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Record W2611621378 · doi:10.7759/cureus.1224

Curated Collections for Educators: Five Key Papers about Program Evaluation

2017· review· en· W2611621378 on OpenAlexaff
Brent Thoma, Michael Gottlieb, Megan Boysen‐Osborn, Andrew King, Antonia Quinn, Sara Krzyzaniak, Nicolas Pineda, Lalena M. Yarris, Teresa M. Chan

Bibliographic record

VenueCureus · 2017
Typereview
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsMcMaster UniversityUniversity of Saskatchewan
Fundersnot available
KeywordsMedical educationDelphi methodRelevance (law)Program evaluationEducational programMedicineDelphiCurriculumProcess (computing)Computer sciencePsychologyPedagogy

Abstract

fetched live from OpenAlex

The evaluation of educational programs has become an expected part of medical education. At some point, all medical educators will need to critically evaluate the programs that they deliver. However, the evaluation of educational programs requires a very different skillset than teaching. In this article, we aim to identify and summarize key papers that would be helpful for faculty members interested in exploring program evaluation. In November of 2016, the 2015-2016 Academic life in emergency medicine (ALiEM) Faculty Incubator program highlighted key papers in a discussion of program evaluation. This list of papers was augmented with suggestions by guest experts and by an open call on Twitter. This resulted in a list of 30 papers on program evaluation. Our authorship group then engaged in a process akin to a Delphi study to build consensus on the most important papers about program evaluation for medical education faculty. We present our group's top five most highly rated papers on program evaluation. We also summarize these papers with respect to their relevance to junior medical education faculty members and faculty developers. Program evaluation is challenging. The described papers will be informative for junior faculty members as they aim to design literature-informed evaluations for their educational programs.

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)0.001
metaresearch head score (Gemma)0.011
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.780
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.190
GPT teacher head0.533
Teacher spread0.342 · 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
GenreReview

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

Citations13
Published2017
Admission routes1
Has abstractyes

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