol Rabiee Savoji
Keyword(s):  
Decision Making ◽  
Factor Analysis ◽  
Data Analysis ◽  
Business Intelligence ◽  
Critical Success Factors ◽  
Success Factors ◽  
Successful Implementation ◽  
Domain Experts ◽  
Critical Success ◽  
Decision Making Processes

Many organizations take business intelligence (BI) systems to improve their decision-making processes. Although many organizations have adopted BI systems, not all of these implementations have been successful. This chapter seeks to identify critical success factors (CSFs) that impact on the successful implementation of BI systems in organizations. So, at first, through literature review, 26 CSFs were identified. Following that, a questionnaire was developed and then filled out by domain experts who had at least three years of experience in BI implementation projects. Robust exploratory factor analysis (EFA) was run for data analysis, which finally classified 26 CSFs into four distinct groups termed as “organizational,” “human,” “project management,” and “technical.” The results of this study provide a very useful reference for scholars and managers to identify the relevant issues of BI projects in Iran.

scholarly journals Civil Society Action and Governance in Vietnam: Selected Findings from an Empirical Survey

2010 ◽  
Vol 29 (2) ◽  
pp. 3-40 ◽  
Author(s):  
Jörg Wischermann
Keyword(s):  
Decision Making ◽  
Civil Society ◽  
Data Analysis ◽  
Social Action ◽  
Ho Chi Minh City ◽  
Interaction Data ◽  
Attitudes And Practices ◽  
Empirical Survey ◽  
Civic Organizations ◽  
Decision Making Processes

In this article, findings from 300 standardized interviews with representatives of Civic Organizations in Ho Chi Minh-City and Ha Noi are presented. Following a view of civil society as a specific mode of social action and interaction, data analysis unveils the existence of core dimensions of such action (respect, empathy/ sympathy, and the willingness to compromise and stick to agreed-upon rules), though the respective values of those dimensions vary strongly. Inseparably linked with such civil society action of whatever kind is consensus-seeking, an aversion to conflicts, and an affinity to synthesis. These attitudes and practices, dominating various Civic Organizations’ internal decision-making processes, represent elements of authoritarian political thinking in Civic Organizations’ leaders’ mindsets and courses of action. Combined, those characteristics make up civil society action “in Vietnamese colours”.

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