Measurement of clinical nurse performance: Developing a tool including contextual items
Bibliographic record
Abstract
Assessment of nurse performance plays an important role in guaranteeing high quality clinic care to achieve desired patient outcomes. Many tools measuring nurse performance in clinical settings have different dimensions of nurse skills. The objective of this research is to develop and test a new performance assessment tool incorporating applicable task and contextual performance items to measure clinical nurse’s performance. Thirty-eight performance items were derived from previously cited literature and some tools that were in use. A questionnaire containing all items under eight categories was designed to reveal the appropriateness levels of the items. It was distributed to 233 clinical nurses from different hospitals in one city, who were asked to score them on a seven-point scale. The results indicate that although clinical skill and professional skill are the most important categories, the most scored item is “Working systematically” (mean = 6.07, s.d. = 0.89) in contextual category.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".