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Record W2527857598 · doi:10.12927/cjnl.2016.24806

An Assessment of How Nurse Practitioners Create Access to Primary Care in Canadian Residential Long-Term Care Settings

2016· article· en· W2527857598 on OpenAlexaffvenueabout
Nancy Carter, Esther Sangster‐Gormley, Jenny Ploeg, Ruth Martin‐Misener, Faith Donald, Abigail Wickson‐Griffiths, Sharon Kaasalainen, Carrie McAiney, Kevin Brazil, Alan Taniguchi, Lori Schindel Martin

Bibliographic record

VenueNursing leadership · 2016
Typearticle
Languageen
FieldHealth Professions
TopicGeriatric Care and Nursing Homes
Canadian institutionsSt. Michael's HospitalSt. Joseph’s Healthcare HamiltonToronto Metropolitan UniversityDalhousie UniversityHamilton Health SciencesUniversity of ReginaUniversity of VictoriaMcMaster University
Fundersnot available
KeywordsNursingPrimary careLong-term careFocus groupHealth careNurse practitionersMedicinePsychologyFamily medicineBusinessPolitical science

Abstract

fetched live from OpenAlex

The aim of this paper is to explore the role and activities of nurse practitioners (NPs) working in long-term care (LTC) to understand concepts of access to primary care for residents. Utilizing the "FIT" framework developed by Penchanksy and Thomas, we used a directed content analysis method to analyze data from a pan-Canadian study of NPs in LTC. Individual and focus group interviews were conducted at four sites in western, central and eastern regions of Canada with 143 participants, including NPs, RNs, regulated and unregulated nursing staff, allied health professionals, physicians, administrators and directors and residents and family members. Participants emphasized how the availability and accessibility of the NP had an impact on access to primary and urgent care for residents. Understanding more about how NPs affect access in Canadian LTC will be valuable for nursing practice and healthcare planning and policy and may assist other countries in planning for the introduction of NPs in LTC settings to increase access to primary 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 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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.191
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.111
GPT teacher head0.442
Teacher spread0.331 · 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 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

Citations7
Published2016
Admission routes3
Has abstractyes

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