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Record W2069265510 · doi:10.5172/conu.2007.26.1.104

Primary health care nurse practitioners in Canada

2007· article· en· W2069265510 on OpenAlexaffabout
Alba DiCenso, Lucille Auffrey, Denise Bryant‐Lukosius, Faith Donald, Ruth Martin‐Misener, Sue Matthews, Joanne Opsteen

Bibliographic record

VenueContemporary Nurse · 2007
Typearticle
Languageen
FieldHealth Professions
TopicNursing Roles and Practices
Canadian institutionsVictorian Order of NursesDalhousie UniversityToronto Metropolitan UniversityRegent Park Community Health CentreCanadian Nurses AssociationMcMaster University
Fundersnot available
KeywordsNursingLegislationContext (archaeology)Work (physics)Primary health careHealth careEconomic shortagePrimary careProject commissioningMedicineHealthcare deliveryNursing shortageNurse practitionersPublic relationsPublishingPolitical scienceFamily medicineNurse educationGovernment (linguistics)Geography

Abstract

fetched live from OpenAlex

Canada, like many countries, is in the midst of primary health care reform. A key priority is to improve access to primary health care, especially in remote communities and areas with physician shortages. As a result, there is an increased emphasis on the integration of primary health care nurse practitioners. As of March 2006, legislation exists in all provinces and two territories in Canada that allows nurse practitioners (NPs) to implement their expanded nursing role. In this paper, we will briefly review the historical development of the NP role in Canada and situate it in the international context; describe the NP role, supply of NPs in the country, and the settings in which they work; propose an NP practice model framework; summarize facilitators and barriers to NP role implementation in primary health care delivery; and outline strategies to address the barriers.

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)0.001
metaresearch head score (Gemma)0.004
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.076
Threshold uncertainty score0.555

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0110.002
Scholarly communication0.0030.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.001

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 head0.038
GPT teacher head0.402
Teacher spread0.364 · 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

Citations71
Published2007
Admission routes2
Has abstractyes

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