New graduate nurses’ clinical competence: A mixed methods systematic review
Bibliographic record
Abstract
AIM: To appraise and synthesize evidence of empirical studies reporting assessment of new graduate nurses' clinical competence in clinical settings. DESIGN: Mixed methods systematic review. DATA SOURCES: The search strategy included keywords relevant to: new graduate nurse; clinical competence; and competence assessment. The searched literature databases included CINAHL, MEDLINE, Embase, PsycINFO and Web of Science. The search was limited to full-text papers in English or French, published between 2010 -September 2019. REVIEW METHODS: Inclusion criteria were: 1) empirical studies; 2) detailed method and complete results sections; 3) competence assessment in clinical settings; and 4) new graduate nurses (≤24 months). Two independent reviewers screened eligible papers, extracted data and used the Mixed Methods Appraisal Tool framework for quality appraisal. Divergences were solved through discussion. RESULTS: About 42 papers were included in this review: quantitative (N = 31), qualitative (N = 7) and mixed methods (N = 4). Findings suggest that new graduate nurses exhibit a good or adequate level of competence. Longitudinal studies show a significant increase in competence from 0-6 months, but findings are inconsistent from 6-12 months. CONCLUSION: There are a multitude of quantitative tools available to measure clinical competence. This suggests a need for a review of their rigor. IMPACT: No recent reviews comprehensively synthesized the findings from new graduate nurses' clinical competence. This review has found that new graduate nurses' competence has been mostly assessed as good, despite the expectation that they should be more competent. Longitudinal studies did not always show a significant increase in competence. These findings can help nurse educators in providing more support to new graduate nurses throughout the transition period or design improved transition programme. This review also identified quantitative tools and qualitative methods that can be used for competence assessment.
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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 | 0.083 | 0.229 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.015 | 0.009 |
| Bibliometrics | 0.025 | 0.018 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.005 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".