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Record W3080887588 · doi:10.1097/ncq.0000000000000513

Improving Teamwork and Communication in Schools of Nursing

2020· article· en· W3080887588 on OpenAlexaff
Marcia Cooke, Nancy M. Valentine

Bibliographic record

VenueJournal of Nursing Care Quality · 2020
Typearticle
Languageen
FieldHealth Professions
TopicInterprofessional Education and Collaboration
Canadian institutionsCooke Aquaculture (Canada)
Fundersnot available
KeywordsTeamworkIntervention (counseling)Patient safetyNursingQuality managementConflict resolutionMedical educationPsychologyQuality (philosophy)MedicineEngineeringOperations managementHealth careManagement

Abstract

fetched live from OpenAlex

BACKGROUND: Teamwork training has been identified as an intervention to promote collaboration, improve communication, and reduce conflict. While teamwork training has been implemented in the clinical setting, most schools of nursing are lacking in standardized teamwork training programs. LOCAL PROBLEM: A lack of teamwork, poor communication, and deficient conflict resolution skills among faculty and staff was impacting the ability to form supportive relationships in the interest of working collaboratively. METHODS: This quality improvement (QI) project examined perceptions of teamwork at 3 points over 18 months. INTERVENTION: Team Strategies and Tools to Enhance Performance and Patient Safety (TeamSTEPPS) was adapted for the academic setting and used for the intervention. RESULTS: Results indicated significant improvement in Team Structure, Leadership, and Communication. Situation Monitoring and Mutual Support were identified for continued development. CONCLUSION: Results suggest that a QI intervention using TeamSTEPPS improved teamwork, communication, collaboration, and conflict resolution in one academic setting.

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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.215
Threshold uncertainty score0.356

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 head0.124
GPT teacher head0.551
Teacher spread0.427 · 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 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

Citations7
Published2020
Admission routes1
Has abstractyes

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