Clinical competence of Iranian nurses: A systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: One of the most important steps in increasing the nurses’ professional competence and consequently improving the quality of nursing care is to evaluate nurses’ clinical competency and then take effective actions to enhance it. This study aimed at exploring the clinical competence of Iranian nurses and factors related to it. MATERIALS AND METHODS: In this systematic review and meta-analysis, PubMed, Scopus, Web of Science, Scientific Information Database, and Iranmedex databases and Google Scholar search engine were searched to February 14, 2020. RESULTS: After screening, a total of 25 articles were included. In general, the level of clinical competence of Iranian nurses was at a desirable level. After meta-analysis of the mean score of nurses’ clinical competence, the combined mean was 161.13 (95% confidence interval [CI]: 137.78–184.48; P < 0.001; I 2 = 99.8%; P value for heterogeneity = P < 0.001) by the Competency Inventory for Registered Nurses (CIRN) questionnaire. The summarized mean of clinical competency measured by the Nurse Competence Scale (NCS) questionnaire was 70.75 (95% CI: 60.80–80.70; P < 0.001; I 2 = 99.9%; P value for heterogeneity = P < 0.001). Factors affecting nurses’ clinical competence were age ≥33 years, nursing work experience ≥9 years, and a master's degree in nursing. However, the clinical competence of nurses had a significant negative relationship with job stress. CONCLUSION: The level of clinical competence of Iranian nurses was desirable. Studies that used the CIRN, reported the highest and lowest clinical competence in clinical care and professional development dimensions, respectively. Studies that used the NCS, reported the highest and lowest clinical competence in dimensions of work role and ensuring quality, respectively.
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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 | 0.015 | 0.032 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.027 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".