Factors Associated With Canadian Nurses' Informatics Competency
Bibliographic record
Abstract
As digital innovations continue to transform health systems in Canada, it is important to examine registered nurses' preparedness in informatics, and factors associated with informatics competency. An exploratory, descriptive, cross-sectional survey was used to determine self-perceived informatics competencies, and factors associated with competency, among practicing nurses in Alberta. Results from 2844 completed surveys showed that nurses' self-perceived informatics competency was slightly above the mark of competent. Perceptions of competency were highest on foundational computer literacy skills and lowest on information and knowledge management competencies. However, overall informatics competency mean scores varied significantly in relation to age, educational qualification, years of experience, and work setting. The quality of informatics training and support offered by employers contributed the most to variance in mean scores of total and subdomains of informatics competency. Other factors, such as age, educational qualification, work setting, previous informatics education, access to the Internet, use of health technology, access to supporting resources, informatics training, an informatics role, and continuing education in informatics, also contributed to mean scores variance in differing degrees. Findings from this study provide a basis for actionable policies to address informatics educational needs and support requirements among nurses practicing now and in the future.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".