Development of the Canadian Nurse Informatics Competency Assessment Scale and Evaluation of Alberta's Registered Nurses' Self-perceived Informatics Competencies
Bibliographic record
Abstract
In today's digitally enabled healthcare environment, it is vitally important to assess Canadian nurses' competency in informatics. The researchers developed the Canadian Nurse Informatics Competency Assessment Scale, a 21-item comprehensive measure based on entry-to-practice informatics competencies for registered nurses, to facilitate assessment of informatics competencies and consequent, planning of formal and continuing education in informatics. The Canadian Nurse Informatics Competency Assessment Scale was used in a cross-sectional survey to determine self-perceived informatics competencies for Alberta's practicing nurses. Results from 2844 completed surveys showed that these nurses perceived their overall informatics competency as slightly above the mark of competent. Perceptions of competency were highest on foundational information and communication technology skills, slightly lower on competencies related to professional regulatory accountability and the use of information and communication technologies in the delivery of patient care, and lowest on information and knowledge management competencies. This study shed some light on priority areas for informatics education among practicing nurses in Alberta. Implications for nursing practice and research are discussed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.011 |
| 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 | 0.001 | 0.000 |
| 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".