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Record W4282835060 · doi:10.1111/nhs.12964

Advanced practice role delineation within Hong Kong: A cross‐sectional study

2022· article· en· W4282835060 on OpenAlexaff
Krista Jokiniemi, Sek Ying Chair, Frances Kam Yuet Wong, Denise Bryant‐Lukosius

Bibliographic record

VenueNursing and Health Sciences · 2022
Typearticle
Languageen
FieldHealth Professions
TopicNursing Roles and Practices
Canadian institutionsMcMaster University
Fundersnot available
KeywordsCLARITYNursing practiceNursingAdvanced practice nursingCross-sectional studyMedicineAdvanced Practice NursesClinical PracticeBest practiceNursing researchMedical educationNurse practitionersHealth carePolitical science

Abstract

fetched live from OpenAlex

A career ladder for nurses, including several levels of nursing practice and specific roles for advanced practice nurses, was introduced in Hong Kong around the start of the 21st century. To date no studies have distinguished the practices of advanced practice nurses in Hong Kong. This cross-sectional study, conducted between November 2020 and March 2021, aims to identify and differentiate the practice patterns of advanced practice nurses by utilizing the Advanced Practice Role Delineation tool. A total of 191 responses were obtained. Three roles were identified: nurse consultant, advanced practice nurse, and advanced practice nurse in management. Among the five advanced practice nursing domains, nurses were most frequently involved in Education and in Direct Comprehensive Care activities, while least active in Research and in Publication and Professional Leadership. Identifying activities in various nursing roles helps to differentiate their responsibilities and provides new insights for role utilization and support. Although the role characteristics are shaped by country contexts, research evidence on practice patterns may be used to support international discussion and efforts to promote role clarity and effective role introduction and optimization.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.231
Threshold uncertainty score0.988

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0130.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.126
GPT teacher head0.549
Teacher spread0.424 · 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 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

Citations18
Published2022
Admission routes1
Has abstractyes

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