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Record W1982658662 · doi:10.1080/15265161.2013.861881

Examining Methods to Assess Core Knowledge Competencies: A Canadian Perspective

2014· letter· en· W1982658662 on OpenAlexaffabout
Barbara Secker, Cécile M. Bensimon, Cheryl Cline, Dianne Godkin, Ann Heesters, Kevin Reel

Bibliographic record

VenueThe American Journal of Bioethics · 2014
Typeletter
Languageen
FieldPsychology
TopicCompetency Development and Evaluation
Canadian institutionsUniversity Health NetworkQueen's UniversitySouthlake Regional Health CenterTrillium Health CentreUniversity of Toronto
Fundersnot available
KeywordsBioethicsProfessionalizationHealth careFormative assessmentCore competencyMedical educationPsychologyEngineering ethicsSociologyPedagogyMedicinePolitical scienceManagementLawSocial scienceEngineering

Abstract

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We agree with White, Jankowski, and Shelton (2014) that professionalization of health care ethics practice requires serious consideration of a written examination to assess core knowledge competenc...

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)0.233
metaresearch head score (Gemma)0.287
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Evaluation · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.767
Threshold uncertainty score0.945

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.2330.287
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0370.049
Science and technology studies0.0180.017
Scholarly communication0.0210.009
Open science0.0120.011
Research integrity0.0040.007
Insufficient payload (model declined to judge)0.0090.002

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 head0.384
GPT teacher head0.491
Teacher spread0.107 · 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designNot applicable
DomainEvaluation
GenreCommentary

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

Citations1
Published2014
Admission routes2
Has abstractyes

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