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Record W2156110969 · doi:10.12927/cjnl.2008.19690

Nursing Scope of Practice: Descriptions and Challenges

2008· article· en· W2156110969 on OpenAlexaffvenueabout
Debbie White, Nelly D. Oelke, Jeanne Besner, Diane Doran, Linda M. Hall, Phyllis Giovannetti

Bibliographic record

VenueNursing leadership · 2008
Typearticle
Languageen
FieldHealth Professions
TopicNursing Roles and Practices
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsScope of practiceNursingScope (computer science)WorkforceHealth careWork (physics)FeelingEconomic shortageNurse educationTeam nursingMedicinePsychologyPolitical science

Abstract

fetched live from OpenAlex

The nursing workforce is faced with shortages of near crisis proportions, yet little is understood about the optimal utilization of various categories of nurses - Licensed Practical Nurses (LPNs), Registered Nurses (RNs) and Registered Psychiatric Nurses (RPNs). The primary purpose in this study was to elicit the perceptions of nurses (RNs, LPNs, and RPNs) of what "working to full scope of practice" meant to them. Participants included acute care nurses in three health regions in western Canada. A key finding from the study was the fact that nurses most often discussed scope of practice by reference to the tasks they perform, rather than the roles they play in healthcare delivery. Assessment and coordination of care were two components of nursing work that most differentiated the three nursing roles. Nonetheless, insufficient role differentiation among nurses and between nurses and other healthcare professionals leaves some nurses feeling devalued and not respected for their contribution to healthcare delivery.

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)0.010
metaresearch head score (Gemma)0.026
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.051

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.026
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.003
Science and technology studies0.0040.007
Scholarly communication0.0070.009
Open science0.0020.007
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0010.000

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 head0.584
GPT teacher head0.469
Teacher spread0.116 · 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 designQualitative
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

Citations91
Published2008
Admission routes3
Has abstractyes

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