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Record W3036920097 · doi:10.12927/cjnl.2020.26238

A Pathway for Implementing the Nurse Practitioner Workforce in a Rural and Remote Health Region

2020· article· en· W3036920097 on OpenAlexaffvenue
Helen Bourque, Kelly Morgan Gunn, Martha MacLeod

Bibliographic record

VenueNursing leadership · 2020
Typearticle
Languageen
FieldHealth Professions
TopicNursing Roles and Practices
Canadian institutionsUniversity of Northern British Columbia
Fundersnot available
KeywordsWorkforceNursingNurse practitionersAdvanced practice nursingRural healthCareer PathwaysPractice nurseWorkforce developmentRural areaMedicineHealth careMedical educationPolitical scienceFamily medicinePrimary care

Abstract

fetched live from OpenAlex

Despite descriptions of nurse practitioner (NP) implementation, there is little guidance for health authorities on how to address the evolving and ongoing challenges of implementing and sustaining NP roles and practice, particularly in rural and remote communities where recruitment and retention are difficult and professional supports may be limited. This article describes a pathway through which NPs have been recruited, supported and retained in their practice in a large rural and remote health authority. The pathway's main steps were the creation of an NP lead for the health authority and the facilitation of conversations with NPs, which resulted in a new organizational model and a renewable action plan for recruitment and retention. The results are a strong NP leadership presence within the health authority, formalized role implementation within the medical staff structure, sustainable mechanisms for professional support, operational management and the strategic development of the NP portfolio. The experience of implementing and integrating the primary care NP workforce in rural and remote settings through an articulated pathway has provided insights into effective NP integration and sustainment in rural primary care settings.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.945
Threshold uncertainty score0.903

Codex and Gemma teacher scores by category

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

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 head0.378
GPT teacher head0.459
Teacher spread0.081 · 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 teacher head, 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

Citations11
Published2020
Admission routes2
Has abstractyes

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