Nursing Best Practice Guidelines: reflecting on the obscene rise of the void
Bibliographic record
Abstract
AIM(S): Drawing on the work of Jean Baudrillard and Michel Foucault, the purpose of this article is to critique the evidence-based movement [and its derivatives - Nursing Best Practice Guidelines (NBPGs)] in vogue in all spheres of nursing. BACKGROUND: NBPGs and their correlate institutions, such as the Registered Nurses' Association of Ontario (RNAO) and 'spotlight' hospitals, impede critical thinking on the part of nurses, and ultimately evacuate the social, political and ethical responsibilities that ought to distinguish the nursing profession. EVALUATION: We contend that the entire NBPG movement is based on the illusion of scientific truth and a promise of ethical care that cannot be delivered in reality. We took as a case study the Registered Nurses' Association of Ontario (RNAO), in the province of Ontario, Canada. KEY ISSUES: NBPGs, along with the evidence-based movement upon which they are based, are a dangerous technology by which healthcare organizations seek to discipline, govern and regulate nursing work. CONCLUSION(S): Despite the remarkable institutional promotion of 'ready-made' and 'ready-to-use' guidelines, we demonstrate how the RNAO deploys BPGs as part of an ideological agenda that is scientifically, socially, politically and ethically unsound. Implications for nursing management Collaborations between health care organizations and professional organizations can become problematic when the latter dictate nursing conduct in such a way that critical thinking is impeded. We believe that nurse managers need to understand that the evidence-based movement is the target of well-deserved critiques. These critiques should also be considered before implementing so-called 'Nursing Best Practice Guidelines' in health care milieux.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.233 | 0.381 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.015 | 0.108 |
| Scholarly communication | 0.037 | 0.039 |
| Open science | 0.009 | 0.020 |
| Research integrity | 0.038 | 0.062 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".