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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 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)0.007
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.035
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.314
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 teacher head, not a consensus.

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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