Psychometric Properties of the Canadian Nurse Informatics Competency Assessment Scale
Bibliographic record
Abstract
Assessment of nursing informatics competencies has gained momentum in the scholarly literature in response to the increased need for resources available to support informatics capacity in nursing. The purpose of this study was to examine the factor structure and internal consistency reliability of the Canadian Nurse Informatics Competency Assessment Scale, a newly developed 21-item measure based on published entry-to-practice informatics competencies for RNs. For this study, 2844 nurses completed the Canadian Nurse Informatics Competency Assessment Scale through a cross-sectional survey. Exploratory principal component analysis with oblique promax rotation revealed a four-component/factor structure for the 21-item Canadian Nurse Informatics Competency Assessment Scale, explaining 61.04% of the variance. Item loading per each component reflected the original Canadian Association of Schools of Nursing grouping of nursing informatics competency indicators, as per three key domains of competency: information and knowledge management (α = .85); professional and regulatory accountability (α = .81); and use of information and communication technology in the delivery of patient care (α = .87) with the exception of one item (Indicator 3), which loaded into the category of foundational information and communication technology skills (α = .81). This study provided preliminary evidence for the construct validity of the entry-to-practice competency domains and the factor structure and reliability of the Canadian Nurse Informatics Competency Assessment Scale among practicing nurses. Further testing among nurses in other settings and among nursing students is recommended.
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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 | 0.008 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".