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Record W3214486851 · doi:10.3138/jvme-2021-0108

Increasing Team Effectiveness through Experiential Team Training: An Explanatory Mixed-Methods Study of First-Year Veterinary Students’ Team Experiences

2021· article· en· W3214486851 on OpenAlexvenueno aff
Sarah Hammond, April A. Kedrowicz

Bibliographic record

VenueJournal of Veterinary Medical Education · 2021
Typearticle
Languageen
FieldHealth Professions
TopicVeterinary Practice and Education Studies
Canadian institutionsnot available
Fundersnot available
KeywordsExperiential learningTeam effectivenessMedical educationPsychologyTask (project management)Intervention (counseling)Psychological safetyTeam compositionMedicineNursingKnowledge managementPedagogyApplied psychologyEngineeringComputer science

Abstract

fetched live from OpenAlex

This article explores the impact of experiential team communication training on student team effectiveness. First-year veterinary students were concurrently enrolled in the Group Communication in Veterinary Medicine course and applied their knowledge to their authentic team experiences in the Veterinary Anatomy and Introduction to Clinical Problem Solving courses. All students completed a modified team effectiveness instrument and a team self-reflection at the end of the semester. Results show that students experienced a high level of team effectiveness. Although students experienced challenges with respect to staying on task and distributing roles and responsibilities, team coordination and communication improved over time, due in part to the team activities associated with the team training intervention. This research provides support for the impact of experiential team training to the development of team process skills and team effectiveness.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.040

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.012
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0030.002
Scholarly communication0.0020.001
Open science0.0020.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.314
GPT teacher head0.587
Teacher spread0.273 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations1
Published2021
Admission routes1
Has abstractyes

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