Scoping review protocol examining charge nurse skills: requirement for the development of training
Bibliographic record
Abstract
INTRODUCTION: The charge nurse (CN) holds a position in clinical-administrative management and is essential for improving the quality and safety of care in healthcare institutions. The position requires five essential skills: leadership; interpersonal communication; clinical-administrative caring; problem solving; and knowledge and understanding of the work environment. The scientific literature has not widely examined the importance of providing these skills as part of initial training, nor when CNs begin their duties. This study aims to fill this gap through an exhaustive review of the literature with the aim of developing standardised training for the CN when they start in their position. METHODS AND ANALYSIS: A scoping review using the Joanna Briggs Institute framework will be conducted. The CINAHL, MEDLINE, Science Direct and Cairn, databases as well as grey literature from ProQuest dissertations and thesis global database, Google Scholar and the website of the Order of Nurses of Quebec will be queried using keywords. Relevant literature in French and English, published between 2000 and 2022 will be retained. The CN is the target population. Outcomes address at least one of the five CN skills, describe how they are operationalised and what their impact is on the organisation of work and quality of care. This analysis will identify essential and relevant elements for the development of standardised, up-to-date and appropriate training for the position of CN. ETHICS AND DISSEMINATION: Ethical approval is not required, as data does not include individual patient data. The results will be published in peer-reviewed journals, presented at conferences and presented to nursing managers and directors. SCOPING REVIEW REGISTRATION: Research Registry ID: researchregistry7030.
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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 | 0.133 | 0.171 |
| Meta-epidemiology (narrow) | 0.005 | 0.005 |
| Meta-epidemiology (broad) | 0.015 | 0.011 |
| Bibliometrics | 0.022 | 0.021 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.009 | 0.007 |
| Insufficient payload (model declined to judge) | 0.082 | 0.015 |
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".