A Framework for Leveling Informatics Content Across Four Years of a Bachelor of Science in Nursing (BSN) Curriculum
Bibliographic record
Abstract
While there are several published statements of nursing informatics competencies needed for the Bachelor of Science in nursing (BSN) graduate, faculty at schools of nursing has little guidance on how to incorporate the teaching of such competencies into curricula that are already overloaded with required content. The authors present a framework for addressing nursing informatics content within teaching plans that already exist in virtually all BSN programs. The framework is based on an organization of curriculum content that moves the learner from elementary to complex nursing concepts and ideas as a means to level the content. Further, the framework is organized around four broad content areas included in all curricula: professional responsibility, care delivery, community and population-based nursing, and leadership/management. Examples of informatics content to be addressed at each level and content area are provided. Lastly a practice-appraisal tool, the UVIC Informatics Practice Appraisal - BSN is presented as a means to track student learning and outcomes across the four years of a BSN program.
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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 | 0.020 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.005 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".