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Record W1988120737 · doi:10.1177/1527154413480889

Revisiting Scope of Practice Facilitators and Barriers for Primary Care Nurse Practitioners

2013· article· en· W1988120737 on OpenAlexaff
Lusine Poghosyan, Angela Nannini, Arlene Smaldone, Sean P. Clarke, Nancy C. O’Rourke, Barbara G. Rosato, Bobbie Berkowitz

Bibliographic record

VenuePolicy Politics & Nursing Practice · 2013
Typearticle
Languageen
FieldHealth Professions
TopicNursing Roles and Practices
Canadian institutionsConcordia UniversityMcGill University
FundersNational Institute on Minority Health and Health DisparitiesAmerican Nurses Foundation
KeywordsNonprobability samplingNursingScope (computer science)Qualitative researchContent analysisPrimary careScope of practiceComprehensionWork (physics)Nurse practitionersMedicineHealth careFamily medicinePolitical scienceComputer scienceSociology

Abstract

fetched live from OpenAlex

Revisiting scope of practice (SOP) policies for nurse practitioners (NPs) is necessary in the evolving primary care environment with goals to provide timely access, improve quality, and contain cost. This study utilized qualitative descriptive design to investigate NP roles and responsibilities as primary care providers (PCPs) in Massachusetts and their perceptions about barriers and facilitators to their SOP. Through purposive sampling, 23 NPs were recruited and they participated in group and individual interviews in spring 2011.The interviews were audio recorded and transcribed. Data were analyzed using Atlas.ti 6.0 software, and content analysis was applied. In addition to NP roles and responsibilities, three themes affecting NP SOP were: regulatory environment; comprehension of NP role; and work environment. NPs take on similar responsibilities as physicians to deliver primary care services; however, the regulatory environment and billing practices, lack of comprehension of the NP role, and challenging work environments limit successful NP practice.

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)0.054
metaresearch head score (Gemma)0.112
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 score0.054
Threshold uncertainty score0.286

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0540.112
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0060.006
Scholarly communication0.0050.006
Open science0.0020.006
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.028
GPT teacher head0.447
Teacher spread0.418 · 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

Citations73
Published2013
Admission routes1
Has abstractyes

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