Evidence, Virulence, and the Disappearance of Nursing Knowledge: A Critique of the Evidence‐Based Dogma
Bibliographic record
Abstract
CONTEXT: Within the domain of health care, the new discourse of evidence-based practice has appeared and gained momentum, giving rise to a plethora of correlates such as specialized scientific journals and best practice guidelines. Following the crowd, nursing has jumped onto this trend's bandwagon. Although some scholars and clinicians have tried to expand these notions, few have considered a deconstructive approach to do so. APPROACH: Drawing on the works of continental postmodern thinkers such as Baudrillard, Deleuze & Guattari, and Foucault, this paper critically examines the rise of the evidence-based nursing (EBN) movement in order to deconstruct the "taken-for-granted" assumption that EBN is in and of itself the favored path to the sound development of nursing knowledge. DISCUSSION: We argue against the hierarchical differentiation of varied research approaches so as to allow diverse methodologies to guide research and ultimately practice. The status quo is challenged, where research agendas are currently dominated by one paradigm of knowledge development; that of post-positivism in which randomized control trials are portrayed as superior evidence. There is a hazard in excluding many other venues to build nursing knowledge and in oversimplifying the complexity of clinical nursing practice. Furthermore, we argue that this preferred path of knowledge development contradicts nursing academics' efforts to distance itself from the medical model of health care provision and research.
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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 | 0.117 | 0.180 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.011 | 0.004 |
| Science and technology studies | 0.008 | 0.189 |
| Scholarly communication | 0.026 | 0.038 |
| Open science | 0.008 | 0.013 |
| Research integrity | 0.023 | 0.035 |
| Insufficient payload (model declined to judge) | 0.002 | 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".