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Record W2161470045 · doi:10.3109/13561820.2010.539305

The role of nurse practitioners in hospital settings: implications for interprofessional practice

2010· article· en· W2161470045 on OpenAlexaffabout
Mary van Soeren, Christina Hurlock‐Chorostecki, Scott Reeves

Bibliographic record

VenueJournal of Interprofessional Care · 2010
Typearticle
Languageen
FieldHealth Professions
TopicNursing Roles and Practices
Canadian institutionsUniversity of TorontoSt Joseph's Health Care
Fundersnot available
KeywordsNursingHealth careTracking (education)Focus groupHealth professionalsPsychologyMedicineMedical educationSociologyPolitical science

Abstract

fetched live from OpenAlex

Expansion of the nurse practitioner (NP) role worldwide indicates a need to understand how the role functions in interprofessional healthcare teams. Through the adoption of a mixed methods approach that gathered on-site tracking and observation, self-recorded logs of consultations and focus group interviews of team members and NPs, we describe the extent of role activity and the nature of interprofessional practices of 46 NPs and their team members in nine hospital sites across the province of Ontario, Canada. Findings outline the nature of the NP role activities, which largely focused on providing clinical care, with the support of their team, to a range of patients across the study settings. We discuss how 'embedding' the NP in this way appears to contribute to utilization of expertise of all professions as well as enabling team members to promote evidence-based practices. We argue that the use of NPs augments interprofessional role utilization through their desire to consult with a range of professionals and the capacity to perform holistic care for patients that is not limited to traditional nursing boundaries.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.013
metaresearch head score (Gemma)0.029
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.106
Threshold uncertainty score0.210

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.029
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0080.007
Scholarly communication0.0050.005
Open science0.0020.006
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.010
GPT teacher head0.433
Teacher spread0.423 · 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 source (direct Gemma or distilled Codex), 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

Citations58
Published2010
Admission routes2
Has abstractyes

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