Being and becoming a nurse: Toward an ontological and reflexive turn in first‐year nursing education
Bibliographic record
Abstract
In this paper, we call for an ontological and reflexive turn in first-year nursing education. An ontological turn focuses on formation, the 'being' and 'becoming' of a nurse, and emphasizes the value of nursing knowledge. First-year nursing students often possess romanticized ideals about being a nurse that devalues the knowledge and expertise of nurses. We posit a thoughtful ontological orientation within nursing education that shifts the emphasis toward becoming skillful nurses, with expertise grounded in nursing perspectives. A focus on formation includes discussions regarding ideologies, dominant perspectives, and reflexive explorations of students' views of nursing juxtaposed with the realities of nursing practice. We propose ontologic reflexivity as an approach to consider what perspectives are prioritized (or not) within the nursing classroom. Within pedagogical dialogic spaces, ontologic reflexivity calls on educators to create opportunities for students to learn the value of nursing knowledge along with other forms of knowledge. We consider ways in which an ontological and reflexive turn within the first year of nursing education may contribute to the formation of nursing students who value nursing knowledge, are open-minded to various forms of knowledge, and possess an intentional reflexive way of being.
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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 | 0.022 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.066 |
| Scholarly communication | 0.022 | 0.015 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.004 | 0.013 |
| 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".