Bibliographic record
Abstract
Recently, patient safety has gained popularity in the nursing literature. While this topic is used extensively and has been analyzed thoroughly, some of the concepts upon which it relies, such as risk, have remained undertheorized. In fact, despite its considerable use, the term 'risk' has been largely assumed to be inherently neutral - meaning that its definition and discovery is seen as objective and impartial, and that risk avoidance is natural and logical. Such an oversight in evaluation requires that the concept of risk be thoroughly analyzed as it relates to nursing practices, particularly in relation to those practices surrounding bio-political nursing care, such as public health, as well as other more trendy nursing topics, such as patient safety. Thus, this paper applies the Evolutionary Model of concept analysis to explore 'risk', and expose it as one mechanism of maintaining prescribed/ proscribed social practices. Thereby, an analysis of risk results in the definitions and roles of the discipline and profession of nursing expanding from solely being dedicated to patient care, to include, in addition, its functions as a governmental body that unwittingly maintains hegemonic infrastructures.
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 | 0.011 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.015 | 0.012 |
| Science and technology studies | 0.002 | 0.018 |
| Scholarly communication | 0.009 | 0.014 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.004 | 0.006 |
| 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".