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Record W2896337643 · doi:10.12927/cjnl.2018.25602

Advancing Family Practice Nursing in Canada: An Environmental Scan of International Literature and National Efforts towards Competency Development

2018· review· en· W2896337643 on OpenAlexaffvenueabout
Julia Lukewich, Samantha Taylor, Marie-Ève Poitras, Ruth Martin‐Misener

Bibliographic record

VenueNursing leadership · 2018
Typereview
Languageen
FieldHealth Professions
TopicFamily and Patient Care in Intensive Care Units
Canadian institutionsDalhousie UniversityUniversité du Québec à ChicoutimiMemorial University of Newfoundland
Fundersnot available
KeywordsCLARITYNursingNursing practiceSoftware deploymentNursing researchMedicineNurse educationPsychology

Abstract

fetched live from OpenAlex

Family practice nurses (also known as primary care nurses) are registered nurses who practice in primary healthcare and function as generalists who provide a broad range of health services, including preventative screening, health education, chronic disease management, care coordination, and system navigation. This paper reports on the current state of family practice nursing in Canada and findings from an environmental scan of literature focused on family practice nursing competency development internationally. Overall, there is a lack of clear information regarding the deployment of family practice nurses in Canada and a lack of clarity about their role in primary healthcare teams. Although family practice nurses play a key role within interprofessional primary healthcare teams, the degree to which family practice nurses have been integrated into primary healthcare varies substantially across provinces/territories. Our environmental scan indicates that the development of family practice nursing competencies is occurring internationally. The steps being taken to develop a defined set of national Canadian family practice nursing competencies are described and implications for policy, administration, leadership, education and patients are discussed.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.944
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.248
GPT teacher head0.419
Teacher spread0.171 · 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.

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

Citations16
Published2018
Admission routes3
Has abstractyes

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