Responsive BScN Programming at Nipissing University: The Continuing Education of Ontario Nurses
Bibliographic record
Abstract
Nipissing University in North Bay, Ontario, is currently the only post- secondary institution in that province to offer a part-time Baccalaureate of Science in Nursing (BScN) program for Registered Practical Nurses (RPNs) through a blended learning model. This program represents a “bridge” from the nurse’s college diploma and offers a curriculum that enables students to continue to practice nursing as they study. Since the program’s inception in 2010, over 500 students have been admitted, attesting to its need. Flexibility, access, partnership, and excellence in teaching and learning comprise the heart of this complex, innovative, and student-centred program. As a blended learning program, it uses synchronous and asynchronous online technologies to deliver theoretical content; these experiences are balanced with face-to-face learning in the clinical setting. Clinical learning is facilitated through partnership agreements with the students’ employers.This paper describes how this RPN to BScN blended learning program has brought Nipissing to a leading edge in continuing education for RPNs. It also demonstrates Nipissing University’s commitment to drive change in the world of professional and adult education.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 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".