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Record W2056153700 · doi:10.3138/jvme.28.1.10

Development and Evaluation of A Leadership Program for Veterinary Students

2001· article· en· W2056153700 on OpenAlexvenueno aff
Dale A. Moore, Donald J. Klingborg

Bibliographic record

VenueJournal of Veterinary Medical Education · 2001
Typearticle
Languageen
FieldHealth Professions
TopicVeterinary Practice and Education Studies
Canadian institutionsnot available
Fundersnot available
KeywordsOpenness to experienceNegotiationLeadership stylePsychologyMedical educationLeadershipMedicinePolitical scienceSocial psychology

Abstract

fetched live from OpenAlex

Leadership skills are important for many facets of professional life, but no known leadership training programs exist in North American veterinary schools. It was the purpose of this project to develop, deliver, and evaluate a leadership program for first-year veterinary students. Leadership attributes emphasized in the course included effective communication, openness to learning from others, self-awareness, commitment beyond self-interest, motivation, decision making, understanding issue complexity, and team building. The five-day course was delivered to 21 new veterinary students randomly selected just prior to their first-year orientation in the fall of 2000. Participants ranked themselves higher than non-participants in a post-course evaluation on their ability to be effective leaders. Participants reported an increase in self-confidence and a clearer understanding of their leadership roles. Participants also noted new support systems among co-participants and expressed a new ability to consider complex issues more broadly. Most reported that they frequently used enhanced skills in giving and receiving feedback and team building. Other leadership tools identified as valuable included negotiation, group dynamics, a structured approach to problem solving, time management, and an awareness of personal learning style preferences as a means to improve communication.

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)0.002
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 score0.954
Threshold uncertainty score0.569

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.002
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 integrity0.0000.000
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 head0.779
GPT teacher head0.652
Teacher spread0.127 · 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
Published2001
Admission routes1
Has abstractyes

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