Advanced practice role delineation within Hong Kong: A cross‐sectional study
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".