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Record W4311821351 · doi:10.1002/nop2.1512

The added value of the nurse practitioner: An evolutionary concept analysis

2022· article· en· W4311821351 on OpenAlexaff
Isabelle Savard, Grace Al Hakim, Kelley Kilpatrick

Bibliographic record

VenueNursing Open · 2022
Typearticle
Languageen
FieldHealth Professions
TopicNursing Roles and Practices
Canadian institutionsCentre Intégré Universitaire de Santé et de Services Sociaux du Centre-Sud-de-l'Île-de-MontréalHôpital Maisonneuve-RosemontCentre intégré universitaire de santé et de services sociaux de l'Est-de-l'Île-de-MontréalMcGill UniversityUniversité du Québec en Outaouais
Fundersnot available
KeywordsCINAHLThematic analysisValue (mathematics)Context (archaeology)Added valueMeaning (existential)PsychologyMEDLINENursingPerceptionNursing literatureFormal concept analysisMedicineQualitative researchSociologyComputer scienceAlternative medicinePolitical scienceBusinessSocial science

Abstract

fetched live from OpenAlex

AIM: Nurse practitioners' added value is often mentioned in publications, but there is no consensus on what value is being added, what value is being added to, and in comparison with what can be considered to be an added value. A concept analysis was conducted to clarify the attributes, antecedents and meaning and better understand the Nurse practitioners' added value. DESIGN: Rodgers' evolutionary concept analysis. METHODS: We selected 16 studies from CINAHL, PubMed, Embase and Medline to conduct a thematic analysis, considering the date, location and discipline of publications. RESULTS: Nurse practitioners' added value include: skills and competencies, activities performed, positive outcomes, and positive role perceptions, and antecedents and consequences were also identified. Nurse practitioners' added value is context-dependent and is often understood by comparing it to a context prior to implementation or other professional roles.

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)0.026
metaresearch head score (Gemma)0.041
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.026
Threshold uncertainty score0.139

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0260.041
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0140.012
Science and technology studies0.0020.005
Scholarly communication0.0080.010
Open science0.0010.004
Research integrity0.0010.001
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 head0.053
GPT teacher head0.467
Teacher spread0.413 · 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 designTheoretical or conceptual
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

Citations22
Published2022
Admission routes1
Has abstractyes

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