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Record W2791990798 · doi:10.3138/jvme.0217-026r

Examining the Validity of the North American Veterinary Licensing Examination (NAVLE) Time Constraints

2018· article· en· W2791990798 on OpenAlexvenueno aff
Richard A. Feinberg, Daniel Jurich, Jennifer Lord, Heather Case, Janine L. Hawley

Bibliographic record

VenueJournal of Veterinary Medical Education · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicDisability Education and Employment
Canadian institutionsnot available
Fundersnot available
KeywordsTest (biology)LicensurePsychologyMedical educationMedicineFamily medicineApplied psychology

Abstract

fetched live from OpenAlex

Individuals who want to become licensed veterinarians in North America must complete several qualifying steps including obtaining a passing score on the North American Veterinary Licensing Examination (NAVLE). Given the high-stakes nature of the NAVLE, it is essential to provide evidence supporting the validity of the reported test scores. One important way to assess validity is to evaluate the degree to which scores are impacted by the allotted testing time which, if inadequate, can hinder examinees from demonstrating their true level of proficiency. We used item response data from the November-December 2014 and April 2015 NAVLE administrations (n =5,292), to conduct timing analyses comparing performance across several examinee subgroups. Our results provide evidence that conditions were sufficient for most examinees, thereby supporting the current time limits. For the relatively few examinees who may have been impacted, results suggest the cause is not a bias with the test but rather the effect of poor pacing behavior combined with knowledge deficits.

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)0.003
metaresearch head score (Gemma)0.004
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 score0.852
Threshold uncertainty score0.932

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.003
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.152
GPT teacher head0.413
Teacher spread0.262 · 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

Citations2
Published2018
Admission routes1
Has abstractyes

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