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Record W3185283782 · doi:10.1080/0142159x.2021.1956681

Ottawa 2020 consensus statements for programmatic assessment – 2. Implementation and practice

2021· article· en· W3185283782 on OpenAlexafffundabout
Dario Torre, Neil Rice, Anna Ryan, Harold G. J. Bok, Luke Dawson, Beth Bierer, Tim Wilkinson, Glendon R. Tait, Tom Laughlin, Kiran Veerapen, Sylvia Heeneman, Adrian Freeman, Cees van der Vleuten

Bibliographic record

VenueMedical Teacher · 2021
Typearticle
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsUniversity of British ColumbiaDalhousie UniversityThe Wilson CentreUniversity of Toronto
FundersFlinders UniversityMcMaster University
KeywordsImplementationThematic analysisCurriculumCulture changeMedical educationEngineering ethicsPsychologyPolitical scienceManagement scienceQualitative researchPedagogySociologyMedicineComputer scienceEngineering

Abstract

fetched live from OpenAlex

INTRODUCTION: Programmatic assessment is a longitudinal, developmental approach that fosters and harnesses the learning function of assessment. Yet the implementation, a critical step to translate theory into practice, can be challenging. As part of the Ottawa 2020 consensus statement on programmatic assessment, we sought to provide descriptions of the implementation of the 12 principles of programmatic assessment and to gain insight into enablers and barriers across different institutions and contexts. METHODS: After the 2020 Ottawa conference, we surveyed 15 Health Profession Education programmes from six different countries about the implementation of the 12 principles of programmatic assessment. Survey responses were analysed using a deductive thematic analysis. RESULTS AND DISCUSSION: A wide range of implementations were reported although the principles remained, for the most part, faithful to the original enunciation and rationale. Enablers included strong leadership support, ongoing faculty development, providing students with clear expectations about assessment, simultaneous curriculum renewal and organisational commitment to change. Most barriers were related to the need for a paradigm shift in the culture of assessment. Descriptions of implementations in relation to the theoretical principles, across multiple educational contexts, coupled with explanations of enablers and barriers, provided new insights and a clearer understanding of the strategic and operational considerations in the implementation of programmatic assessment. Future research is needed to further explore how contextual and cultural factors affect implementation.

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 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)0.002
metaresearch head score (Gemma)0.008
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.679
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.045
GPT teacher head0.510
Teacher spread0.465 · 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 teacher head, not a consensus.

Study designNot applicable
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

Citations52
Published2021
Admission routes3
Has abstractyes

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