Measuring Nursing Informatics Competencies of Practicing Nurses in Korea
Bibliographic record
Abstract
Informatics competencies are a necessity for contemporary nurses. However, few researchers have investigated informatics competencies for practicing nurses. A full set of Informatics competencies, an instrument to measure these competencies, and potential influencing factors have yet to be identified for practicing nurses. The Nursing Informatics Competencies Questionnaire was designed, tested for psychometrics, and used to measure beginning and experienced levels of practice. A pilot study using 54 nurses ensured item comprehension and clarity. Internal consistency and face and content validity were established. A cross-sectional survey was then conducted on 230 nurses in Seoul, Korea, to determine construct validity, describe a complete set of informatics competencies, and explore possible influencing factors on existing informatics competencies. Principal components analysis, descriptive statistics, and multiple regression were used for data analysis. Principal components analysis gives support for the Nursing Informatics Competencies Questionnaire construct validity. Survey results indicate that involvement in a managerial position and self-directed informatics-related education may be more influential for improving informatics competencies, whereas general clinical experience and workplace settings are not. This study provides a foundation for understanding how informatics competencies might be integrated throughout nurses' work lives and how to develop appropriate strategies to support nurses in their informatics practice in clinical settings.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".