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Record W1972714579 · doi:10.3138/jvme.36.1.30

Livestock Producers’ Views on Accessing Food-Animal Veterinary Services: Implications for Student Recruitment, Training, and Practice Management

2009· article· en· W1972714579 on OpenAlexvenueno aff
Kimberly L. Jensen, Burton C. English, R. Jamey Menard, Robert E. Holland

Bibliographic record

VenueJournal of Veterinary Medical Education · 2009
Typearticle
Languageen
FieldHealth Professions
TopicVeterinary Practice and Education Studies
Canadian institutionsnot available
Fundersnot available
KeywordsLivestockEconomic shortageVeterinary medicineIncentiveBusinessService (business)MarketingAgricultureMedicineAgricultural scienceGeographyGovernment (linguistics)Economics

Abstract

fetched live from OpenAlex

Nationally, shortages of food-animal veterinary practitioners have been projected over the next several years. The purpose of this study was to ascertain livestock producers' perceptions on access to veterinary services and to measure opinions on potential solutions to access problems. Data for the study were from a 2006 survey of livestock producers in Tennessee. The study found that the majority of livestock producers had not perceived problems in obtaining veterinary services during the past year. Among those who had, the problems most commonly cited were a delay in obtaining services; that the veterinarian would treat the animal only if the producer transported it to the veterinary facility; and that the cost of the veterinary service was too high relative to the value of the animal. While it was hypothesized that producers who experienced a problem would have smaller farms on average and would reside in counties with lower numbers of large- or food-animal veterinarians, the results did not support this hypothesis. Among those who perceived a problem, scholarship programs to encourage veterinary students to specialize in large- or food-animal care and greater availability of veterinary technicians to perform health care services were viewed as effective ways to alleviate access problems. Financial incentives for veterinarians to locate in rural areas were also viewed as effective. While shortages have been predicted nationally, data from this survey do not suggest a perceived shortage in Tennessee. Problems in obtaining services appear to be more closely related to practice management and availability of large-animal practitioners in dairy and equine medicine.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.819
Threshold uncertainty score0.844

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
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 head0.719
GPT teacher head0.629
Teacher spread0.091 · 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 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

Citations14
Published2009
Admission routes1
Has abstractyes

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