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Record W7079469756 · doi:10.26108/2x5h-4x28

Nursing primary care back to health: Primary care nurse practitioners and their potentional to improve rural health in Nova Scotia

2009· article· en· W7079469756 on OpenAlexaboutno aff

Bibliographic record

VenueAcadiaU-DEV · 2009
Typearticle
Languageen
FieldComputer Science
TopicGeochemistry and Geologic Mapping
Canadian institutionsnot available
Fundersnot available
KeywordsNova scotiaNurse practitionersPrimary careInterviewPrimary health careDistrict nurseRural healthHealth care

Abstract

fetched live from OpenAlex

At least 4.5 million Canadians do not have a family physician. Nurse practitioners (NPs) can carry out 80% of a general practitioner's duties and may be an answer to the physician deficit in rural Nova Scotia. Qualitative, in-depth interviewing of three nurse practitioners is used in this thesis to explore the roles and experiences of nurse practitioners in the workplace. Grounded theory is used to allow themes to emerge from the data. A feminist standpoint theoretical framework is applied to help evaluate the nurse practitioner experience. Based on my findings, I argue that further implementation of nurse practitioner roles, both in independent and in collaborative practice with physicians, will improve access to health care in Nova Scotia.

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)0.002
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: Empirical
Teacher disagreement score0.182
Threshold uncertainty score0.366

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0060.002
Scholarly communication0.0020.000
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.012
GPT teacher head0.267
Teacher spread0.255 · 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

Citations0
Published2009
Admission routes1
Has abstractyes

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