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The communicative power of nurse practitioners in multidisciplinary primary healthcare teams

2012· article· en· W2131390505 on OpenAlexaffabout
Elizabeth Quinlan, Susan Robertson

Bibliographic record

VenueJournal of the American Academy of Nurse Practitioners · 2012
Typearticle
Languageen
FieldHealth Professions
TopicNursing Roles and Practices
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsMultidisciplinary approachContext (archaeology)Health careProfessional boundariesKnowledge managementNursingMedicineKnowledge transferMedical educationPsychologySociologyComputer science

Abstract

fetched live from OpenAlex

PURPOSE: The purpose of the study is to explore the role of the nurse practitioner (NP) in facilitating knowledge exchange within multidisciplinary primary healthcare teams. The rationale for the study is that most knowledge transfer and exchange literature is from a single profession perspective; yet, an increasing number of healthcare practitioners work in the context of multidisciplinary teams. There is little research examining the mechanisms by which knowledge crosses professional and disciplinary boundaries. DATA SOURCE: The study's data source is a survey administered to NPs in urban, rural, and remote primary healthcare teams in Saskatchewan. The mapping techniques of social network analysis are applied to the survey data. CONCLUSIONS: The study's conclusions concern the structure of the intrateam knowledge-exchange behaviors and, in particular, the role of the NP as knowledge boundary spanner. IMPLICATIONS FOR PRACTICE: The study hypothesizes that the hallmark of well-functioning multidisciplinary teams is the effective intrateam knowledge exchange and that Saskatchewan's new NPs bring a "boundary-spanning" capacity to the knowledge exchange of the province's multidisciplinary primary healthcare teams. The study fills this gap in the conceptual and empirical research within the evolving context of the reorganization of primary health care.

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)0.005
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesResearch integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.261
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.003
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 head0.044
GPT teacher head0.454
Teacher spread0.410 · 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.

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

Citations32
Published2012
Admission routes2
Has abstractyes

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