MétaCan
Menu
Back to cohort
Record W2082563159 · doi:10.3109/13561820.2014.940416

Insight into team competence in medical, nursing and respiratory therapy students

2014· article· en· W2082563159 on OpenAlexaff
Elaine Sigalet, Tyrone Donnon, Vincent Grant

Bibliographic record

VenueJournal of Interprofessional Care · 2014
Typearticle
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsCompetence (human resources)NursingMedicinePsychologyMedical education

Abstract

fetched live from OpenAlex

This study provides information for educators about levels of competence in teams comprised of medical, nursing and respiratory therapy students after receiving a simulation-based team-training (SBT) curriculum with and without an additional formalized 30-min team-training (TT) module. A two-group pre- and post-test research design was used to evaluate team competence with respect to leadership, roles and responsibilities, communication, situation awareness and resource utilization. All scenarios were digitally recorded and evaluated using the KidSIM Team Performance Scale by six experts from medicine, nursing and respiratory therapy. The lowest scores occurred for items that reflected situation awareness. All teams improved their aggregate scores from Time 1 to Time 2 (p < 0.05). Student teams in the intervention group achieved significantly higher performance scores at Time 1 (Cohen's d = 0.92, p < 0.001) and Time 2 (d = 0.61, p < 0.01). All student teams demonstrated significant improvement in their ability to work more effectively by Time 2. The results suggest that situational awareness is an advanced expectation for the undergraduate student team. The provision of a formalized TT module prior to engaging student teams in a simulation-based TT curriculum led to significantly higher performances at Time 1 and 2.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.509
Threshold uncertainty score0.350

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.015
GPT teacher head0.411
Teacher spread0.396 · 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

Citations31
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Interprofessional CareSame topicInnovations in Medical EducationFrench-language works237,207