Caring as the unacknowledged matrix of evidence-based nursing
Bibliographic record
Abstract
In this article, we explicate evidence-based nursing (EBN), critically appraise its framework and respond to nurses' concern that EBN sidelines the caring elements of nursing practice. We use resources from care ethics, especially Vrinda Dalmiya's work that considers care as crucial for both epistemology and ethics, to show how EBN is compatible with, and indeed can be enhanced by, the caring aspects of nursing practice. We demonstrate that caring can act as a bridge between 'external' evidence and the other pillars of the EBN framework: clinical expertise; patient preferences and values. Drawing on an influential EBN handbook, section 1 presents the aims and features of EBN, including the normative principle that EBN should take place within a 'context of caring'. We aim to understand this context and whether it can be neatly detached from the EBN framework, as the handbook seems to suggest. In section 2, we highlight the grounds for resistance to EBN from the nursing community, before mounting the argument that nursing practices can be understood fruitfully through feminist care ethics and/or virtue ethics lenses. In section 3, we deepen that analysis using Dalmiya's concepts of care-knowing and care as a hybrid ethico-epistemic virtue, which are ideally suited to the complex practices of nursing. In section 4, we bring this rich understanding of care into conversation with EBN, showing that its framework cannot be adequately theorised without paying proper attention to care. Caring can be neither an innocuous background assumption of nor an afterthought to the EBN framework.
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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 | 0.102 | 0.094 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.011 | 0.120 |
| Scholarly communication | 0.029 | 0.039 |
| Open science | 0.005 | 0.023 |
| Research integrity | 0.012 | 0.018 |
| Insufficient payload (model declined to judge) | 0.004 | 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".