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Record W2944737796 · doi:10.1177/1043659619846248

Building an International Partnership to Develop Advanced Practice Nurses in Anesthesia Settings: Using a Theory-Driven Approach

2019· article· en· W2944737796 on OpenAlexaff
Jiale Hu, Yan Yang, Michael D. Fallacaro, Brenda Wands, Suzanne Wright, Yiyan Zhou, Hong Ruan

Bibliographic record

VenueJournal of Transcultural Nursing · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicDelphi Technique in Research
Canadian institutionsUniversity of Ottawa
FundersShanghai Municipal Education Commission
KeywordsGeneral partnershipContext (archaeology)NursingProcess (computing)SustainabilityMedicineHealth careWork (physics)Knowledge managementMedical educationPolitical scienceEngineeringComputer science

Abstract

fetched live from OpenAlex

The International Federation of Nurse Anesthetists is calling for international collaboration to develop advanced nursing practice in anesthesia settings globally. However, international collaboration is challenging. Limited information is available about what process and factors specifically lead to a successful international collaboration partnership. This article aimed to describe a theoretical and empirical base that can be used to build and maintain long-term international partnerships. The Theoretical Framework of Developing International Partnerships was developed, which comprises seven interrelated concepts including partnerships, collaborations, environment, structure, process for collaborating, outcomes, and sustainability. It was used to guide an equitable horizontal collaboration partnership to develop anesthesia nursing care in local culture and context. Five major challenges were identified during the collaboration process. Sixty-six strategies were developed to facilitate collaboration using the theoretical framework. This work can inform others in establishing an international collaboration and partnership in advancing nursing knowledge and culturally congruent health care delivery.

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)0.003
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 score0.315
Threshold uncertainty score0.462

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.002
Open science0.0010.000
Research integrity0.0000.000
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 head0.087
GPT teacher head0.479
Teacher spread0.392 · 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

Citations6
Published2019
Admission routes1
Has abstractyes

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