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Record W2589185236

Theories of Expertise and Measures of Competence: Cognitive and Interactional Perspectives

2008· article· en· W2589185236 on OpenAlexaffabout
Carl H. Frederikson, Timothy Koschman, Brian MacWhinney, Colleen Seifart, Edward H. Shortliffe

Bibliographic record

VenueeScholarship (California Digital Library) · 2008
Typearticle
Languageen
FieldMedicine
TopicClinical Reasoning and Diagnostic Skills
Canadian institutionsMcGill University
Fundersnot available
KeywordsPsychologyCompetence (human resources)CognitionMedical educationSocial psychologyMedicine
DOInot available

Abstract

fetched live from OpenAlex

Theories of Expertise and Measures of Competence: Cognitive and Interactional Perspectives Participants Carl H. Frederiksen Timothy Koschmann Dept. of Educ. & Counseling Psychology McGill University Carl.Frederiksen@mcgill.ca Dept. of Medical Education Southern Illinois University tkoschmann@siumed.edu Brian MacWhinney Colleen Seifert Dept. of Psychology Carnegie-Mellon University macw@cmu.edu Dept. of Psychology University of Michigan seifert@umich.edu Discussant Edward H. Shortliffe Dept. of biomedical Informatics Arizona State University edward.shortliffe@asu.edu solving. In these tests, applicants work up a series of clinical cases portrayed, for the purposes of the exam, by “standardized patients” (SPs). Applicants interview these patients, perform physical examinations, and document their findings in simulated chart notes. They are evaluated using a combination of behavioral checklists and categorical grades assigned by trained observers (NBME, 2005). Competence, then, is defined instrumentally as that which the assessments measure—little light is shed on its underlying cognitive and interactional processes. Further, behavioral checklists have been found to imperfectly reflect changing levels of expertise (Hodges et al., 1999). Such checklists would appear, therefore, to lack validity as a metric for professional competence. Though expert evaluators may be able to recognize expert performance when they see it, there are also problems with categorical ratings. Such ratings seek to break down the components of competent performance (e.g., “Questioning skills,” “Information-sharing,” “Professional manner and rapport”), but in the process, may lose the phenomenon of interest. In the clinical exam, the cognitive skills and the interactional work of gathering pertinent information, forming an appropriate clinical picture and communicating findings (both to the patient and fellow healthcare workers) are irremediably interdependent. The papers to be presented here are all based on a corpus of graded performance samples. These samples are modeled after the practical testing protocols used in high- stakes licensure exams. Each sample includes a video recording of the subject interviewing the SP and conducting a physical exam, the subject’s associated chart note, and a recording of a detailed debriefing interview. These structured samples allow comparisons of performance across different levels of clinical training. Drawing on diverse disciplinary backgrounds, the four presentations Abstract This symposium explores the relationship between expertise and competence, two terms used to describe skilled performance. Expertise has long been a foundational topic of inquiry in Cognitive Science (Chi, Glaser, & Farr, 1988; Ericsson, Charness, Feltovich & Hoffman, 2006). Research in this area has produced a much better understanding of how expertise develops at the highest levels of performance (Ericsson et al., 2006). It might seem reasonable that theories of expertise might inform the methods used to assess competence, but it is not clear that this has necessarily been the case. In contradistinction to expertise as studied in Cognitive Science, professional competency is a regulatory matter. Minimal standards of performance are established by certifying bodies and, in professions such as law and medicine, enforced by statute. The relationship between theories of expertise and what measures of competence actually measure has received relatively little attention in the past. Given its inherent complexity and vital importance to society, much of the research on expertise has been carried out within the domain of medicine (e.g., Patel, Kaufman, & Magder, 1996; Ericsson et al., 2006). Medicine is a highly regulated field and one in which there is a vital need to establish and maintain high standards of practice. State medical boards were established in the U.S. over a century ago to provide “the public a way to enforce basic standards of competence and ethical behavior in their physicians, and physicians a way to protect the integrity of their profession” (FSMB, n.d.). All medical boards require passing scores on the components of a nationally-administered licensing exam. Though most of this exam is based on conventional written tests, one component involves practical problem-

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.038
metaresearch head score (Gemma)0.059
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.038
Threshold uncertainty score0.201

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0380.059
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0130.007
Science and technology studies0.0020.047
Scholarly communication0.0090.018
Open science0.0050.006
Research integrity0.0060.010
Insufficient payload (model declined to judge)0.0030.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.028
GPT teacher head0.276
Teacher spread0.248 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2008
Admission routes2
Has abstractyes

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