Developing nursing students’ informatics competencies – A Canadian faculty perspective
Bibliographic record
Abstract
OBJECTIVES: The aim of this study was to explore nursing faculty experiences in integrating digital tools to support undergraduate students' learning and development of nursing informatics competencies. METHODS: This focused ethnography study used a combination of semi-structured interviews, document reviews, and field visits. Convenience and snowball sampling were applied to recruit participants. Data were analyzed concurrently with data collection, using thematic analysis. RESULTS: Twenty-one faculty members from nine undergraduate nursing programs in Western Canada participated. Themes discussed include: 1) meaning of the term nursing informatics, 2) faculty perceived nursing informatics competence, 3) developing students' nursing informatics competencies, 4) facilitators, and 5) challenges. CONCLUSIONS: Nursing faculty are relatively engaged in developing students' informatics competencies. However, challenges must be addressed and faculty need more support to improve their own informatics capacity. Implications for Practice and Research: This study has implications for faculty, nursing program administrators, and nursing organizations.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.004 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".