A scoping review of clinical skill development of preregistration registered nurses in Australia and five other English‐speaking countries
Bibliographic record
Abstract
AIM: The aim of this scoping review is to synthesise current evidence around the clinical skill development of preregistration registered nurses (RNs) in Australia, United Kingdom (UK), Ireland, United States (US), Canada and New Zealand, to inform nurse education, policy and clinical practice. BACKGROUND: Nursing is a practical profession, and registered nurses require specific skills, knowledge and attributes in order to care for patients safely. The context for health care delivery is shifting, and the education of nurses must adapt to effectively equip the registered nurse of the future. DESIGN: A scoping review was conducted of clinical skill development in preregistration nurses. CINAHL Plus, MEDLINE, Health Source (Nursing/Academic edition) and Scopus were searched. Included studies were primary Australian studies and international literature reviews, which focussed on preregistration nursing education. Papers were written in the English language and focussed on clinical skill development. Results were synthesised narratively. The review is reported here in accordance with the Preferred Reporting Items for Systematic Review and Meta-analyses Scoping Review extension (PRISMA-ScR) guidelines. RESULTS: One hundred fifty-five Australia studies and 89 international reviews were included in the review. Six key themes were identified, namely clinical skills, approaches to teaching and learning, interprofessional education, assessment of learning, clinical placement and simulation. CONCLUSION: There is substantial variation in strategies and programmes to facilitate clinical skill development both within Australia and internationally, indicating a genuine shift away from traditional didactic pedagogy. New graduate registered nurses were expected to be "work-ready," albeit at a novice level, when they enter the workplace. Future research should consider measures of impact on actual clinical practice and focus on developing work-ready graduates for the range of clinical settings in which they may practice. Educators, policymakers and educational institutions can use these findings to inform curriculum developments to ensure that clinical skill development is evidence-based.
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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 | 0.032 | 0.098 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.033 | 0.030 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".