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Record W2914647101 · doi:10.1097/nor.0000000000000514

Nurse Practitioners in Orthopaedic Surgical Settings

2019· review· en· W2914647101 on OpenAlexaff
Brittany G. Spence, Joanne Ricci, Fairleth McCuaig

Bibliographic record

VenueOrthopaedic Nursing · 2019
Typereview
Languageen
FieldHealth Professions
TopicNursing Roles and Practices
Canadian institutionsBurnaby HospitalUniversity of British Columbia
Fundersnot available
KeywordsMedicineOrthopedic surgeryMultidisciplinary approachEconomic shortageMultidisciplinary teamOrthopaedic nursingHealth careNurse practitionersNursingSurgical nursingMEDLINEMedical emergencySurgeryPrimary nursingNurse education

Abstract

fetched live from OpenAlex

PURPOSE: The purpose of this article was to conduct an extensive literature review of nurse practitioners (NPs) in orthopaedic surgical settings to delineate whether a need exists for NPs in these settings. BACKGROUND: Due to physician shortages and changes in healthcare, patients are experiencing difficulty accessing orthopaedic surgeons. To meet this need, NPs are becoming an essential part of the multidisciplinary orthopaedic team in Level 1 trauma hospitals. RESULTS: Nurse practitioners are qualified and competent to work in a variety of orthopaedic settings including preoperative clinics, primary care orthopaedic clinics, and pre-/postoperative care within the hospital. The benefits of NPs in orthopaedic surgical settings includes increased access to care, improved team communication, decreased length of stay, improved quality of care, and improved patient satisfaction. Moreover, NPs meet patient needs while surgeons are operating, and have a positive impact on resident surgeon education. CONCLUSION: A need exists for NPs in orthopaedic surgical settings to both improve access to healthcare for patients and reduce the burden on orthopaedic surgeons.

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)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesResearch integrity, Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.952
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0020.006
Insufficient payload (model declined to judge)0.0020.003

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 head0.074
GPT teacher head0.471
Teacher spread0.396 · 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; both teacher heads agree on what is shown here.

Study designOther design
Domainnot available
GenreReview

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

Citations24
Published2019
Admission routes1
Has abstractyes

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