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Record W2900596435 · doi:10.3138/jvme.0317-046r1

Impact of Team Communication Training on Performance and Self-Assessment of Team Functioning during Sophomore Surgery

2018· article· en· W2900596435 on OpenAlexvenueno aff
Amanda Hanley, April A. Kedrowicz, Sarah Hammond, Elizabeth M. Hardie

Bibliographic record

VenueJournal of Veterinary Medical Education · 2018
Typearticle
Languageen
FieldHealth Professions
TopicVeterinary Practice and Education Studies
Canadian institutionsnot available
Fundersnot available
KeywordsTeamworkCompetence (human resources)Team effectivenessPsychologyMedical educationSurgical teamMedicineKnowledge managementSocial psychology

Abstract

fetched live from OpenAlex

Collaboration and teamwork are important skills for veterinary professionals that affect relationship development, health and well-being, financial success, and clinical outcomes. This study explores the impact of team communication training on performance and assessment of team functioning during second-year surgery by comparing two different classes. The class of 2017 (control group) received no formal training in team communication before their participation in surgery, and the class of 2018 (treatment group) participated in training offered through a dedicated team communication course. Results showed that team training increased surgical preparation times and had a positive impact on perceptions of competence in some teamwork behaviors. Both cohorts identified similar challenges and solutions associated with teamwork, although the team-trained students responded to challenges differently than the control group. Team communication training had a positive impact on students' ability to plan and organize their experiences, navigate team dynamics in the moment, and respond to stress in a positive manner. These findings suggest that team training does, in fact, make a difference in students' abilities to navigate a team task productively.

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)0.002
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.114
Threshold uncertainty score0.657

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
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 integrity0.0000.001
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 head0.282
GPT teacher head0.533
Teacher spread0.251 · 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 designObservational
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

Citations5
Published2018
Admission routes1
Has abstractyes

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