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Record W2945503502 · doi:10.21815/jde.019.018

Should the U.S. Adopt a National Dental Clinical Licensure Examination? Two Viewpoints

2019· article· en· W2945503502 on OpenAlexaff
Joseph E. Gambacorta, Natalie Jeong, Mary MacDougall, Riki Gottlieb, Jeffery B. Price, Robin Reinke

Bibliographic record

VenueJournal of Dental Education · 2019
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicOccupational and Professional Licensing Regulation
Canadian institutionsUniversity of British Columbia
FundersAmerican Dental Education Association
KeywordsLicensureViewpointsMedical educationMedicinePsychologyArt

Abstract

fetched live from OpenAlex

This Point/Counterpoint article examines the need for and potential impact of implementing a national clinical examination for initial licensure in dentistry. Viewpoint 1 supports a national licensure exam that meets the clinical exam's credentialing requirement for licensure in every state. According to this viewpoint, a national exam will reduce costs, enhance portability of graduates, simplify the transition from dental school to practice or specialty training programs, and standardize requirements for licensure between states. Viewpoint 2 opposes a national licensure exam. This viewpoint supports individual states' dental board decision making process, which is based on identifiable state-specific criteria. The ability to prioritize needs at the state level allows for higher exam standards, easier modifications, more focused requirements, and better calibration in specific exam areas. Viewpoint 2 argues that the delicate balance between licensure agencies and organized dentistry in each state, as well as the involvement of dental schools in the licensure process, must be preserved. This Point/Counterpoint concludes with a joint statement about the prospects for adoption of a national licensure exam.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.073
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.069
GPT teacher head0.367
Teacher spread0.298 · 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; both teacher heads agree on what is shown here.

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

Citations0
Published2019
Admission routes1
Has abstractyes

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