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Record W3045134062 · doi:10.1177/1077558720942706

Nurse Practitioner Role and Practice Environment in Primary and in Nonprimary Care in California

2020· article· en· W3045134062 on OpenAlexaff
Shira G. Winter, Susan A. Chapman, Garrett K. Chan, Karen G. Duderstadt, Joanne Spetz

Bibliographic record

VenueMedical Care Research and Review · 2020
Typearticle
Languageen
FieldHealth Professions
TopicNursing Roles and Practices
Canadian institutionsImpact
Fundersnot available
KeywordsOddsScope of practiceNursingScope (computer science)Nurse practitionersSpecialtyCertificationMedicineMEDLINEPrimary careJob satisfactionDistrict nurseFamily medicineHealth carePsychologyLogistic regressionPolitical science

Abstract

fetched live from OpenAlex

Between 2008 and 2016, there was an increase in nurse practitioners in specialty care. This study explores some differences in role and practice environment between primary care and nonprimary care nurse practitioners in the domains of time spent on activities, barriers to providing care, working to scope of practice, full skill utilization, and satisfaction. This cross-sectional quantitative study, based on data from the 2017 Survey of California Nurse Practitioners and Certified Nurse Midwives, found that nurse practitioners in nonprimary care practices have lower odds of reporting time as a barrier to practice, lower odds of reporting practice to full scope, and higher odds of reporting a hierarchical or supervisory relationship with the physician. Future exploration of these differences may shed light on ways to promote nonprimary care practice environments to foster more effective collaboration and fewer barriers to providing 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 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)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.119
Threshold uncertainty score0.236

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.069
GPT teacher head0.481
Teacher spread0.412 · 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 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

Citations1
Published2020
Admission routes1
Has abstractyes

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