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Record W2006667893 · doi:10.1017/s1463423606000387

Optimizing nursing scope of practice within a primary health care context: linking role accountabilities to health outcomes

2006· article· en· W2006667893 on OpenAlexaffabout
Jeanne Besner

Bibliographic record

VenuePrimary Health Care Research & Development · 2006
Typearticle
Languageen
FieldHealth Professions
TopicInterprofessional Education and Collaboration
Canadian institutionsAlberta Health Services
Fundersnot available
KeywordsNursingHealth careScope (computer science)Context (archaeology)Health policyScope of practiceMedicineService delivery frameworkPublic relationsPsychologyService (business)BusinessPolitical sciencePublic health

Abstract

fetched live from OpenAlex

With the increasing focus on collaborative, interprofessional models of service delivery in many parts of the world, it is crucial that nurses be able to demonstrate confidence in the value they add to the health system. As the largest group of health professionals, nurses must serve as change agents in strengthening health systems and influencing the development of appropriate health policy. This requires that nurses have a solid understanding of what is their unique role in contributing to attainment of desired health outcomes in the populations they serve. In this article, the role of nurses in health care is reviewed, with commentary on some areas in which nurses are currently underutilized in health care delivery, whether employed in community or institutional settings. Data from research conducted in Canada suggest that nurses have a tendency to define themselves by the tasks or activities they perform and seem unable to clearly articulate their special role in health care. Strong leadership and a willingness by nurses to re-orient their practice will be required to ensure that the potential for harnessing the knowledge, experience, capabilities and commitment of nurses in advancing health care reform shifts from rhetoric to reality.

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)0.009
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.387
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0050.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.002
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 head0.048
GPT teacher head0.502
Teacher spread0.454 · 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 designQualitative
Domainnot available
GenreCommentary

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
Published2006
Admission routes2
Has abstractyes

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