“Sterling Examples of Computer Simulations & OSCEs (Objective Structured Clinical Examinations)” Carol O’Byrne Jeffrey Kelley Richard Hawkins Sydney Smee Presented at the 2005 CLEAR Annual Conference September Phoenix, Arizona

Presented at the 2005 CLEAR Annual Conference September Phoenix, Arizona Session Format  Introduction: 25+ years of Performance Assessment  Presentations Richard Hawkins, National Board of Medical Examiners overview of a new national OSCE Jeff Kelley, Applied Measurement Professionals development of a new real estate computer simulation Sydney Smee, Medical Council of Canada setting performance standards for a national OSCE Carol O’Byrne, Pharmacy Examining Board of Canada scoring performance and reporting results to candidates for a national OSCE  Q&A

Presented at the 2005 CLEAR Annual Conference September Phoenix, Arizona Session Goals Consider the role and importance of simulations in a professional qualifying examination context Explore development and large scale implementation challenges Observe how practice analysis results are integrated with the implementation of a simulation examination Consider options for scoring, standard setting and reporting to candidates Consider means to enhance fairness and consistency Identify issues for further research and development

Presented at the 2005 CLEAR Annual Conference September Phoenix, Arizona Defining ‘Performance Assessment’...the assessment of the integration of two or more learned capabilities …i.e., observing how a candidate performs a physical examination (technical skill) is not performance-based assessment unless findings from the examination are used for purposes such as generating a problem list or deciding on a management strategy (cognitive skills) (Mavis et al, 1996)

Presented at the 2005 CLEAR Annual Conference September Phoenix, Arizona Why Test Performance? To determine if individuals can ‘do the job’ integrating knowledge, skills and abilities to solve complex client and practice problems meeting job-related performance standards To complement MC tests measuring important skills, abilities and attitudes which are difficult to impossible to measure through MCQs alone reducing impact of factors, such as cuing, logical elimination & luck or chance that may confound MC test results

Presented at the 2005 CLEAR Annual Conference September Phoenix, Arizona A 25+ Year Spectrum of Performance Assessment ‘Pot luck’ direct observation apprenticeship, internship, residency programs Oral and pencil-paper, short- or long- answer questions Hands-on job samples military, veterinary medicine, mechanics, plumbers Portfolios advanced practice, continuing competency

Presented at the 2005 CLEAR Annual Conference September Phoenix, Arizona Simulations Electronic: architecture, aviation, respiratory care, real estate, nursing, medicine, etc. Objective Structured Clinical Examination (OSCE): medicine, pharmacy, physiotherapy, chiropractic medicine, massage therapy and including the legal profession, psychology, and others

Presented at the 2005 CLEAR Annual Conference September Phoenix, Arizona Simulation Promotes Evidence- based Testing… 1900 Wright brothers flight test Flew manned kite 200 feet in 20 seconds 1903 Wright brothers flight test Flew manned glider 852 feet in 59 seconds, 8 to 12 feet in the air! In between they built a wind tunnel to simulate flight under various wind direction and speed conditions, varying wing shapes, curvatures and aspect ratios to test critical calculations and glider lift to assess performance in important and potentially risky situations without incurring actual risk

Presented at the 2005 CLEAR Annual Conference September Phoenix, Arizona Attitudes, Skills and Abilities tested through Simulations Attitudes: client centeredness alignment with ethical and professional values and principles Skills: interpersonal and communications clinical, e.g. patient / client care technical Abilities to: analyze and manage risk, exercise sound judgment gather, synthesize and critically evaluate information act systematically and adaptively, independently and within teams defend, evaluate and/or modify decisions/actions taken monitor outcomes and follow up appropriately

Presented at the 2005 CLEAR Annual Conference September Phoenix, Arizona Performance / Simulation Assessment Design Elements  Domain(s) of interest & sampling plan  Realistic context – practice-related problems and scenarios  Clear, measurable performance standards  Stimuli and materials to elicit performance  Administrative, observation and data collection procedures  Assessment criteria that reflect standards  Scoring rules that incorporate assessment criteria  Cut scores/performance profiles reflecting standards  Quality assurance processes  Meaningful data summaries for reports to candidates and others

Presented at the 2005 CLEAR Annual Conference September Phoenix, Arizona Score Variability and Reliability  Multiple factors interact and influence scores differential and compensatory aptitudes of candidates (knowledge, skills, abilities, attitudes) format, difficulty and number of tasks or problems consistency of presentation between candidates, locations, occasions complex scoring schemes (checklists, ratings, weights) rater consistency between candidates, locations, occasions  Designs are often complex (not crossed) examinees ‘nested’ within raters - within tasks – within sites, etc.  Problems and tasks are multidimensional

Presented at the 2005 CLEAR Annual Conference September Phoenix, Arizona Analyzing Performance Assessment Data Generalizability (G) studies – to identify and quantify sources of variation Dependability (D) studies – to determine how to minimize the impact of error and optimize score reliability Heirarchical linear modeling (HLM) studies – to quantify and rank sources of variation in complex nested designs

Presented at the 2005 CLEAR Annual Conference September Phoenix, Arizona Standard Setting What score or combination of scores (profile) indicates that the candidate is able to meet expected standards of performance, thereby fulfilling the purpose(s) of the test? What methods can be used to determine this standard?

Presented at the 2005 CLEAR Annual Conference September Phoenix, Arizona Reporting Results to Candidates Pass-fail (classification) May also include: Individual test score and passing score Sub-scores by objective(s) and/or other criteria Quantile standing among all candidates – or among those who failed Group data - score ranges, means, standard deviations) Reliability and validity evidence (narrative, indices and/or error estimates and their interpretation) Other

Presented at the 2005 CLEAR Annual Conference September Phoenix, Arizona Some Validity Questions Exactly what are we measuring with each simulation? Does it support the test purpose? To what extent is each candidate is presented with the same or equivalent challenges? How consistently are candidates’ performances assessed no matter who or where the assessor is? Are the outcomes similar to findings in other comparable evaluations? How ought we to inform & report to candidates about performance standards / expectations & their own performance strengths/gaps?

Presented at the 2005 CLEAR Annual Conference September Phoenix, Arizona Evaluation Goals Validity evidence Strong links from job analysis to interpretation of test results Simulation performance relates to performance in training and other tests of similar capabilities Reliable, generalizable scores and ratings Dependable pass-fail (classification) standards Feasibility and sustainability For program scale (number of candidates, sites, etc.) Economic, human, physical, technological resources Continuous evaluation and enhancement plan

Presented at the 2005 CLEAR Annual Conference September Phoenix, Arizona Wisdom Bytes Simulations should be as true to life as possible (fidelity) Simulations should test capabilities that cannot be tested in more efficient formats Simulation tests should focus on integration of multiple capabilities rather than on a single basic capability The nature of each simulation/task should be clear but candidates should be ‘cued’ only as far as is realistic in practice Increasing the number of tasks contributes more to the generalizability and dependability of results than increasing the number of raters

Presented at the 2005 CLEAR Annual Conference September Phoenix, Arizona Expect the Unpredictable… Candidate diversity Language Training Test format familiarity Accommodation requests Logistical challenges Technological glitches Personnel fatigue and/or attention gaps Site variations Security cracks Test content exposure in prep programs, study materials – in various languages