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Record W1976167171 · doi:10.3138/jvme.38.2.141

The Role of Veterinary Medical Librarians in Teaching Information Literacy

2011· article· en· W1976167171 on OpenAlexvenueno aff
Andrea Dinkelman, Ann Viera, Danelle A. Bickett-Weddle

Bibliographic record

VenueJournal of Veterinary Medical Education · 2011
Typearticle
Languageen
FieldHealth Professions
TopicVeterinary Practice and Education Studies
Canadian institutionsnot available
Fundersnot available
KeywordsInformation literacyCurriculumMedical educationPresentation (obstetrics)Veterinary educationLibrary instructionLiteracyVeterinary medicineMedicinePsychologyPedagogy

Abstract

fetched live from OpenAlex

This qualitative study seeks to determine the nature of the instruction librarians provide to veterinary medical students at all 28 United States veterinary colleges. A secondary goal of the study was to determine in what ways and to what extent librarians participated in other instructional activities at their colleges. Over half of the librarians formally taught in one or more courses, predominantly in the first two years of the veterinary curriculum. One presentation per course was most common. Over half of the librarians interviewed stated that evidence-based veterinary medicine was taught at their colleges, and about half of these librarians collaborated with veterinary faculty in this instruction. Many librarians participated in orientation for first-year veterinary students. The librarians also taught instructional sessions for residents, interns, faculty, graduate students, and practicing veterinarians. This study found that librarians teach information literacy skills both formally and informally, but, in general, instruction by librarians was not well integrated into the curriculum. This study advances several recommendations to help veterinary students develop information literacy skills. These include: encourage veterinary faculty and administrators to collaborate more closely with librarians, incorporate a broader array of information literacy skills into assignments, and add a literature evaluation course to the curriculum.

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)0.005
metaresearch head score (Gemma)0.007
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.845
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.007
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 science0.0010.000
Research integrity0.0000.002
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 head0.254
GPT teacher head0.512
Teacher spread0.258 · 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 designOther design
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

Citations8
Published2011
Admission routes1
Has abstractyes

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