Competency Assessment Tools for Registered Nurses: An Integrative Review
Bibliographic record
Abstract
BACKGROUND: The clinical nurse educator in practice settings assists registered nurses through education and works with nurse managers to evaluate the continuing competency of registered nurses. The availability of self reporting tools with acceptable psychometric properties may contribute to an understanding of staff expertise and continued competence to perform their required duties. METHODS: An integrative review of the literature was conducted using keyword searches in CINAHL, ERIC, and PsyciNFO. The search for tools published in the past decade focused on self-assessment of continuing competence in practicing nurses. RESULTS: Four research reports were found with multidimensional self-reporting tools designed for use with nurses in ongoing practice. Each tool specifies a unique set of dimensions of continuing competency (e.g., clinical care, leadership, interpersonal relationships) and has had its validity or reliability tested with practicing nurses. CONCLUSION: The results of the review showed an improvement in the development and availability of tools.However, the tools are still lacking in dimension and further investment in this area of research is needed.
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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 | 0.010 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.013 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".