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Record W2040706083 · doi:10.3138/jvme.33.3.365

A Model for Teaching Raptor Medicine in the Veterinary Curriculum

2006· article· en· W2040706083 on OpenAlexvenueno aff
Laurel A. Degernes, Julie A. Nettifee

Bibliographic record

VenueJournal of Veterinary Medical Education · 2006
Typearticle
Languageen
FieldImmunology and Microbiology
TopicRabies epidemiology and control
Canadian institutionsnot available
Fundersnot available
KeywordsVenipunctureCurriculumWildlifeMedicineVeterinary medicineMedical educationPsychologyEcologySurgeryBiology

Abstract

fetched live from OpenAlex

Injured or sick wild avian species, especially raptors (birds of prey, including hawks, owls, falcons, and eagles), can present different challenges to veterinary students and veterinarians who are trained in companion avian medicine (e.g., parrot medicine). Proper capture and restraint, feeding, housing, and certain diagnostic and treatment techniques involving raptors require different skills, knowledge, and resources than working with parrots. We developed an innovative raptor medicine program that enables students to acquire proficiency in safe capture, restraint, and examination techniques and in common diagnostic and treatment procedures. A self-assessment survey was developed to determine students' confidence and proficiency in 10 procedures taught in the lab. Groups were compared by class status (Year 1 vs. Year 2 and 3) and level of prior raptor experience (non-experienced or experienced). In surveys conducted before and after teaching two sets of raptor training labs, students rated themselves significantly more proficient in all 10 diagnostic and treatment procedures after completing the two raptor laboratories. The greatest improvements were observed in technical skill procedures such as fluid administration, intramuscular injections, cloacal swabs, venipuncture, and bandaging. Our approach to incorporating elective wildlife learning experiences into the veterinary curriculum may be replicable in other veterinary schools, with or without a wildlife rehabilitation program.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.144
Threshold uncertainty score0.287

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.048
GPT teacher head0.372
Teacher spread0.324 · 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 designNot applicable
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

Citations3
Published2006
Admission routes1
Has abstractyes

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