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Record W2168499992 · doi:10.12927/cjnl.2011.22468

Community of Practice: A Nurse Practitioner Collaborative Model

2011· article· en· W2168499992 on OpenAlexafffundvenue
Judith Burgess, Linda Sawchenko

Bibliographic record

VenueNursing leadership · 2011
Typearticle
Languageen
FieldHealth Professions
TopicNursing Roles and Practices
Canadian institutionsUniversity of Victoria
FundersCanadian Institutes of Health Research
KeywordsCompetence (human resources)Health carePublic relationsCollaborative leadershipCollaborative modelCommunity of practiceParticipatory action researchSociologyKnowledge managementNursingPsychologyMedicinePolitical sciencePedagogyComputer science

Abstract

fetched live from OpenAlex

A study was undertaken with nurse practitioners (NPs) in 2008-2009 to examine post-legislation role development in British Columbia. The authors used a participatory action research approach to engage NPs in social investigation, education and action, and to explore, from the participants' perspective, how collaboration advances NP role integration in primary healthcare. A particular discovery of the study was the Interior Health Authority Community of Practice (CoP) established in collaboration with health leaders and NPs. The purpose of this paper is to report on the CoP and the five characteristics describing this collaborative CoP model, including sanctioned social structure, knowledge exchange network, practice discovery and innovation, generating meaning and value, and power sharing for strategic improvement. The CoP helped NPs to build collegial and collaborative relationships, enhance practice learning and competence, extend and apply new knowledge, enrich professional identities, and shape health organizational policy and politics. Because healthcare research about CoPs is limited, principles of a collaborative CoP model are offered for broader healthcare use. The authors conclude that a collaborative CoP model addresses the internal interests and needs of participating members while attending to the external concerns of the organization, and thus contributes to healthcare improvement.

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)0.002
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: none
Teacher disagreement score0.938
Threshold uncertainty score0.899

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.002
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 head0.450
GPT teacher head0.479
Teacher spread0.029 · 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

Citations17
Published2011
Admission routes3
Has abstractyes

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