The Psychometric Properties of Version 2 of the Canadian Nurse Informatics Competency Assessment Scale
Bibliographic record
Abstract
In 2020, we conducted a mixed methods study comprised of a cross-sectional survey in which we applied a modified version of the 21-item Canadian Nurse Informatics Competency Assessment Scale and one-on-one interviews to explore self-perceived nursing informatics competency and readiness for future digital health practice. A total of 221 senior-level students in BScN programs in western Canada participated. This article reports on results related to the factor structure and internal consistency reliability of the 26-item (version 2) of the Canadian Nurse Informatics Competency Assessment Scale. Exploratory principal component analysis with the varimax rotation revealed a four-component structure, explaining 55.10% of the variance. All items on the Canadian Nurse Informatics Competency Assessment Scale 2 had good loadings, except item 7, which did not load to any domain but was retained based on an evaluation of the α value and item relevance to nursing practice. A few items shifted to different domains. The overall reliability of the Canadian Nurse Informatics Competency Assessment Scale 2 was ( α = .916) and its subscales: information and knowledge management ( α = .814), professional and regulatory accountability ( α = .741), and use of information and communication technology ( α = .895). This study provided preliminary evidence for the factor structure and reliability of the Canadian Nurse Informatics Competency Assessment Scale 2 among nursing students. Further testing 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 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".