Characteristics of advanced practice nurses’ clinical competence in primary health care settings: a scoping review protocol
Bibliographic record
Abstract
OBJECTIVE: This scoping review aims to identify the clinical competency characteristics, in terms of knowledge, skills, and attitudes, of advanced practice nurses in primary health care settings. INTRODUCTION: Although much has been written about the competencies of advanced practice nurses, more detail about the skills, knowledge, and attitudes that inform their clinical competence in primary health care needs to be ascertained. This will promote the development, implementation, and evaluation of advanced nursing practice in contexts where it is unavailable. INCLUSION CRITERIA: Studies that address the clinical competency characteristics of advanced practice nurses in primary health care settings will be considered for inclusion. METHODS: This review will be conducted according to JBI methodology for scoping reviews. A comprehensive search will be conducted in PubMed, CINAHL (EBSCOhost), Web of Science Core Collection, Scopus, Virtual Health Library, Scientific Electronic Library Online, Embase, ScienceDirect, Cochrane Library, and Google Scholar for primary and secondary qualitative, quantitative, and mixed methods studies on the topic of relevance. Gray literature will be searched in DART-E, TESES CAPES, CAUL (Australian Digital Theses), and Theses Canada Editorials. Letters, editorials, commentaries, conference abstracts, and documents published by advanced practice nurses' associations will also be considered for inclusion. Two independent reviewers will screen the studies at title and abstract and at full text. The same reviewers will extract relevant data using an instrument developed by the reviewers. These data will be presented in a narrative synthesis to facilitate the analysis of the evidence found. REVIEW REGISTRATION: Open Science Framework https://osf.io/zbqdn.
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 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 | 0.130 | 0.100 |
| Meta-epidemiology (narrow) | 0.005 | 0.006 |
| Meta-epidemiology (broad) | 0.015 | 0.016 |
| Bibliometrics | 0.026 | 0.019 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.007 | 0.008 |
| Research integrity | 0.010 | 0.007 |
| Insufficient payload (model declined to judge) | 0.044 | 0.010 |
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".