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Record W2995120354 · doi:10.1111/jep.13328

Competency‐based education calls for programmatic assessment: But what does this look like in practice?

2019· article· en· W2995120354 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueJournal of Evaluation in Clinical Practice · 2019
Typearticle
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsRoyal College of Physicians and Surgeons of CanadaQueen's University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsSummative assessmentFormative assessmentOperationalizationCompetence (human resources)Medical educationDocumentationPsychologyQualitative propertyMedicinePedagogyComputer science

Abstract

fetched live from OpenAlex

RATIONALE, AIMS, AND OBJECTIVES: Programmatic assessment has been identified as a system-oriented approach to achieving the multiple purposes for assessment within Competency-Based Medical Education (CBME, i.e., formative, summative, and program improvement). While there are well-established principles for designing and evaluating programs of assessment, few studies illustrate and critically interpret, what a system of programmatic assessment looks like in practice. This study aims to use systems thinking and the 'two communities' metaphor to interpret a model of programmatic assessment and to identify challenges and opportunities with operationalization. METHOD: An interpretive case study was used to investigate how programmatic assessment is being operationalized within one competency-based residency program at a Canadian university. Qualitative data were collected from residents, faculty, and program leadership via semi-structured group and individual interviews conducted at nine months post-CBME implementation. Data were analyzed using a combination of data-based inductive analysis and theory-derived deductive analysis. RESULTS: In this model, Academic Advisors had a central role in brokering assessment data between communities responsible for producing and using residents' performance information for decision making (i.e., formative, summative/evaluative, and program improvement). As system intermediaries, Academic Advisors were in a privileged position to see how the parts of the assessment system contributed to the functioning of the whole and could identify which system components were not functioning as intended. Challenges were identified with the documentation of residents' performance information (i.e., system inputs); use of low-stakes formative assessments to inform high-stakes evaluative judgments about the achievement of competence standards; and gaps in feedback mechanisms for closing learning loops. CONCLUSIONS: The findings of this research suggest that program stakeholders can benefit from a systems perspective regarding how their assessment practices contribute to the efficacy of the system as a whole. Academic Advisors are well positioned to support educational development efforts focused on overcoming challenges with operationalizing programmatic assessment.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.039
metaresearch head score (Gemma)0.123
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.881
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0390.123
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.003
Open science0.0000.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0010.000

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 head0.068
GPT teacher head0.545
Teacher spread0.478 · 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