Recontextualizing Learning in Nursing Education: Taking an Ontological Turn
Bibliographic record
Abstract
An ontological focus has been embedded within nursing education since its inception. There has been a strong emphasis on teaching students to become safe, competent nurses by translating knowledge into clinical action. But how would nursing education shift if we were to more intentionally orient the educative process ontologically and explicitly put epistemology at the service of ontology? We consider this question of an ontological turn in nursing education by examining what is commonly referred to in nursing curricula as interpersonal communication. With the goal of providing learning opportunities that can support students to develop confident and competent practice within the shifting, complex terrain of contemporary health care milieus, we explore the possibility of shifting the relationship between epistemology and ontology, and purposefully orienting the educative process in such a way that emphasizes and illuminates the manner in which nursing knowledge and action intersect with subjectivity and context.
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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 | 0.021 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.009 | 0.135 |
| Scholarly communication | 0.020 | 0.029 |
| Open science | 0.002 | 0.018 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 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".