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Record W2025068750 · doi:10.1097/nna.0b013e31824337f4

Measuring Actual Scope of Nursing Practice

2012· article· en· W2025068750 on OpenAlexafffundabout
Danielle D’Amour, Carl‐Ardy Dubois, Johanne Déry, Sean P. Clarke, Éric Tchouaket Nguemeleu, Régis Blais, Michèle Rivard

Bibliographic record

VenueJONA The Journal of Nursing Administration · 2012
Typearticle
Languageen
FieldNursing
TopicNursing Diagnosis and Documentation
Canadian institutionsUniversité de Montréal
FundersCanadian Institutes of Health Research
KeywordsScope (computer science)Scope of practiceNursingReliability (semiconductor)Face validityNursing practiceMedicineClinical PracticeDimension (graph theory)PsychometricsHealth careComputer science

Abstract

fetched live from OpenAlex

OBJECTIVE: : This project describes the development and testing of the actual scope of nursing practice questionnaire. BACKGROUND: : Underutilization of the skill sets of registered nurses (RNs) is a widespread concern. Cost-effective, safe, and efficient care requires support by management to facilitate the implementation of nursing practice at the full scope. METHODS: : Literature review, expert consultation, and face validity testing were used in item development. The instrument was tested with 285 nurses in 22 medical units in 11 hospitals in Canada. RESULTS: : The 26-item, 6-dimension questionnaire demonstrated validity and reliability. The responses suggest that nurses practice at less than their optimal scope, with key dimensions of professional practice being implemented infrequently. CONCLUSIONS: : This instrument can help nurse leaders increase the effective use of RN time in carrying out the full scope of their professional practice.

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)0.008
metaresearch head score (Gemma)0.033
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.043

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.033
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.000
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 head0.100
GPT teacher head0.400
Teacher spread0.300 · 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

Citations77
Published2012
Admission routes3
Has abstractyes

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