How Does the Proportion of Child-Specific Content of Pre-Registration Nursing Programmes in Higher Education Institutions Impact Upon Newly Qualified Registered Nurses’ Perceptions of Preparedness to Care for Children, Young People, and Their Families? A Narrative Review Protocol
Bibliographic record
Abstract
This paper presents a narrative review protocol to explore how the proportion of child-specific content of pre-registration programmes in universities impact upon newly qualified nurses' perceptions of preparedness to care for children, young people (CYP), and their families. The preparation and education to become a nurse who cares for children and young people differs from country to country. Providers of pre-registration nurse education offer routes into nursing from diploma to degree and in some countries post-graduate routes. The United Kingdom offers pre-registration programmes leading to qualifying as a children's nurse whereas programmes in countries such as the USA and Canada lead to a professional registration as a registered nurse with postgraduate study to specialize in areas such as pediatrics. The role of pre-registration nursing programmes is to facilitate preparedness for practice. Preparation for practice can include theoretical teaching and practice learning through simulation and face-to-face experience with countries requiring different numbers of practice hours to be completed. Although practice hours are central to nursing education, there is limited evidence on the impact and portion of child-specific content, including clinical learning in preparation of newly qualified nurses to care for CYP and their families. A preliminary search of Prospero, CINAHL, Medline and Cochrane Database indicates that there are no current or in progress reviews identified. The Population of interest, Exposure of interest, and Outcome framework were used to define the research question and inform the eligibility criteria. The review will consider different research designs if related to the research question. The search strategy will conform to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines for systematic reviews. Two independent reviewers will be involved in the screening progress to determine the final studies for inclusion. Eligible studies will be assessed for methodological quality using the Joanna Briggs Institute critical appraisal tools. Extraction of study characteristics and data related to the research question will be extracted into a predefined table. Data synthesis will be conducted using a descriptive analytical approach to summarize extracted data.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".