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Who Is Teaching What, When? An Evolving Online Tool to Manage Dental Curricula

2014· article· en· W2390795062 on OpenAlexaff
Joanne N. Walton

Bibliographic record

VenueJournal of Dental Education · 2014
Typearticle
Languageen
FieldMedicine
TopicRadiology practices and education
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsCurriculumAccreditationDocumentationMedical educationSession (web analytics)Computer sciencePsychologyMedicinePedagogyWorld Wide Web

Abstract

fetched live from OpenAlex

There are numerous issues in the documentation and ongoing development of health professions curricula. It seems that curriculum information falls quickly out of date between accreditation cycles, while students and faculty members struggle in the meantime with the "hidden curriculum" and unintended redundancies and gaps. Beyond knowing what is in the curriculum lies the frustration of timetabling learning in a transparent way while allowing for on-the-fly changes and improvements. The University of British Columbia Faculty of Dentistry set out to develop a curriculum database to answer the simple but challenging question "who is teaching what, when?" That tool, dubbed "OSCAR," has evolved to not only document the dental curriculum, but as a shared instrument that also holds the curricula and scheduling detail of the dental hygiene degree and clinical graduate programs. In addition to providing documentation ranging from reports for accreditation to daily information critical to faculty administrators and staff, OSCAR provides faculty and students with individual timetables and pushes updates via text, email, and calendar changes. It incorporates reminders and session resources for students and can be updated by both faculty members and staff. OSCAR has evolved into an essential tool for tracking, scheduling, and improving the school's curricula.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.629
Threshold uncertainty score0.422

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.002
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.014
GPT teacher head0.358
Teacher spread0.344 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations4
Published2014
Admission routes1
Has abstractyes

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