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Record W2041522049 · doi:10.3138/jvme.28.1.16

Veterinary Practice Management: Teaching Needs as Viewed by Consultants and Teachers

2001· article· en· W2041522049 on OpenAlexvenueno aff
James W. Lloyd, Eric R. Larsen

Bibliographic record

VenueJournal of Veterinary Medical Education · 2001
Typearticle
Languageen
FieldHealth Professions
TopicVeterinary Practice and Education Studies
Canadian institutionsnot available
FundersUniversity of Kansas
KeywordsFocus groupDiversity (politics)Medical educationConfidentialityPsychologyProfessional developmentMedicineSociologyPolitical scienceBusinessMarketing

Abstract

fetched live from OpenAlex

A study was conducted to assess veterinary practice management (VPM) educational and research needs from the perspective of consultants and teachers. An online focus group discussion was designed, involving two separate groups (consultants and teachers). One week was allocated to each group, with five to eight questions posed per day. Pseudonyms were used to provide confidentiality. Teachers were selected by inviting the primary VPM course coordinator at each AAVMC school. Consultants required at least two recommendations and were selected in a stratified, non-random manner to achieve both geographic and disciplinary diversity. Participation was stronger within the consultant group: 98 pages of data were generated by the consultants and 35 pages by teachers. Participants agreed that the sub-optimal economic conditions that characterize the veterinary profession are reflective of relatively low-level management skills. This situation establishes the need to strengthen VPM educational and research programs. Many specific suggestions were provided. It was recommended that a cooperative effort between academia and the private sector be employed. However, participants recognized that successful DVM education programs in VPM will be difficult to achieve and sustain without strong support from college administrators and faculties.

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)0.007
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.485
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.007
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)0.0010.000

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 head0.238
GPT teacher head0.546
Teacher spread0.309 · 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 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

Citations17
Published2001
Admission routes1
Has abstractyes

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