The Psychometric Properties of Version 2 of the Canadian Nurse Informatics Competency Assessment Scale
Why this work is in the frame
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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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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it