MétaCan
Menu
Back to cohort
Record W2168884628 · doi:10.12927/cjnl.2006.18602

Living with Grey: Role Understandings between Clinical Nurse Educators and Advanced Practice Nurses

2006· article· en· W2168884628 on OpenAlexaffvenue
Sarah Wall

Bibliographic record

VenueNursing leadership · 2006
Typearticle
Languageen
FieldSocial Sciences
TopicEducation, Leadership, and Health Research
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsNursingProfessionalizationConfusionNurse educationQualitative researchMedicineValue (mathematics)Nursing researchPsychologySociology

Abstract

fetched live from OpenAlex

Professionalization efforts in nursing have opened up new opportunities for nurses to develop the roles in which they work. One of these roles is advanced nursing practice. However, the development of the advanced roles, with their aims of making an advanced contribution in education, administration, research and practice, results in role overlap and confusion. This paper presents the findings of a qualitative study that explored the ways in which nursing educators understand, value, utilize and interact with nurses in the advanced practice role. Data were collected among nurse educators and advanced nurse practitioners in an urban, acute care setting. The findings demonstrate how nurses in potentially conflicting roles differentiate themselves and define their job duties. Organizational supports for implementing clear advanced roles are suggested, adding to the knowledge upon which nursing administrators can base their strategic human resources decisions.

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)0.022
metaresearch head score (Gemma)0.044
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 score0.022
Threshold uncertainty score0.118

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0220.044
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0160.031
Scholarly communication0.0130.022
Open science0.0020.016
Research integrity0.0040.009
Insufficient payload (model declined to judge)0.0030.000

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 head0.294
GPT teacher head0.492
Teacher spread0.198 · 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

Citations11
Published2006
Admission routes2
Has abstractyes

Explore more

Same venueNursing leadershipSame topicEducation, Leadership, and Health ResearchFrench-language works237,207