Factors Associated With Canadian Nurses' Informatics Competency
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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